OtherObservables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9e5616c-2a8c-44cb-a55d-8717d938155d",
   "metadata": {},
   "source": [
    "# Utility functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b328b8a3-a93f-4eec-9885-48e4733c8ffd",
   "metadata": {},
   "source": [
    "## Model hooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a0a9d579-ab3c-4bb4-ba61-4389e78e6d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import MethodType\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.rcParams[\"font.family\"] = \"serif\" \n",
    "\n",
    "class AttnHack:\n",
    "    # this is a hack that allows us to get intermediate values used in attention\n",
    "    # what we're doing here is copy-and-pasting the attention code, but adding in some lines that grab\n",
    "    # the values that we want\n",
    "    def __init__(self, module):\n",
    "        self.original_attn_method = module._attn\n",
    "        self.module = module\n",
    "    def attach(self, out_dict):\n",
    "        def my_attn(self, query, key, value, attention_mask=None, head_mask=None):\n",
    "            # Keep the attention weights computation in fp32 to avoid overflow issues\n",
    "            query = query.to(torch.float32)\n",
    "            key = key.to(torch.float32)\n",
    "    \n",
    "            attn_weights = torch.matmul(query, key.transpose(-1, -2))\n",
    "    \n",
    "            query_length, key_length = query.size(-2), key.size(-2)\n",
    "            causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]\n",
    "            mask_value = torch.finfo(attn_weights.dtype).min\n",
    "            # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`.\n",
    "            # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device`\n",
    "            mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device)\n",
    "            attn_weights = torch.where(causal_mask, attn_weights, mask_value)\n",
    "\n",
    "            out_dict['attn_raw_weights'] = attn_weights\n",
    "            out_dict['attn_mask'] = attention_mask\n",
    "            out_dict['head_mask'] = head_mask\n",
    "    \n",
    "            if attention_mask is not None:\n",
    "                # Apply the attention mask\n",
    "                attn_weights = attn_weights + attention_mask\n",
    "        \n",
    "            attn_weights = nn.functional.softmax(attn_weights, dim=-1)\n",
    "            attn_weights = attn_weights.to(value.dtype)\n",
    "            attn_weights = self.attn_dropout(attn_weights)\n",
    "    \n",
    "            # Mask heads if we want to\n",
    "            if head_mask is not None:\n",
    "                attn_weights = attn_weights * head_mask\n",
    "    \n",
    "            attn_output = torch.matmul(attn_weights, value)\n",
    "    \n",
    "            return attn_output, attn_weights\n",
    "        self.module._attn = MethodType(my_attn, self.module)\n",
    "    def remove(self):\n",
    "        self.module._attn = self.original_attn_method\n",
    "\n",
    "def attach_hooks(model):\n",
    "    hooks = []\n",
    "    out_list = {\"layers\": [ {} for l in range(len(model.transformer.h)) ]}\n",
    "    \n",
    "    def make_hook(hook_name, out_dict, is_input=False):\n",
    "        def my_hook(model, input, output):\n",
    "            if is_input:\n",
    "                out_dict[hook_name] = input[0]\n",
    "            else:\n",
    "                out_dict[hook_name] = output\n",
    "        return my_hook\n",
    "        \n",
    "    for l in range(len(model.transformer.h)):\n",
    "        hooks.append(model.transformer.h[l].register_forward_hook(\n",
    "            make_hook(\"block_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].mlp.register_forward_hook(\n",
    "            make_hook(\"mlp_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].mlp.register_forward_hook(\n",
    "            make_hook(\"mlp_in\", out_list['layers'][l], is_input=True)\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].ln_2.register_forward_hook(\n",
    "            make_hook(\"ln_2_in\", out_list['layers'][l], is_input=True)\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].attn.register_forward_hook(\n",
    "            make_hook(\"attn_in\", out_list['layers'][l], is_input=True)\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].attn.register_forward_hook(\n",
    "            make_hook(\"attn_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].attn.attention.q_proj.register_forward_hook(\n",
    "            make_hook(\"q_proj_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].attn.attention.k_proj.register_forward_hook(\n",
    "            make_hook(\"k_proj_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].attn.attention.v_proj.register_forward_hook(\n",
    "            make_hook(\"v_proj_out\", out_list['layers'][l])\n",
    "        ))\n",
    "        hooks.append(model.transformer.h[l].ln_1.register_forward_hook(\n",
    "            make_hook(\"ln_1_in\", out_list['layers'][l], is_input=True)\n",
    "        ))\n",
    "        # now, hook on the attention hacks\n",
    "        hack = AttnHack(model.transformer.h[l].attn.attention)\n",
    "        hack.attach(out_list['layers'][l])\n",
    "        hooks.append(hack)\n",
    "    hooks.append(model.transformer.ln_f.register_forward_hook(\n",
    "        make_hook(\"ln_f_in\", out_list, is_input=True)\n",
    "    ))\n",
    "    hooks.append(model.lm_head.register_forward_hook(\n",
    "        make_hook(\"lm_out\", out_list)\n",
    "    ))\n",
    "        \n",
    "    return out_list, hooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ae7d9d9-49ca-48ec-a1d2-cd60d5d7d957",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_with_patched_activations(model, prompt, patch_dicts, pre_tokenized=False, return_np=False, np_patch=None):\n",
    "    # patch_dicts: a list of dicts\n",
    "    # each patched activation dict has format {'layer', 'sublayer', 'token', 'patch'}\n",
    "    # currently-supported sublayers: ln_1_in, ln_2_in, attn_in, mlp_in\n",
    "\n",
    "    if np_patch is None: np_patch = return_np\n",
    "    # create and attach all hooks\n",
    "    out_list, read_hooks = attach_hooks(model)\n",
    "    write_hooks = []\n",
    "    def make_write_hook(token, patch, my_np_patch):\n",
    "        def my_hook(model, input):\n",
    "            # TODO: do we have to clone input?\n",
    "            if my_np_patch:\n",
    "                input[0][0,token,:] = torch.from_numpy(patch)\n",
    "            else:\n",
    "                input[0][0,token,:] = patch\n",
    "            return input\n",
    "        return my_hook\n",
    "    for patch_dict in patch_dicts:\n",
    "        cur_layer = model.transformer.h[patch_dict['layer']]\n",
    "        if patch_dict['sublayer'] == 'ln_1_in':\n",
    "            cur_module = cur_layer.ln_1\n",
    "        elif patch_dict['sublayer'] == 'attn_in':\n",
    "            cur_module = cur_layer.attn\n",
    "        elif patch_dict['sublayer'] == 'ln_2_in':\n",
    "            cur_module = cur_layer.ln_2\n",
    "        elif patch_dict['sublayer'] == 'mlp_in':\n",
    "            cur_module = cur_layer.mlp\n",
    "        write_hooks.append(cur_module.register_forward_pre_hook(\n",
    "            make_write_hook(patch_dict['token'], patch_dict['patch'], np_patch)\n",
    "        ))\n",
    "\n",
    "    if pre_tokenized:\n",
    "        tokens = {\"input_ids\": prompt['input_ids'].to(\"cuda:0\"), 'attention_mask': prompt['attention_mask'].to(\"cuda:0\")}\n",
    "    else:\n",
    "        tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
    "    model(**tokens)\n",
    "    for hook in read_hooks: hook.remove()\n",
    "    for hook in write_hooks: hook.remove()\n",
    "    if return_np:\n",
    "        out_list = embeddings_torch_to_np(out_list)\n",
    "    return out_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3189e2f1-fecc-42f9-9f96-ce503afb7fb2",
   "metadata": {},
   "source": [
    "## Memory efficiency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "400108c9-7921-44a2-8b9b-bcfa9e28da88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "def get_num_tensors():\n",
    "    tensors_count = 0\n",
    "    for obj in gc.get_objects():\n",
    "        try:\n",
    "            if torch.is_tensor(obj) and obj.device.type == \"cuda\":\n",
    "                tensors_count += 1\n",
    "        except:\n",
    "            pass\n",
    "    return tensors_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e7839933-e29f-48ea-8da9-a234deca12cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_free():\n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()\n",
    "def free_embeddings(embeddings):\n",
    "    l = 0\n",
    "    key = 0\n",
    "    val = 0\n",
    "    while len(embeddings['layers']) > 0:\n",
    "        for key, val in embeddings['layers'][0].items():\n",
    "            embeddings['layers'][0][key] = None\n",
    "        embeddings['layers'][0] = None\n",
    "        del embeddings['layers'][0]\n",
    "        del key\n",
    "        del val\n",
    "    embeddings['layers'] = None\n",
    "    embeddings['ln_f_in'] = None\n",
    "    embeddings = None\n",
    "    do_free()\n",
    "def _convert_embeddings(embeddings, convert_func, free=True):\n",
    "    new_embeddings = {\"layers\": [ {} for l in range(len(model.transformer.h)) ]}\n",
    "    l = 0\n",
    "    x = 0 # dummy\n",
    "    key = 0\n",
    "    val = 0\n",
    "    for l in range(len(embeddings['layers'])):\n",
    "        #print(f\"Layer {l} num tensors before: {get_num_tensors()}\")\n",
    "        for key, val in embeddings['layers'][l].items():\n",
    "            if val is None:\n",
    "                new_embeddings['layers'][l][key] = None\n",
    "            elif type(val) is tuple:\n",
    "                my_l = []\n",
    "                for x in val:\n",
    "                    if type(x) is tuple:\n",
    "                        my_l.append(tuple(convert_func(y) for y in x))\n",
    "                    else:\n",
    "                        my_l.append(convert_func(x))\n",
    "                new_embeddings['layers'][l][key] = tuple( my_l )\n",
    "                while len(my_l) > 0:\n",
    "                    my_l[0] = None\n",
    "                    del my_l[0]\n",
    "                del my_l\n",
    "            else:\n",
    "                new_embeddings['layers'][l][key] = convert_func(val)\n",
    "    new_embeddings['ln_f_in'] = convert_func(embeddings['ln_f_in'])\n",
    "    new_embeddings['lm_out'] = convert_func(embeddings['lm_out'])\n",
    "    if free:\n",
    "        free_embeddings(embeddings)\n",
    "        del embeddings\n",
    "        del x, key, val\n",
    "        do_free()\n",
    "    return new_embeddings\n",
    "def embeddings_torch_to_np(embeddings, free=True):\n",
    "    return _convert_embeddings(embeddings, lambda x: x.detach().cpu().numpy(), free=free)\n",
    "def embeddings_np_to_torch(embeddings, free=True):\n",
    "    return _convert_embeddings(embeddings, lambda x: torch.from_numpy(x).to(\"cuda\"), free=free)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7de67b33-4b89-41e3-8760-303b63c323fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embeddings_torch_to_cpu(embeddings):\n",
    "    new_embeddings = {\"layers\": [ {} for l in range(len(model.transformer.h)) ]}\n",
    "    l = 0\n",
    "    while len(embeddings['layers']) > 0:\n",
    "        for key, val in embeddings['layers'][0].items():\n",
    "            if val is None:\n",
    "                new_embeddings['layers'][l][key] = None\n",
    "            elif type(val) is tuple:\n",
    "                new_embeddings['layers'][l][key] = (x.detach().cpu() for x in val)\n",
    "            else:\n",
    "                new_embeddings['layers'][l][key] = val.detach().cpu()\n",
    "            embeddings['layers'][0][key] = None\n",
    "            #do_free()\n",
    "        embeddings['layers'][0] = None\n",
    "        del embeddings['layers'][0]  \n",
    "        l += 1\n",
    "    embeddings['layers'] = None\n",
    "    new_embeddings['ln_f_in'] = embeddings['ln_f_in'].detach().cpu()\n",
    "    embeddings['ln_f_in'] = None\n",
    "    embeddings = None\n",
    "    do_free()\n",
    "    return new_embeddings\n",
    "\n",
    "def embeddings_torch_to_gpu(embeddings):\n",
    "    new_embeddings = {\"layers\": [ {} for l in range(len(model.transformer.h)) ]}\n",
    "    l = 0\n",
    "    while len(embeddings['layers']) > 0:\n",
    "        for key, val in embeddings['layers'][0].items():\n",
    "            if val is None:\n",
    "                new_embeddings['layers'][l][key] = None\n",
    "            elif type(val) is tuple:\n",
    "                new_embeddings['layers'][l][key] = (x.to(\"cuda\") for x in val)\n",
    "            else:\n",
    "                new_embeddings['layers'][l][key] = val.to(\"cuda\")\n",
    "            embeddings['layers'][0][key] = None\n",
    "            #do_free()\n",
    "        embeddings['layers'][0] = None\n",
    "        del embeddings['layers'][0]\n",
    "        l += 1\n",
    "    embeddings['layers'] = None\n",
    "    new_embeddings['ln_f_in'] = embeddings['ln_f_in'].to(\"cuda\")\n",
    "    embeddings['ln_f_in'] = None\n",
    "    embeddings = None\n",
    "    do_free()\n",
    "    return new_embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138dfd4d-5a8e-4b00-b961-db8fd5892a50",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Range normal setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "27b349a0-5135-43ed-b8ad-2186efd4e125",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 50257\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn\n",
    "\n",
    "def make_lm_head_normal(pos_list, neg_list, normalize=False, take_first_token=False):\n",
    "    n = torch.zeros(vocab_size)\n",
    "    for tok in pos_list:\n",
    "        if not take_first_token:\n",
    "            n += F.one_hot(torch.tensor(tokenizer(tok).input_ids),vocab_size)[0]\n",
    "        else:\n",
    "            n += F.one_hot(torch.tensor(tokenizer(tok).input_ids[0]),vocab_size)\n",
    "    for tok in neg_list:\n",
    "        if not take_first_token:\n",
    "            n -= F.one_hot(torch.tensor(tokenizer(tok).input_ids),vocab_size)[0]\n",
    "        else:\n",
    "            n -= F.one_hot(torch.tensor(tokenizer(tok).input_ids[0]),vocab_size)\n",
    "    if normalize:\n",
    "        n = n/torch.linalg.norm(n)\n",
    "    return n\n",
    "\n",
    "def make_range_normal(pos_list, neg_list, normalize=False):\n",
    "    n = torch.zeros(vocab_size)\n",
    "    for tok in pos_list:\n",
    "        n += F.one_hot(torch.tensor(tokenizer(tok).input_ids),vocab_size)[0]\n",
    "    for tok in neg_list:\n",
    "        n -= F.one_hot(torch.tensor(tokenizer(tok).input_ids),vocab_size)[0]\n",
    "    print(n.shape)\n",
    "    n = n.T.to(\"cuda\", dtype=torch.float32)\n",
    "    n = model.lm_head.weight.data.T @ n\n",
    "    if normalize:\n",
    "        n = n/torch.linalg.norm(n)\n",
    "    return n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c11b32-75e8-41ff-baf3-647ad2944943",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Nonlinear approximation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "822240d5-69c1-4748-bf55-10ca4c17ad0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mdmm\n",
    "# find point on domain boundary closest to our starting point using mdmm constrained optimization library\n",
    "def torch_domain_boundary_proj(x_0, f, range_normal, range_affine, lr=0.01, eps=0.0001):\n",
    "    x_0 = x_0.detach()\n",
    "    x = x_0.detach().clone().to(\"cuda\")\n",
    "    x.requires_grad_(True)\n",
    "    \n",
    "    constraint = mdmm.EqConstraint(lambda: torch.dot(range_normal, f(x)-range_affine),0)\n",
    "    mdmm_module = mdmm.MDMM([constraint])\n",
    "    \n",
    "    optimizer = mdmm_module.make_optimizer([x], lr=lr)\n",
    "    \n",
    "    loss = None\n",
    "    prev_loss = None\n",
    "    while loss is None or abs(loss.item()) > eps:\n",
    "        prev_loss = loss\n",
    "        \n",
    "        loss_intermediate = torch.linalg.norm(x-x_0)**2\n",
    "        mdmm_return = mdmm_module(loss_intermediate)\n",
    "        \n",
    "        loss = mdmm_return.infs[0]**2\n",
    "        #print(prev_loss, loss)\n",
    "        \n",
    "        if prev_loss is not None and abs(prev_loss.item() - loss.item()) < eps**2: break\n",
    "            \n",
    "        optimizer.zero_grad()\n",
    "        mdmm_return.value.backward()\n",
    "        optimizer.step()\n",
    "    print(\"Final loss:\", loss.item())\n",
    "    print(eps)\n",
    "    if prev_loss is not None: print(abs(prev_loss.item() - loss.item()))\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75ab09a0-1244-4784-9917-2df42889ed88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def domain_boundary_intersection(x_pos, x_neg, f, range_normal, range_affine, lr=0.01, eps=0.000001):\n",
    "    g = lambda t_: (range_normal.detach() @ ( f((1-t_)*x_neg.detach() + t_*x_pos.detach()) - range_affine.detach()))**2\n",
    "    \n",
    "    t = torch.Tensor([0.5]).detach().to(\"cuda\")\n",
    "    t.requires_grad_(True)\n",
    "    \n",
    "    optimizer = torch.optim.Adam([t], lr=lr)\n",
    "    \n",
    "    loss = None\n",
    "    prev_loss = None\n",
    "    while loss is None or abs(loss.item()) > eps:\n",
    "        optimizer.zero_grad()\n",
    "        prev_loss = loss\n",
    "        loss = g(t)\n",
    "        if prev_loss is not None and abs(prev_loss.item() - loss.item()) < eps**2: break\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        with torch.no_grad():\n",
    "            t[:] = torch.clamp(t, min=0, max=1)\n",
    "    print(\"Final loss:\", loss.item())\n",
    "    print(\"Final t:\", t)\n",
    "    if prev_loss is not None: print(abs(prev_loss.item() - loss.item()))\n",
    "    return (1-t)*x_neg + t*x_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b00c8cb-272e-49f8-a2a4-f6190c4ab9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tangent_plane_given_point(x_0_new, f, range_normal, range_affine):\n",
    "    # now, find the tangent hyperplane at x_0_new\n",
    "    x_0_new.requires_grad_(True)\n",
    "    g = lambda x: f(x) @ range_normal - (range_normal @ range_affine)\n",
    "    grad = torch.autograd.grad(g(x_0_new), x_0_new)\n",
    "    \n",
    "    return grad[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78ebee1b-6625-4bd5-abaf-1f3279b66926",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_grad(x0, f):\n",
    "    x0.requires_grad_(True)\n",
    "    grad = torch.autograd.grad(f(x0), x0)    \n",
    "    return grad[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d330c7f-88b5-4059-958b-ce5e32912376",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_and_affine(x_0, f, range_normal, range_affine, lr=0.01, eps=0.0001):\n",
    "    affine = torch_domain_boundary_proj(x_0, f, range_normal, range_affine, lr=lr, eps=eps)\n",
    "    normal = get_tangent_plane_given_point(affine, f, range_normal, range_affine)\n",
    "    return normal, affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e0fd00a-2ee0-4093-a0bc-381bd2eafa5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_absolute_loss(f, embeddings_list, layer, sublayer, dom_normal, dom_affine, range_normal, range_affine, token=-1):\n",
    "    avg_diff_sum = 0\n",
    "    avg_diff_count = 0\n",
    "    for np_embeddings in embeddings_list:\n",
    "        embeddings = embeddings_np_to_torch(np_embeddings, free=False)\n",
    "        if layer is None:\n",
    "            my_point = embeddings[sublayer][:, token, :]\n",
    "        else:\n",
    "            my_point = embeddings['layers'][layer][sublayer][:, token, :]\n",
    "        with torch.no_grad():\n",
    "            avg_diff_sum += torch.abs(( (f(my_point) - range_affine) @ range_normal) - ((my_point-dom_affine) @ dom_normal)).item()\n",
    "        avg_diff_count += 1\n",
    "        free_embeddings(embeddings)\n",
    "    return avg_diff_sum/avg_diff_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dfe17a26-ffaa-4403-a4e5-3bb52588fe44",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_attention_scores_for_single_token(embeddings, token):\n",
    "    with torch.no_grad():\n",
    "        all_scores = []\n",
    "        for layer in range(len(embeddings['layers'])):\n",
    "            scores = embeddings['layers'][layer]['attn_out'][2] #(0, head, dst_token, src_token)\n",
    "            scores = scores[0, :, token, token]\n",
    "            all_scores.append(scores)\n",
    "        all_scores = torch.stack(all_scores)\n",
    "    return all_scores #(layer, head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f4fb6c6-1415-4f16-a33a-ca799aefa450",
   "metadata": {},
   "outputs": [],
   "source": [
    "def single_token_ov_circuit(model, embeddings, token, mlp_layers, attn_layers, x, range_normal=None, do_print=False):\n",
    "    mlp_layer_idx = 0\n",
    "    attn_layer_idx = 0\n",
    "\n",
    "    mlp_layers = list(sorted(mlp_layers))\n",
    "    attn_layers = list(sorted(attn_layers))\n",
    "\n",
    "    mlp_layer = mlp_layers[mlp_layer_idx]\n",
    "    attn_layer = attn_layers[attn_layer_idx]\n",
    "\n",
    "    token_attn_scores = get_attention_scores_for_single_token(embeddings, token)\n",
    "    \n",
    "    hidden_states = x\n",
    "    while True:\n",
    "        try:\n",
    "            mlp_layer = mlp_layers[mlp_layer_idx]\n",
    "        except:\n",
    "            mlp_layer = None\n",
    "        \n",
    "        try:\n",
    "            attn_layer = attn_layers[attn_layer_idx]\n",
    "        except:\n",
    "            attn_layer = None\n",
    "\n",
    "        if mlp_layer is None and attn_layer is None:\n",
    "            if range_normal is not None: return hidden_states @ range_normal\n",
    "            return hidden_states\n",
    "        if mlp_layer is None or (attn_layer is not None and attn_layer <= mlp_layer):\n",
    "            if do_print:\n",
    "                print(f\"attn{attn_layer} ====\")\n",
    "                print(f\"\\tattn_scores: {token_attn_scores[attn_layer]}\")\n",
    "            # do attention\n",
    "            residual = hidden_states\n",
    "            hidden_states = model.transformer.h[attn_layer].ln_1(hidden_states)\n",
    "            with torch.no_grad():\n",
    "                attn_module = model.transformer.h[attn_layer].attn.attention\n",
    "                v_proj = attn_module.v_proj.weight\n",
    "                o_proj = attn_module.out_proj.weight\n",
    "                num_heads = attn_module.num_heads\n",
    "                head_dim = attn_module.head_dim\n",
    "\n",
    "                combined_ov_mat = torch.zeros(num_heads*head_dim, num_heads*head_dim).to(\"cuda\")\n",
    "\n",
    "                for head in range(num_heads):\n",
    "                    combined_ov_mat += token_attn_scores[attn_layer, head]\\\n",
    "                        * get_ov_mat_for_head(v_proj, o_proj, num_heads, head_dim, head)\n",
    "            hidden_states = combined_ov_mat @ hidden_states\n",
    "            hidden_states = hidden_states + residual\n",
    "            attn_layer_idx += 1\n",
    "            if do_print: print(f\"\\tafter attn{attn_layer}: {hidden_states @ range_normal}\")\n",
    "        if attn_layer is None or (mlp_layer is not None and mlp_layer < attn_layer):\n",
    "            # do mlp\n",
    "            if do_print: print(f\"mlp{mlp_layer} ====\")\n",
    "            residual = hidden_states\n",
    "            hidden_states = model.transformer.h[mlp_layer].ln_2(hidden_states)\n",
    "            hidden_states = model.transformer.h[mlp_layer].mlp(hidden_states)\n",
    "            hidden_states = hidden_states + residual\n",
    "            mlp_layer_idx += 1\n",
    "            if do_print: print(f\"\\tafter mlp{mlp_layer}: {hidden_states @ range_normal}\")\n",
    "    if range_normal is not None: return hidden_states @ range_normal\n",
    "    return hidden_states    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31838de8-19bb-4ad0-b378-199cfa287afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ov_path(model, embeddings, path, x, range_normal=None, do_print=False):    \n",
    "    hidden_states = x\n",
    "    for i, component in enumerate(path):\n",
    "        layer = component['layer']\n",
    "        sublayer = component['sublayer']\n",
    "        token = component['token']\n",
    "        if sublayer == 'attn':\n",
    "            if i == len(path)-1:\n",
    "                next_token = -1\n",
    "            else:\n",
    "                next_token = path[i+1]['token']\n",
    "            token_attn_scores = embeddings['layers'][layer]['attn_out'][2] #(0, head, dst_token, src_token)\n",
    "            token_attn_scores = token_attn_scores[0, :, next_token, token]\n",
    "            if do_print:\n",
    "                print(f\"attn{layer} ====\")\n",
    "                print(f\"\\tattn_scores: {token_attn_scores}\")\n",
    "            # do attention\n",
    "            residual = hidden_states\n",
    "            hidden_states = model.transformer.h[layer].ln_1(hidden_states)\n",
    "            with torch.no_grad():\n",
    "                attn_module = model.transformer.h[layer].attn.attention\n",
    "                v_proj = attn_module.v_proj.weight\n",
    "                o_proj = attn_module.out_proj.weight\n",
    "                num_heads = attn_module.num_heads\n",
    "                head_dim = attn_module.head_dim\n",
    "\n",
    "                combined_ov_mat = torch.zeros(num_heads*head_dim, num_heads*head_dim).to(\"cuda\")\n",
    "\n",
    "                if type(component['head']) == list:\n",
    "                    for head in component['head']:\n",
    "                        combined_ov_mat += token_attn_scores[head]\\\n",
    "                            * get_ov_mat_for_head(v_proj, o_proj, num_heads, head_dim, head)\n",
    "                elif component['head'] == -1:\n",
    "                    for head in range(num_heads):\n",
    "                        combined_ov_mat += token_attn_scores[head]\\\n",
    "                            * get_ov_mat_for_head(v_proj, o_proj, num_heads, head_dim, head)\n",
    "                else:\n",
    "                    head = component['head']\n",
    "                    combined_ov_mat = token_attn_scores[head]\\\n",
    "                            * get_ov_mat_for_head(v_proj, o_proj, num_heads, head_dim, head)\n",
    "            hidden_states = combined_ov_mat @ hidden_states\n",
    "            hidden_states = hidden_states + residual\n",
    "            if do_print: print(f\"\\tafter attn{layer}: {hidden_states @ range_normal}\")\n",
    "        elif sublayer == 'mlp':\n",
    "            # do mlp\n",
    "            if do_print: print(f\"mlp{layer} ====\")\n",
    "            residual = hidden_states\n",
    "            hidden_states = model.transformer.h[layer].ln_2(hidden_states)\n",
    "            hidden_states = model.transformer.h[layer].mlp(hidden_states)\n",
    "            hidden_states = hidden_states + residual\n",
    "            if do_print: print(f\"\\tafter mlp{layer}: {hidden_states @ range_normal}\")\n",
    "    if range_normal is not None: return hidden_states @ range_normal\n",
    "    return hidden_states    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c1ce95e-405b-4187-aad6-bec70f583c21",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Layer decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "11c87add-899f-45a2-9ab1-c79aede2d9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decompose_layer_mlp(layer, embeddings_pos, embeddings_neg, token_idx, range_normal):\n",
    "    mlp_y_pos = embeddings_pos['layers'][layer]['mlp_out'][0, token_idx, :]\n",
    "    mlp_y_neg = embeddings_neg['layers'][layer]['mlp_out'][0, token_idx, :]\n",
    "    mlp_dot_pos = range_normal @ mlp_y_pos\n",
    "    mlp_dot_neg = range_normal @ mlp_y_neg\n",
    "    \n",
    "    mlp_resid_pos = embeddings_pos['layers'][layer]['ln_2_in'][0, token_idx, :]\n",
    "    mlp_resid_neg = embeddings_neg['layers'][layer]['ln_2_in'][0, token_idx, :]\n",
    "    mlp_resid_dot_pos = range_normal @ mlp_resid_pos\n",
    "    mlp_resid_dot_neg = range_normal @ mlp_resid_neg\n",
    "\n",
    "    return {\n",
    "        \"layer\": f\"{layer}_mlp\",\n",
    "        \"pos\": mlp_dot_pos.item(), \"neg\": mlp_dot_neg.item(), \"resid_pos\": mlp_resid_dot_pos.item(), \"resid_neg\": mlp_resid_dot_neg.item(),\n",
    "        \"score\": mlp_dot_pos.item()-mlp_dot_neg.item(), \"resid_score\": mlp_resid_dot_pos.item()-mlp_resid_dot_neg.item()\n",
    "    }\n",
    "\n",
    "def decompose_layer_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal):\n",
    "    attn_y_pos = embeddings_pos['layers'][layer]['attn_out'][0][0, token_idx, :]\n",
    "    attn_dot_pos = range_normal @ attn_y_pos\n",
    "    attn_resid_pos = embeddings_pos['layers'][layer]['ln_1_in'][0, token_idx, :]\n",
    "    attn_resid_dot_pos = range_normal @ attn_resid_pos\n",
    "\n",
    "    if embeddings_neg is None:\n",
    "        attn_y_neg = torch.zeros(attn_y_pos.shape).to(\"cuda\")\n",
    "        attn_dot_neg = torch.zeros(attn_dot_pos.shape).to(\"cuda\")\n",
    "        attn_resid_neg = torch.zeros(attn_resid_pos.shape).to(\"cuda\")\n",
    "        attn_resid_dot_neg = torch.zeros(attn_resid_dot_pos.shape).to(\"cuda\")\n",
    "    else:\n",
    "        attn_y_neg = embeddings_neg['layers'][layer]['attn_out'][0][0, token_idx, :]\n",
    "        attn_dot_neg = range_normal @ attn_y_neg\n",
    "        attn_resid_neg = embeddings_neg['layers'][layer]['ln_1_in'][0, token_idx, :]\n",
    "        attn_resid_dot_neg = range_normal @ attn_resid_neg\n",
    "\n",
    "    return {\n",
    "        \"layer\": f\"{layer}_attn\",\n",
    "        \"pos\": attn_dot_pos.item(), \"neg\": attn_dot_neg.item(), \"resid_pos\": attn_resid_dot_pos.item(), \"resid_neg\": attn_resid_dot_neg.item(),\n",
    "        \"score\": attn_dot_pos.item()-attn_dot_neg.item(), \"resid_score\": attn_resid_dot_pos.item()-attn_resid_dot_neg.item()\n",
    "    }\n",
    "\n",
    "def decompose_embedding(embeddings_pos, embeddings_neg, token, range_normal):\n",
    "    embedding_contrib_pos = embeddings_pos['layers'][0]['ln_1_in'][0,token,:]@range_normal\n",
    "    if embeddings_neg is None:\n",
    "        embedding_contrib_neg = torch.zeros(1).to(\"cuda\")\n",
    "    else:\n",
    "        embedding_contrib_neg = embeddings_neg['layers'][0]['ln_1_in'][0,token,:]@range_normal\n",
    "    return {\n",
    "        \"layer\": f\"embedding\",\n",
    "        \"pos\": embedding_contrib_pos.item(), \"neg\": embedding_contrib_neg.item(), \n",
    "        \"score\": embedding_contrib_pos.item()-embedding_contrib_neg.item()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1b50abb7-b537-410e-b4da-a4e14193029c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model, attn_layer, is_embedding=False, is_mlp=False, use_ln_2=False):\n",
    "    if is_embedding:\n",
    "        y_pos = embeddings_pos['layers'][0]['ln_1_in'][0,token_idx,:]\n",
    "    else:\n",
    "        y_pos = embeddings_pos['layers'][layer][f'{\"mlp\" if is_mlp else \"attn\"}_out']\n",
    "        if not is_mlp: y_pos = y_pos[0]\n",
    "        y_pos = y_pos[0, token_idx, :]\n",
    "    if embeddings_neg is None:\n",
    "        y_neg = torch.zeros(y_pos.shape).to(\"cuda\")\n",
    "    else:\n",
    "        if is_embedding:\n",
    "            y_neg = embeddings_neg['layers'][0]['ln_1_in'][0,token_idx,:]\n",
    "        else:\n",
    "            y_neg = embeddings_neg['layers'][layer][f'{\"mlp\" if is_mlp else \"attn\"}_out']\n",
    "            if not is_mlp: y_neg = y_neg[0]\n",
    "            y_neg = y_neg[0, token_idx, :]\n",
    "\n",
    "    if use_ln_2:\n",
    "        pre_attn_resid_pos = embeddings_pos['layers'][attn_layer]['ln_2_in'][0, token_idx, :]\n",
    "    else:\n",
    "        pre_attn_resid_pos = embeddings_pos['layers'][attn_layer]['ln_1_in'][0, token_idx, :]\n",
    "    pre_attn_resid_neg = pre_attn_resid_pos - y_pos + y_neg\n",
    "\n",
    "    if use_ln_2: ln = model.transformer.h[attn_layer].ln_2\n",
    "    else: ln = model.transformer.h[attn_layer].ln_1\n",
    "    \n",
    "    dot_pos = range_normal @ ln(pre_attn_resid_pos)\n",
    "    dot_neg = range_normal @ ln(pre_attn_resid_neg)\n",
    "\n",
    "    return {\n",
    "        \"layer\": f'{layer}_{\"mlp\" if is_mlp else \"attn\"}' if not is_embedding else 'embedding',\n",
    "        \"pos\": dot_pos.item(), \"neg\": dot_neg.item(), \"score\": dot_pos.item()-dot_neg.item()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ab26b1b7-a8e4-4b8a-9c77-5523afd852a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decompose_layers(embeddings_pos, embeddings_neg, token_idx, range_normal, all_tokens=False):\n",
    "    retval = []\n",
    "    num_tokens = embeddings_pos['layers'][-1]['ln_1_in'].shape[1]\n",
    "    for layer in range(len(embeddings_pos['layers'])):\n",
    "        if not all_tokens:\n",
    "            retval.append(decompose_layer_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal))\n",
    "            retval.append(decompose_layer_mlp(layer, embeddings_pos, embeddings_neg, token_idx, range_normal))\n",
    "        else:\n",
    "            attn_dict = {\n",
    "                \"layer\": f\"{layer}_attn\",\n",
    "                \"pos\": 0, \"neg\": 0, \"resid_pos\": 0, \"resid_neg\": 0,\n",
    "                \"score\": 0, \"resid_score\": 0\n",
    "            }\n",
    "            for token in range(num_tokens):\n",
    "                cur_attn_dict = decompose_layer_attn(layer, embeddings_pos, embeddings_neg, token, range_normal)\n",
    "                attn_dict['pos'] += cur_attn_dict['pos']\n",
    "                attn_dict['neg'] += cur_attn_dict['neg']\n",
    "                attn_dict['resid_pos'] += cur_attn_dict['resid_pos']\n",
    "                attn_dict['resid_neg'] += cur_attn_dict['resid_neg']\n",
    "            attn_dict['score'] = attn_dict['pos'] - attn_dict['neg']\n",
    "            attn_dict['resid_score'] = attn_dict['resid_pos'] - attn_dict['resid_neg']\n",
    "            retval.append(attn_dict)\n",
    "\n",
    "            mlp_dict = {\n",
    "                \"layer\": f\"{layer}_mlp\",\n",
    "                \"pos\": 0, \"neg\": 0, \"resid_pos\": 0, \"resid_neg\": 0,\n",
    "                \"score\": 0, \"resid_score\": 0\n",
    "            }\n",
    "            for token in range(num_tokens):\n",
    "                cur_mlp_dict = decompose_layer_mlp(layer, embeddings_pos, embeddings_neg, token, range_normal)\n",
    "                mlp_dict['pos'] += cur_mlp_dict['pos']\n",
    "                mlp_dict['neg'] += cur_mlp_dict['neg']\n",
    "                mlp_dict['resid_pos'] += cur_mlp_dict['resid_pos']\n",
    "                mlp_dict['resid_neg'] += cur_mlp_dict['resid_neg']\n",
    "            mlp_dict['score'] = mlp_dict['pos'] - mlp_dict['neg']\n",
    "            mlp_dict['resid_score'] = mlp_dict['resid_pos'] - mlp_dict['resid_neg']\n",
    "            retval.append(mlp_dict)\n",
    "    if not all_tokens:\n",
    "        retval.append(decompose_embedding(embeddings_pos, embeddings_neg, token_idx, range_normal))\n",
    "    else:\n",
    "        embedding_dict = {\n",
    "            \"layer\": \"embedding\",\n",
    "            \"pos\": 0, \"neg\": 0, \"score\": 0\n",
    "        }\n",
    "        for token in range(num_tokens):\n",
    "            cur_embedding_dict = decompose_layer_embedding(embeddings_pos, embeddings_neg, token, range_normal)\n",
    "            embedding_dict['pos'] += cur_embedding_dict['pos']\n",
    "            embedding_dict['neg'] += cur_embedding_dict['neg']\n",
    "        embedding_dict['score'] = embedding_dict['pos'] - embedding_dict['neg']\n",
    "        retval.append(embedding_dict)\n",
    "    return retval\n",
    "    \n",
    "def decompose_layers_wrt_attn(embeddings_pos, embeddings_neg, token_idx, range_normal, model, attn_layer, all_tokens=False, use_ln_2=False):\n",
    "    retval = []\n",
    "    num_tokens = embeddings_pos['layers'][-1]['ln_1_in'].shape[1]\n",
    "    for layer in range(attn_layer+1):\n",
    "        if not all_tokens:\n",
    "            if layer != attn_layer or use_ln_2:\n",
    "                retval.append(decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_mlp=False, use_ln_2=use_ln_2))\n",
    "            if layer != attn_layer: \n",
    "                retval.append(decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_mlp=True, use_ln_2=use_ln_2))\n",
    "        else:\n",
    "            if layer != attn_layer or use_ln_2:\n",
    "                attn_dict = {\n",
    "                    \"layer\": f\"{layer}_attn\",\n",
    "                    \"pos\": 0, \"neg\": 0,\n",
    "                    \"score\": 0\n",
    "                }\n",
    "                for token in range(num_tokens):\n",
    "                    cur_attn_dict = decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_mlp=False, use_ln_2=use_ln_2)\n",
    "                    attn_dict['pos'] += cur_attn_dict['pos']\n",
    "                    attn_dict['neg'] += cur_attn_dict['neg']\n",
    "                attn_dict['score'] = attn_dict['pos'] - attn_dict['neg']\n",
    "                retval.append(attn_dict)\n",
    "            \n",
    "            if layer != attn_layer:\n",
    "                mlp_dict = {\n",
    "                    \"layer\": f\"{layer}_mlp\",\n",
    "                    \"pos\": 0, \"neg\": 0,\n",
    "                    \"score\": 0\n",
    "                }\n",
    "                for token in range(num_tokens):\n",
    "                    cur_mlp_dict = decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_mlp=True, use_ln_2=use_ln_2)\n",
    "                    mlp_dict['pos'] += cur_mlp_dict['pos']\n",
    "                    mlp_dict['neg'] += cur_mlp_dict['neg']\n",
    "                mlp_dict['score'] = mlp_dict['pos'] - mlp_dict['neg']\n",
    "                retval.append(mlp_dict)\n",
    "    if not all_tokens:\n",
    "        retval.append(decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_embedding=True, use_ln_2=use_ln_2))\n",
    "    else:\n",
    "        embedding_dict = {\n",
    "            \"layer\": \"embedding\",\n",
    "            \"pos\": 0, \"neg\": 0, \"score\": 0\n",
    "        }\n",
    "        for token in range(num_tokens):\n",
    "            cur_embedding_dict = decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model=model, attn_layer=attn_layer, is_embedding=True, use_ln_2=use_ln_2)\n",
    "            embedding_dict['pos'] += cur_embedding_dict['pos']\n",
    "            embedding_dict['neg'] += cur_embedding_dict['neg']\n",
    "        embedding_dict['score'] = embedding_dict['pos'] - embedding_dict['neg']\n",
    "        retval.append(embedding_dict)\n",
    "    return retval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "66370818-06af-4c08-8225-dd8244f2b9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decompose_layers_wrt_attn_token_set(embeddings_pos, embeddings_neg, tokens, range_normal, model, attn_layer, use_ln_2=False):\n",
    "    num_tokens = embeddings_pos['layers'][-1]['ln_1_in'].shape[1]\n",
    "    if tokens is None:\n",
    "        tokens = list(range(num_tokens))\n",
    "    total_list = []\n",
    "    for token in tokens:\n",
    "        cur_list = decompose_layers_wrt_attn(\n",
    "            embeddings_pos, embeddings_neg, token, range_normal, model, attn_layer, use_ln_2=use_ln_2, all_tokens=False)\n",
    "        for i in range(len(cur_list)):\n",
    "            cur_list[i]['dst_token'] = token\n",
    "        total_list += cur_list\n",
    "    return total_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4cecc0af-9b46-4975-8b32-7ac494522d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_decomposed_layers(decomposed): return sorted(decomposed, key=lambda x: x['score'], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87874abe-2a53-4681-a5a6-6786fe2cbc28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def plot_decomposed_layers(decomposed):\n",
    "    fill_xs = (np.sort(list(range(len(decomposed)))*2)-0.5)/2\n",
    "    fill_where = (np.arange(len(decomposed)*2)-1)%4<2\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(np.arange(len(decomposed))/2, [x['score'] for x in decomposed], label=\"Sublayer score\")\n",
    "    try:\n",
    "        ax.plot(np.arange(len(decomposed))/2, [x['resid_score'] for x in decomposed], label=\"Residual score\")\n",
    "    except KeyError: pass\n",
    "    ax.fill_between(fill_xs, ax.get_ylim()[0], ax.get_ylim()[1], where=fill_where, alpha=0.2, color='red', label=\"Attention sublayer\")\n",
    "    old_xlim = ax.get_xlim()\n",
    "    ax.plot(old_xlim, [0,0], color='black')\n",
    "    ax.set_xlim(old_xlim)\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "939933ed-d6f8-44b6-9990-684ceed95182",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Interpreting through attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca6da0ae-2d25-49ee-8f2c-502638f7348d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_mat_heads(tensor, num_heads, attn_head_size):\n",
    "    # code stolen (with slight modification) from Huggingface GPT-Neo-1.3b repo\n",
    "    new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)\n",
    "    tensor = tensor.view(new_shape)\n",
    "    return tensor.permute(1,0,2)  # (head, seq_length, head_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9dbb7812-ab41-40a5-bd8c-eb2c1690c841",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dots_per_head_and_token(i, attn_module, scores, split_weights, split_vals, grad):\n",
    "    dots = torch.zeros((attn_module.num_heads, scores.shape[-1])).to(\"cuda\", dtype=torch.float32)\n",
    "    # couldn't think of an easy way to vectorize this\n",
    "    for head in range(attn_module.num_heads):\n",
    "        for j in range(scores.shape[-1]):\n",
    "            dots[head, j] = torch.dot(grad, split_weights[head] @ split_vals[0,head,j,:])\n",
    "    return dots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7e552b9d-438d-4a7f-b4a1-a926493cb9a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_v_proj_for_head(v_proj, head_dim, head):\n",
    "    return v_proj[head*head_dim:(head+1)*head_dim]\n",
    "def get_ov_mat_for_head(v_proj, o_proj, num_heads, head_dim, head):\n",
    "    o_proj_head = split_mat_heads(o_proj, num_heads, head_dim)[head]\n",
    "    v_proj_head = v_proj[head*head_dim:(head+1)*head_dim]\n",
    "    return o_proj_head @ v_proj_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee6834fa-2d79-49f8-8c25-a02177bce327",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_contribs(model, embeddings, layer, token_idx, range_normal):\n",
    "    attn_module = model.transformer.h[layer].attn.attention\n",
    "    attn_split_weights = split_mat_heads(attn_module.out_proj.weight, attn_module.num_heads, attn_module.head_dim)\n",
    "    attn_split_vals = attn_module._split_heads(embeddings['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "    attn_scores = embeddings['layers'][layer]['attn_out'][2]\n",
    "    attn_dots = get_dots_per_head_and_token(token_idx, attn_module, attn_scores, attn_split_weights, attn_split_vals, range_normal)\n",
    "    attn_contribs = attn_dots * attn_scores[0, :, token_idx, :]\n",
    "    attn_contribs_list = []\n",
    "    for head, row in enumerate(attn_contribs):\n",
    "        for j, item in enumerate(row):\n",
    "            attn_contribs_list.append((item.item(), attn_dots[head,j].item(), attn_scores[0,head,token_idx,j].item(), head, j))\n",
    "    attn_contribs_list = sorted(attn_contribs_list, key=lambda x: abs(x[0]), reverse=True)\n",
    "    return attn_contribs, attn_contribs_list\n",
    "\n",
    "def get_contribs_per_head_token_naive(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal, range_layer=None, use_ln_2=False):\n",
    "    attn_module = model.transformer.h[layer].attn.attention\n",
    "    attn_split_weights = split_mat_heads(attn_module.out_proj.weight, attn_module.num_heads, attn_module.head_dim)\n",
    "\n",
    "    c = 1\n",
    "    if not (range_layer is None): \n",
    "        c = get_LN_constant(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal, range_layer, use_ln_2=use_ln_2)\n",
    "    \n",
    "    attn_split_vals_pos = attn_module._split_heads(embeddings_pos['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "    attn_scores_pos = embeddings_pos['layers'][layer]['attn_out'][2]\n",
    "    attn_dots_pos = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_pos, attn_split_weights, attn_split_vals_pos, range_normal)*c\n",
    "    attn_contribs_pos = attn_dots_pos * attn_scores_pos[0, :, token_idx, :]\n",
    "\n",
    "    if embeddings_neg is None:\n",
    "        # zero ablation\n",
    "        attn_split_vals_neg = torch.zeros(attn_split_vals_pos.shape).to(\"cuda\")\n",
    "        attn_scores_neg =  torch.zeros(attn_scores_pos.shape).to(\"cuda\")\n",
    "        attn_dots_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "        attn_contribs_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "    else:\n",
    "        attn_split_vals_neg = attn_module._split_heads(embeddings_neg['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "        attn_scores_neg = embeddings_neg['layers'][layer]['attn_out'][2]\n",
    "        attn_dots_neg = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_neg, attn_split_weights, attn_split_vals_neg, range_normal)*c\n",
    "        attn_contribs_neg = attn_dots_neg * attn_scores_neg[0, :, token_idx, :]\n",
    "\n",
    "    attn_contribs = attn_contribs_pos - attn_contribs_neg\n",
    "    attn_contribs_list = []\n",
    "    for head, row in enumerate(attn_contribs):\n",
    "        for j, item in enumerate(row):\n",
    "            attn_contribs_list.append({\n",
    "                \"head\": head, \"token\": j, \"total_contrib\": item.item(), \"layer\": layer,\n",
    "                \"pos_contrib\": attn_contribs_pos[head,j].item(), \"pos_score\": attn_scores_pos[0,head,token_idx,j].item(), \"pos_dot\": attn_dots_pos[head,j].item(), \n",
    "                \"neg_contrib\": attn_contribs_neg[head,j].item(), \"neg_score\": attn_scores_neg[0,head,token_idx,j].item(), \"neg_dot\": attn_dots_neg[head,j].item()\n",
    "            })\n",
    "    attn_contribs_list = sorted(attn_contribs_list, key=lambda x: abs(x['total_contrib']), reverse=True)\n",
    "    return attn_contribs, attn_contribs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16aa69b2-4b11-43b0-87da-baa947787fab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_LN_constant(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal, range_layer, use_ln_2=False):\n",
    "    good_score = decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal, model, range_layer, is_mlp=False, use_ln_2=use_ln_2)['score']\n",
    "    bad_score = decompose_layer_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal)['score']\n",
    "    return good_score/bad_score\n",
    "\n",
    "def get_LN_constant_across_tokens(model, embeddings_pos, embeddings_neg, layer, range_normal, range_layer, use_ln_2=False):\n",
    "    num_tokens = embeddings_pos['layers'][layer]['attn_in'].shape[1]\n",
    "    bad_scores = [decompose_layer_attn(layer, embeddings_pos, embeddings_neg, token_idx, range_normal)['score'] for token_idx in range(num_tokens)]\n",
    "    bad_score_idx = np.argmax(bad_scores)\n",
    "    bad_score = bad_scores[bad_score_idx]\n",
    "    good_score = decompose_layer_wrt_attn(layer, embeddings_pos, embeddings_neg, bad_score_idx, range_normal, model, range_layer, is_mlp=False, use_ln_2=use_ln_2)['score']\n",
    "    return good_score/bad_score\n",
    "\n",
    "def get_contribs_wrt_attn_naive(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal, range_layer, token_only=False, use_ln_2=False):\n",
    "    # we're naively assuming that locally, we can approximate layer-norm as just scaling our output by a constant\n",
    "    c = get_LN_constant(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal, range_layer, use_ln_2=use_ln_2)\n",
    "    \n",
    "    attn_module = model.transformer.h[layer].attn.attention\n",
    "    attn_split_weights = split_mat_heads(attn_module.out_proj.weight, attn_module.num_heads, attn_module.head_dim)\n",
    "    \n",
    "    attn_split_vals_pos = attn_module._split_heads(embeddings_pos['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "    attn_scores_pos = embeddings_pos['layers'][layer]['attn_out'][2]\n",
    "    attn_dots_pos = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_pos, attn_split_weights, attn_split_vals_pos, range_normal)*c\n",
    "    attn_contribs_pos = attn_dots_pos * attn_scores_pos[0, :, token_idx, :]\n",
    "\n",
    "    if embeddings_neg is None:\n",
    "        # zero ablation\n",
    "        attn_split_vals_neg = torch.zeros(attn_split_vals_pos.shape).to(\"cuda\")\n",
    "        attn_scores_neg =  torch.zeros(attn_scores_pos.shape).to(\"cuda\")\n",
    "        attn_dots_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "        attn_contribs_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "    else:\n",
    "        attn_split_vals_neg = attn_module._split_heads(embeddings_neg['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "        attn_scores_neg = embeddings_neg['layers'][layer]['attn_out'][2]\n",
    "        attn_dots_neg = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_neg, attn_split_weights, attn_split_vals_neg, range_normal)*c\n",
    "        attn_contribs_neg = attn_dots_neg * attn_scores_neg[0, :, token_idx, :]\n",
    "\n",
    "    attn_contribs_list = []\n",
    "    if not token_only:\n",
    "        attn_contribs = attn_contribs_pos - attn_contribs_neg\n",
    "        for head, row in enumerate(attn_contribs):\n",
    "            for j, item in enumerate(row):\n",
    "                attn_contribs_list.append({\n",
    "                    \"head\": head, \"token\": j, \"total_contrib\": item.item(), \"layer\": layer,\n",
    "                    \"pos_contrib\": attn_contribs_pos[head,j].item(), \"pos_score\": attn_scores_pos[0,head,token_idx,j].item(), \"pos_dot\": attn_dots_pos[head,j].item(), \n",
    "                    \"neg_contrib\": attn_contribs_neg[head,j].item(), \"neg_score\": attn_scores_neg[0,head,token_idx,j].item(), \"neg_dot\": attn_dots_neg[head,j].item()\n",
    "                })\n",
    "    else:\n",
    "        attn_contribs_pos = attn_contribs_pos.sum(axis=0) # sum over all heads\n",
    "        attn_contribs_neg = attn_contribs_neg.sum(axis=0)\n",
    "        attn_contribs = attn_contribs_pos - attn_contribs_neg\n",
    "        for j, item in enumerate(attn_contribs):\n",
    "            attn_contribs_list.append({\n",
    "                \"token\": j, \"total_contrib\": item.item(), \"layer\": layer,\n",
    "                \"pos_contrib\": attn_contribs_pos[j].item(), \"neg_contrib\": attn_contribs_neg[j].item()\n",
    "            })\n",
    "    attn_contribs_list = sorted(attn_contribs_list, key=lambda x: abs(x['total_contrib']), reverse=True)\n",
    "    return attn_contribs, attn_contribs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ab1e296-a5a3-4516-af5d-801f373a2662",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_head_token_contribs_for_attn_layer_per_token(model, embeddings_pos, embeddings_neg, token, normal, range_layer, token_only=False):\n",
    "    big_list = []\n",
    "    #cs = []\n",
    "    for layer in range(range_layer):\n",
    "        #cs.append(get_LN_constant(model, embeddings_pos, embeddings_neg, layer, token, normal, range_layer))\n",
    "        _, cur_list = get_contribs_wrt_attn_naive(model, embeddings_pos, embeddings_neg, layer, token, normal, range_layer, token_only=token_only)\n",
    "        big_list += cur_list\n",
    "    # get original embedding contribution\n",
    "    #c = sum(cs)/len(cs)\n",
    "    c = 1 # TODO: I think that there shouldn't be any LayerNorm constant for the original embeddings\n",
    "    embedding_contrib = c * (embeddings_pos['layers'][0]['ln_1_in'][0,token,:] - embeddings_neg['layers'][0]['ln_1_in'][0,token,:])@normal\n",
    "    print(c)\n",
    "    big_list.append({\n",
    "        \"layer\": \"embedding\", \"token\": token,\n",
    "        \"total_contrib\": embedding_contrib,\n",
    "    })\n",
    "    return sorted(big_list, key=lambda x: x['total_contrib'], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4699c337-bebb-4979-9f2e-52bd38e442ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_head_contribs_for_attn_layer_per_token(model, embeddings_pos, embeddings_neg, token, normal, range_layer=None):\n",
    "    big_mat_list = []\n",
    "    num_layers = range_layer\n",
    "    if num_layers is None:\n",
    "        num_layers = len(model.transformer.h)\n",
    "    for layer in range(num_layers):\n",
    "        cur_mat = get_contribs_per_head_token_naive(model, embeddings_pos, embeddings_neg, layer, token, normal, range_layer=range_layer)[0]\n",
    "        big_mat_list.append(cur_mat.sum(axis=1))\n",
    "    return torch.stack(big_mat_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b090c754-db4c-4edd-a6bb-b0e0507573be",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_head_contribs_for_output_per_token(model, embeddings_pos, embeddings_neg, token, normal):\n",
    "    big_mat_list = []\n",
    "    cs = []\n",
    "    for layer in range(len(model.transformer.h)):\n",
    "        cur_mat = get_contribs_per_head_token_naive(model, embeddings_pos, embeddings_neg, layer, token, normal)[0]\n",
    "        big_mat_list.append(cur_mat.sum(axis=1))\n",
    "    \"\"\"# get original embedding contribution\n",
    "    c = sum(cs)/len(cs)\n",
    "    embedding_contrib_mat = c * (embeddings_pos['layers'][0]['ln_1_in'][0,:,:] - embeddings_neg['layers'][0]['ln_1_in'][0,:,:])@normal\n",
    "    \n",
    "    print(c)\n",
    "    big_list.append({\n",
    "        \"layer\": \"embedding\",\n",
    "        \"total_contrib\": embedding_contrib,\n",
    "    })\"\"\"\n",
    "    return torch.stack(big_mat_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "87589119-831f-4cf6-ba26-1ee43a93c2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mediated_head_contrib(model, head_layer_list, embeddings_pos, embeddings_neg, range_normal):\n",
    "    num_tokens = embeddings_pos['layers'][head_layer_list[0][1]]['attn_in'].shape[1]\n",
    "    # head_layer_list: [ (head, layer) ] from latest layer head to earliest layer head\n",
    "    cs = []\n",
    "    normals = []\n",
    "    for i, head_layer in enumerate(head_layer_list):\n",
    "        if i == 0:\n",
    "            if head_layer[0] == -1:\n",
    "                normals.append(range_normal)\n",
    "            else:\n",
    "                normals.append(get_attn_normal(model, head_layer[1], head_layer[0], range_normal))\n",
    "            cs.append(1)\n",
    "            continue\n",
    "        if head_layer[0] == -1:\n",
    "            # original embedding\n",
    "            cs.append(1)\n",
    "            normals.append(normals[i-1])\n",
    "            continue\n",
    "        try:\n",
    "            with torch.no_grad():\n",
    "                c = get_LN_constant_across_tokens(model, embeddings_pos, embeddings_neg, head_layer[1], normals[i-1], head_layer_list[i-1][1])\n",
    "        except ZeroDivisionError:\n",
    "            return 0, torch.zeros(range_normal.shape).to(\"cuda\")\n",
    "        cs.append(c)\n",
    "        normals.append(get_attn_normal(model, head_layer[1], head_layer[0], c*normals[i-1]))\n",
    "    # now, build up Ys backwards\n",
    "    Ys_pos = []\n",
    "    Ys_neg = []\n",
    "    for i, head_layer in enumerate(reversed(head_layer_list)):\n",
    "        if i == 0:\n",
    "            if head_layer[0] == -1:\n",
    "                # get contribution of original embedding\n",
    "                prev_Y_pos =embeddings_pos['layers'][0]['ln_1_in'][0].T\n",
    "                prev_Y_neg = embeddings_neg['layers'][0]['ln_1_in'][0].T\n",
    "            else:\n",
    "                prev_Y_pos = embeddings_pos['layers'][head_layer[1]]['attn_in'][0].T # after [0].T: (neuron, token)\n",
    "                prev_Y_neg = embeddings_neg['layers'][head_layer[1]]['attn_in'][0].T # after [0].T: (neuron, token)\n",
    "        else:\n",
    "            prev_Y_pos = Ys_pos[i-1]\n",
    "            prev_Y_neg = Ys_neg[i-1]\n",
    "\n",
    "        with torch.no_grad():\n",
    "            Y_pos = prev_Y_pos @ embeddings_pos['layers'][head_layer[1]]['attn_out'][2][0,head_layer[0],:,:].T\n",
    "            Y_neg = prev_Y_neg @ embeddings_neg['layers'][head_layer[1]]['attn_out'][2][0,head_layer[0],:,:].T\n",
    "        \n",
    "        Ys_pos.append(Y_pos)\n",
    "        Ys_neg.append(Y_neg)\n",
    "    return torch.dot(normals[-1], (Ys_pos[-1]-Ys_neg[-1])[:,-1]).item(), normals[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0753ae90-d379-4a4b-9c52-00f3b22aad89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# greedy heuristic\n",
    "# TODO: also account for embeddings\n",
    "def get_max_paths(model, embeddings_pos, embeddings_neg, range_normal, max_length, num_paths):\n",
    "    num_layers = len(model.transformer.h)\n",
    "    num_heads = model.transformer.h[0].attn.attention.num_heads\n",
    "    \n",
    "    my_list = []\n",
    "    embeddings_list = []\n",
    "    for head in range(num_heads):\n",
    "        for layer in range(num_layers):\n",
    "            contrib, _ = get_mediated_head_contrib(model, [(head,layer)], embeddings_pos, embeddings_neg, range_normal)\n",
    "            my_list.append({\"path\": [(head, layer)], \"contrib\": contrib})\n",
    "    contrib, _ = get_mediated_head_contrib(model, [(-1,-1)], embeddings_pos, embeddings_neg, range_normal)\n",
    "    embeddings_list.append({\"path\": [(-1,-1)], \"contrib\": contrib})\n",
    "    \n",
    "    prev_list = list(sorted(my_list, key=lambda x: x['contrib'], reverse=True))[:num_paths]\n",
    "    for cur_path_size in range(1, max_length):\n",
    "        cur_list = []\n",
    "        for list_item in prev_list:\n",
    "            base_path = list_item['path']\n",
    "            for head in range(num_heads):\n",
    "                for layer in range(base_path[-1][1]):\n",
    "                    # let's go, four nested loops!\n",
    "                    cur_path = base_path + [(head, layer)]\n",
    "                    contrib, _ = get_mediated_head_contrib(model, cur_path, embeddings_pos, embeddings_neg, range_normal)\n",
    "                    cur_list.append({'path': cur_path, 'contrib': contrib})\n",
    "            contrib, _ = get_mediated_head_contrib(model, base_path + [(-1,-1)], embeddings_pos, embeddings_neg, range_normal)\n",
    "            embeddings_list.append({'path': base_path + [(-1,-1)], 'contrib': contrib})\n",
    "        prev_list = list(sorted(cur_list, key=lambda x: x['contrib'], reverse=True))[:num_paths]\n",
    "    out_list = prev_list + embeddings_list\n",
    "    out_list = list(sorted(out_list, key=lambda x: x['contrib'], reverse=True))[:num_paths]\n",
    "    return out_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b9bddb-6bb4-4c10-8815-0e0ec8afbc15",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Linear feature tracing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "65ff333b-b34d-43df-aefb-1760428f1e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_attn_normal(model, layer, head, range_normal):\n",
    "    attn_module = model.transformer.h[layer].attn.attention\n",
    "    W_out = split_mat_heads(attn_module.out_proj.weight, attn_module.num_heads, attn_module.head_dim)[head]\n",
    "    W_val = attn_module.v_proj.weight[head*attn_module.head_dim:(head+1)*attn_module.head_dim,:]\n",
    "    # attn_normal = (W_out W_V)^T range_normal = W_V^T W_out^T range_normal\n",
    "    return W_val.T @ W_out.T @ range_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91876f33-e985-4788-8603-e3c6087d6cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fake_mlp_normal(model, layer, range_normal):\n",
    "    return model.transformer.h[layer].mlp.c_fc.weight.T @ (model.transformer.h[layer].mlp.c_proj.weight.T @ range_normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b7b28938-6964-4dce-ac3b-69301c0dcdbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def coupling_coeff(y1, y2): return torch.dot(y1, y2)/torch.linalg.norm(y1)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "30fa9e61-57b5-4538-8dfb-ae9f1d6a57e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cossim(y1, y2): return torch.dot(y1, y2)/(torch.linalg.norm(y1)*torch.linalg.norm(y2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac12250-a6aa-45cb-96f2-93c58954ed0d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Prompt evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "43930dc2-bca6-49e7-b70c-cef707e65d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def slice_tokenized_prompts_into_context_windows(prompts, size, max_tokens=None):\n",
    "    retlist = []\n",
    "    if max_tokens is not None:\n",
    "        num_tokens_seen = 0\n",
    "    for prompt in prompts:\n",
    "        prompt_len = prompt['input_ids'].shape[1]\n",
    "        for i in range(0, prompt_len, size):\n",
    "            input_ids = prompt['input_ids'][0,i:min(i+size, prompt_len)][None]\n",
    "            attention_mask = prompt['attention_mask'][0,i:min(i+size, prompt_len)][None]\n",
    "            retlist.append({'input_ids': input_ids, 'attention_mask': attention_mask})\n",
    "            if max_tokens is not None:\n",
    "                num_tokens_seen += input_ids.shape[1]\n",
    "                if num_tokens_seen > max_tokens: return retlist\n",
    "    return retlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9c3bcd71-d6f8-46e3-83a9-86b668bb0824",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "def embeddings_from_prompt_list(model, prompts, return_np=True, use_tqdm=True, pre_tokenized=False, print_tensors_count=False):\n",
    "    outs_list = []\n",
    "    with torch.no_grad():\n",
    "        if use_tqdm: prompts_gen = tqdm.tqdm(prompts)\n",
    "        else: prompts_gen = prompts\n",
    "        for i, prompt in enumerate(prompts):\n",
    "            cur_embeddings, hooks = attach_hooks(model)\n",
    "            if pre_tokenized:\n",
    "                tokens = {\"input_ids\": prompt['input_ids'].to(\"cuda:0\"), 'attention_mask': prompt['attention_mask'].to(\"cuda:0\")}\n",
    "            else:\n",
    "                tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
    "            model(**tokens)\n",
    "            for hook in hooks: hook.remove()\n",
    "            if return_np:\n",
    "                cur_embeddings = embeddings_torch_to_np(cur_embeddings)\n",
    "            outs_list.append(cur_embeddings)\n",
    "            if print_tensors_count:\n",
    "                print(f\"After loading prompt {i}, number of tensors: {get_num_tensors()}\")\n",
    "    return outs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "02d77bfa-ff80-40b4-844d-747ebd9aa634",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "def embeddings_from_prompt_list2(model, prompts, layer, sublayer, token=None, use_tqdm=True, pre_tokenized=False, print_tensors_count=False, mean_embedding=False, print_every=0, save_out=\"embeddings.pkl\", save_every=None):\n",
    "    outs_list = []\n",
    "    with torch.no_grad():\n",
    "        if use_tqdm: prompts_gen = tqdm.tqdm(prompts)\n",
    "        else: prompts_gen = prompts\n",
    "        for i, prompt in enumerate(prompts):\n",
    "            cur_embeddings, hooks = attach_hooks(model)\n",
    "            if pre_tokenized:\n",
    "                tokens = {\"input_ids\": prompt['input_ids'].to(\"cuda:0\"), 'attention_mask': prompt['attention_mask'].to(\"cuda:0\")}\n",
    "            else:\n",
    "                tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
    "            model(**tokens)\n",
    "            for hook in hooks: hook.remove()\n",
    "            cur_embeddings = embeddings_torch_to_np(cur_embeddings)\n",
    "            if layer is not None:\n",
    "                x = cur_embeddings['layers'][layer][sublayer]\n",
    "            else:\n",
    "                x = cur_embeddings[sublayer]\n",
    "            if token is not None:\n",
    "                x = np.copy(x[0, token])\n",
    "            elif mean_embedding:\n",
    "                x = np.mean(x[0], axis=0)\n",
    "            else:\n",
    "                x = np.copy(x)\n",
    "            outs_list.append(x)\n",
    "            free_embeddings(cur_embeddings)\n",
    "            if print_every > 0 and i % print_every == 0:\n",
    "                print(f\"Prompt {i} has finished processing.\")\n",
    "            if save_every is not None and i % save_every == 0:\n",
    "                pickle.dump(outs_list, open(save_out, \"wb\"))\n",
    "            if print_tensors_count:\n",
    "                print(f\"After loading prompt {i}, number of tensors: {get_num_tensors()}\")\n",
    "    return outs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb75954e-dca3-42b6-80d4-f5624ae03462",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_scores_for_prompt(model, layer, sublayer, prompt, normal, use_tqdm=True, pre_tokenized=False):\n",
    "    embedding = embeddings_from_prompt_list(model, [prompt], return_np=False, use_tqdm=use_tqdm, pre_tokenized=pre_tokenized)[0]\n",
    "    if not (layer is None):\n",
    "        sublayer_embedding = embedding['layers'][layer][sublayer] # assuming that sublayer embedding has shape (1, tokens, neurons)\n",
    "    else:\n",
    "        sublayer_embedding = embedding[sublayer]\n",
    "    sublayer_embedding = sublayer_embedding[0]\n",
    "    # get dots: (tokens, neurons) . (neurons) = (tokens)\n",
    "    with torch.no_grad():\n",
    "        normal_scores = sublayer_embedding @ normal\n",
    "    normal_scores = normal_scores.detach().cpu().numpy()\n",
    "    free_embeddings(embedding)\n",
    "    del sublayer_embedding\n",
    "    do_free()\n",
    "    return normal_scores    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "995a91da-c73c-491c-a0fd-a5c1452fe64b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_scores_for_prompt_list(model, layer, sublayer, prompts, normal, use_tqdm=True, pre_tokenized=False):\n",
    "    embeddings = embeddings_from_prompt_list(model, prompts, return_np=True, use_tqdm=use_tqdm, pre_tokenized=pre_tokenized)\n",
    "    ret_list = []\n",
    "    for i in range(len(embeddings)):\n",
    "        embedding = embeddings[i]\n",
    "        if not (layer is None):\n",
    "            sublayer_embedding = embedding['layers'][layer][sublayer] # assuming that sublayer embedding has shape (1, tokens, neurons)\n",
    "        else:\n",
    "            sublayer_embedding = embedding[sublayer]\n",
    "        sublayer_embedding = sublayer_embedding[0]\n",
    "        # get dots: (tokens, neurons) . (neurons) = (tokens)\n",
    "        normal_scores = sublayer_embedding @ normal\n",
    "        free_embeddings(embedding)\n",
    "        del sublayer_embedding\n",
    "        do_free()\n",
    "        ret_list.append(normal_scores)\n",
    "    return ret_list    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "57bdaeb9-1d3d-4df2-aeeb-1ffb05dc39be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "def get_normal_list_scores_for_prompt_list(model, normals, prompts, use_tqdm=True, pre_tokenized=False, save_every=None, save_out=\"normal_list_scores.pkl\"):\n",
    "    #normals is a list of dicts: [{\"normal\", \"layer\", \"sublayer\", \"name\", \"token\"}]\n",
    "    retval = [ {\n",
    "        'info': {\n",
    "            'name': normal['name'],\n",
    "            'layer': normal['layer'],\n",
    "            'sublayer': normal['sublayer'],\n",
    "            'token': normal['token']\n",
    "        },\n",
    "        'scores': []\n",
    "    } for normal in normals ]\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        if use_tqdm: tqdm_lambda = lambda x: tqdm.tqdm(x)\n",
    "        else: tqdm_lambda = lambda x: x\n",
    "        for prompt_idx, prompt in enumerate(tqdm_lambda(prompts)):\n",
    "            embedding, hooks = attach_hooks(model)\n",
    "            if pre_tokenized:\n",
    "                tokens = {\"input_ids\": prompt['input_ids'].to(\"cuda:0\"), 'attention_mask': prompt['attention_mask'].to(\"cuda:0\")}\n",
    "            else:\n",
    "                tokens = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda:0\")\n",
    "            model(**tokens)\n",
    "            for hook in hooks: hook.remove()\n",
    "            embedding = embeddings_torch_to_np(embedding)\n",
    "            for i, normal_info in enumerate(normals):\n",
    "                normal = normal_info['normal']\n",
    "                layer = normal_info['layer']\n",
    "                sublayer = normal_info['sublayer']\n",
    "                token = normal_info['token']\n",
    "                if not (layer is None):\n",
    "                    sublayer_embedding = embedding['layers'][layer][sublayer] # assuming that sublayer embedding has shape (1, tokens, neurons)\n",
    "                else:\n",
    "                    sublayer_embedding = embedding[sublayer]\n",
    "                sublayer_embedding = sublayer_embedding[0]\n",
    "                # get dots: (tokens, neurons) . (neurons) = (tokens)\n",
    "                normal_scores = sublayer_embedding @ normal\n",
    "                if token is not None:\n",
    "                    normal_scores = normal_scores[token]\n",
    "                retval[i]['scores'].append(normal_scores)\n",
    "            free_embeddings(embedding)\n",
    "            del sublayer_embedding\n",
    "            do_free()\n",
    "            if save_every is not None:\n",
    "                if prompt_idx % save_every == 0 and prompt_idx != 0:\n",
    "                    fp = open(save_out, \"wb\")\n",
    "                    pickle.dump(retval, fp)\n",
    "                    fp.close()\n",
    "    return retval    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f8a023a-5b50-4b3d-b16b-3206852688f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_normal_scores_for_prompt_list(model, layer, sublayer, prompts, normal, k=5, pre_tokenized=False, reverse=False, both=False):\n",
    "    scores_list = []\n",
    "    for prompt in tqdm.tqdm(prompts):\n",
    "        scores_list.append(get_normal_scores_for_prompt(model, layer, sublayer, prompt, normal, use_tqdm=False, pre_tokenized=pre_tokenized))\n",
    "    scores_arr = np.concatenate(scores_list)\n",
    "    scores_tensor = torch.from_numpy(scores_arr)\n",
    "    scores_topk = torch.topk(scores_tensor, k=k, largest=(not reverse), sorted=True)\n",
    "    # go from index to (prompt, token)\n",
    "    prompt_start_indices = np.cumsum([0]+[x.shape[0] for x in scores_list])[:-1]\n",
    "    retlist = []\n",
    "    for score, index in zip(scores_topk.values, scores_topk.indices):\n",
    "        score = score.item()\n",
    "        index = index.item()\n",
    "        prompt_idx = np.searchsorted(prompt_start_indices, index, side='right')-1\n",
    "        try:\n",
    "            token_idx = index-prompt_start_indices[prompt_idx]\n",
    "        except IndexError:\n",
    "            print(f\"prompt_idx: {prompt_idx}. prompt_start_indices: {prompt_start_indices}. index: {index}\")\n",
    "            raise IndexError\n",
    "        retlist.append({\"score\": score, \"prompt_idx\": prompt_idx, \"token_idx\": token_idx})\n",
    "    if both:\n",
    "        scores_topk = torch.topk(scores_tensor, k=k, largest=reverse, sorted=True)\n",
    "        new_retlist = []\n",
    "        for score, index in zip(scores_topk.values, scores_topk.indices):\n",
    "            score = score.item()\n",
    "            index = index.item()\n",
    "            prompt_idx = np.searchsorted(prompt_start_indices, index, side='right')-1\n",
    "            try:\n",
    "                token_idx = index-prompt_start_indices[prompt_idx]\n",
    "            except IndexError:\n",
    "                print(f\"prompt_idx: {prompt_idx}. prompt_start_indices: {prompt_start_indices}. index: {index}\")\n",
    "                raise IndexError\n",
    "            new_retlist.append({\"score\": score, \"prompt_idx\": prompt_idx, \"token_idx\": token_idx})\n",
    "        return retlist, new_retlist\n",
    "    return retlist   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ccaca1f9-486d-431d-a2e3-4a5a04f1ef41",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_normal_scores_for_scores_list(prompts, scores_list, k=5, pre_tokenized=False, reverse=False, both=False):\n",
    "    scores_arr = np.concatenate(scores_list)\n",
    "    scores_tensor = torch.from_numpy(scores_arr)\n",
    "    scores_topk = torch.topk(scores_tensor, k=k, largest=(not reverse), sorted=True)\n",
    "    # go from index to (prompt, token)\n",
    "    prompt_start_indices = np.cumsum([0]+[x.shape[0] for x in scores_list])[:-1]\n",
    "    retlist = []\n",
    "    for score, index in zip(scores_topk.values, scores_topk.indices):\n",
    "        score = score.item()\n",
    "        index = index.item()\n",
    "        prompt_idx = np.searchsorted(prompt_start_indices, index, side='right')-1\n",
    "        try:\n",
    "            token_idx = index-prompt_start_indices[prompt_idx]\n",
    "        except IndexError:\n",
    "            print(f\"prompt_idx: {prompt_idx}. prompt_start_indices: {prompt_start_indices}. index: {index}\")\n",
    "            raise IndexError\n",
    "        retlist.append({\"score\": score, \"prompt_idx\": prompt_idx, \"token_idx\": token_idx})\n",
    "    if both:\n",
    "        scores_topk = torch.topk(scores_tensor, k=k, largest=reverse, sorted=True)\n",
    "        new_retlist = []\n",
    "        for score, index in zip(scores_topk.values, scores_topk.indices):\n",
    "            score = score.item()\n",
    "            index = index.item()\n",
    "            prompt_idx = np.searchsorted(prompt_start_indices, index, side='right')-1\n",
    "            try:\n",
    "                token_idx = index-prompt_start_indices[prompt_idx]\n",
    "            except IndexError:\n",
    "                print(f\"prompt_idx: {prompt_idx}. prompt_start_indices: {prompt_start_indices}. index: {index}\")\n",
    "                raise IndexError\n",
    "            new_retlist.append({\"score\": score, \"prompt_idx\": prompt_idx, \"token_idx\": token_idx})\n",
    "        return retlist, new_retlist\n",
    "    return retlist   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e7206825-43cf-43a6-ac25-6a53b296e5a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit_lens(model, tokenizer, v, k=5, reverse=False):\n",
    "    with torch.no_grad():\n",
    "        unembedded = model.lm_head.weight.data @ model.transformer.ln_f(v)\n",
    "        return tokenizer.batch_decode([ [x] for x in torch.topk(unembedded, k=k, largest=(not reverse)).indices ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e85daa9a-65e3-4f15-81a6-7e4ce62195fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit_lens_layer0(model, tokenizer, v, k=5, reverse=False):\n",
    "    with torch.no_grad():\n",
    "        unembedded = torch.linalg.pinv(model.transformer.wte.weight).T @ v\n",
    "        return tokenizer.batch_decode([ [x] for x in torch.topk(unembedded, k=k, largest=(not reverse)).indices ])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4809e324-9259-490e-8493-5e0539d3467a",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Patching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ad39800d-ede1-4767-89ba-845958ed60d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_single_attn_head(model, layer, head, xs, qk_xs=None):\n",
    "\t# xs are \"attn_in\"\n",
    "\tattn_module = model.transformer.h[layer].attn.attention\n",
    "\tq_proj = attn_module.q_proj.weight[head*attn_module.head_dim:(head+1)*attn_module.head_dim]\n",
    "\tk_proj = attn_module.k_proj.weight[head*attn_module.head_dim:(head+1)*attn_module.head_dim]\n",
    "\tv_proj = attn_module.v_proj.weight[head*attn_module.head_dim:(head+1)*attn_module.head_dim]\n",
    "\n",
    "\tif qk_xs is None: qk_xs = xs\n",
    "\tquery = qk_xs @ q_proj.T\n",
    "\tkey = qk_xs @ k_proj.T\n",
    "\tvalue = xs @ v_proj.T\n",
    "\n",
    "\tquery = query.view(query.shape[0], 1, query.shape[1], query.shape[2])\n",
    "\tkey = key.view(key.shape[0], 1, key.shape[1], key.shape[2])\n",
    "\tvalue = value.view(value.shape[0], 1, value.shape[1], value.shape[2])\n",
    "\n",
    "\tattn_output, attn_weights = attn_module._attn(query, key, value)\n",
    "\n",
    "\to_proj = attn_module.out_proj.weight[:, head*attn_module.head_dim:(head+1)*attn_module.head_dim]\n",
    "\n",
    "\tattn_output = attn_output[:, 0, :, :] #(batch, token, feature)\n",
    "\tattn_output = attn_output @ o_proj.T\n",
    "\n",
    "\treturn attn_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8c5fce25-81c4-4285-b3b5-8425ea77a6db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def path_patching(model, embeddings_pos, embeddings_neg, path, only_patch_ov=False, no_ln_f=False):\n",
    "\t# path is a list like [(13, 'attn', 11), (16, 'mlp'), (17, 'attn', 14)]\n",
    "\tpos_layer = embeddings_pos['layers'][path[0][0]]\n",
    "\tif path[0][1] == 'attn':\n",
    "\t\t#if use_ln: sublayer = 'ln_1_in'\n",
    "\t\t#else: sublayer = 'attn_in'\n",
    "\t\tsublayer = 'ln_1_in'\n",
    "\telif path[0][1] == 'mlp':\n",
    "\t\t#if use_ln: sublayer = 'ln_2_in'\n",
    "\t\t#else: sublayer = 'mlp_in'\n",
    "\t\tsublayer = 'ln_2_in'\n",
    "\n",
    "\tclean = pos_layer[sublayer]\n",
    "\n",
    "\tif embeddings_neg is not None:\n",
    "\t\tneg_layer = embeddings_neg['layers'][path[0][0]]\n",
    "\t\tdirty = neg_layer[sublayer]\n",
    "\telse:\n",
    "\t\tdirty = torch.zeros(clean.shape).to(\"cuda\")\n",
    "\n",
    "\tfor component in path:\n",
    "\t\tlayer = component[0]\n",
    "\t\tmodel_layer = model.transformer.h[layer]\n",
    "\t\tbase = embeddings_pos['layers'][component[0]]\n",
    "\t\tbase_full_embed = base[sublayer]\n",
    "\t\tdirty_full_embed = base_full_embed - clean + dirty\n",
    "\t\tif component[1] == 'attn':\n",
    "\t\t\t#if use_ln:\n",
    "\t\t\t#\tdirty = model_layer.ln_1(dirty_full_embed)\n",
    "\t\t\tdirty = model_layer.ln_1(dirty_full_embed)\n",
    "\t\t\tif only_patch_ov:\n",
    "\t\t\t\tdirty = compute_single_attn_head(model, layer, component[2], dirty, qk_xs=base['attn_in'])\n",
    "\t\t\telse:\n",
    "\t\t\t\tdirty = compute_single_attn_head(model, layer, component[2], dirty)\n",
    "\t\t\t\t\n",
    "\t\t\tclean = model_layer.ln_1(base_full_embed)\n",
    "\t\t\tclean = compute_single_attn_head(model, layer, component[2], clean)\n",
    "\t\t\t#clean = base['attn_out'][0]\n",
    "\t\telif component[1] == 'mlp':\n",
    "\t\t\t#if use_ln:\n",
    "\t\t\t#\tdirty = model_layer.ln_2(dirty_full_embed)\n",
    "\t\t\tdirty = model_layer.ln_2(dirty_full_embed)\n",
    "\t\t\tdirty = model_layer.mlp(dirty)\n",
    "\t\t\tclean = base['mlp_out']\n",
    "\n",
    "\t# last step: patch ln_f\n",
    "\tbase_full_embed = embeddings_pos['ln_f_in']\n",
    "\tdirty_full_embed = base_full_embed - clean + dirty\n",
    "\tif no_ln_f:\n",
    "\t\treturn base_full_embed, dirty_full_embed\n",
    "\t\n",
    "\tdirty = model.lm_head(model.transformer.ln_f(dirty_full_embed))\n",
    "\tclean = model.lm_head(model.transformer.ln_f(base_full_embed))\n",
    "\treturn clean, dirty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb08c03-9f67-4007-a88d-27b6d7683bbf",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Finding feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "90cf15ce-888d-4fe6-ae9f-fa05a7430fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_layer_subset(model, path, hidden_states):\n",
    "    for component in path:\n",
    "        residual = hidden_states\n",
    "        layer = component['layer']\n",
    "        model_layer = model.transformer.h[layer]\n",
    "        if component['sublayer'] == 'attn':\n",
    "            hidden_states = model_layer.ln_1(hidden_states)\n",
    "            if component['head'] == None:\n",
    "                attn_outputs = model.transformer.h[layer].attn(hidden_states)\n",
    "                attn_output = attn_outputs[0]\n",
    "            else:\n",
    "                attn_output = compute_single_attn_head(model, layer, component['head'], hidden_states)\n",
    "            hidden_states = residual + attn_output\n",
    "        elif component['sublayer'] == 'mlp':\n",
    "            hidden_states = model_layer.ln_2(hidden_states)\n",
    "            feed_forward_hidden_states = model_layer.mlp(hidden_states)\n",
    "            hidden_states = residual + feed_forward_hidden_states\n",
    "    return hidden_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9b5a3ba2-34ca-4aa9-93e5-95447900dbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def path_patching_per_token(model, embeddings_pos, embeddings_neg, path, only_patch_ov=False, no_ln_f=False, range_normal=None, only_return_path_diffs=False):\n",
    "    # path is a list like [{'layer': 6, 'head': 6, 'sublayer': 'attn', 'token': 3}, {'layer': 15, 'head': 13, 'sublayer': 'attn', 'token': 3}]\n",
    "    #print(path)\n",
    "    with torch.no_grad():\n",
    "        pos_layer = embeddings_pos['layers'][path[0]['layer']]\n",
    "        if path[0]['sublayer'] == 'attn' or path[0]['sublayer'] == 'embedding':\n",
    "            sublayer = 'ln_1_in'\n",
    "        elif path[0]['sublayer'] == 'mlp':\n",
    "            sublayer = 'ln_2_in'\n",
    "    \n",
    "        clean = pos_layer[sublayer]\n",
    "    \n",
    "        if embeddings_neg is not None:\n",
    "            neg_layer = embeddings_neg['layers'][path[0]['layer']]\n",
    "            dirty = neg_layer[sublayer]\n",
    "        else:\n",
    "            dirty = torch.zeros(clean.shape).to(\"cuda\")\n",
    "    \n",
    "        for component in path:\n",
    "            layer = component['layer']\n",
    "            model_layer = model.transformer.h[layer]\n",
    "            base = embeddings_pos['layers'][component['layer']]\n",
    "\n",
    "            if component['sublayer'] == 'attn' or component['sublayer'] == 'embedding':\n",
    "                sublayer = 'ln_1_in'\n",
    "            elif component['sublayer'] == 'mlp':\n",
    "                sublayer = 'ln_2_in'\n",
    "            base_full_embed = base[sublayer]\n",
    "            \n",
    "            if component['token'] is not None:\n",
    "                # TODO: this used to be dirty_full_embed = base_full_embed - clean[:, component['token'], :] + dirty[:, component['token'], :]\n",
    "                dirty_full_embed = base_full_embed.clone()\n",
    "                dirty_full_embed[:, component['token'], :] += dirty[:, component['token'], :] - clean[:, component['token'], :]\n",
    "            else:\n",
    "                dirty_full_embed = base_full_embed - clean + dirty\n",
    "\n",
    "            if range_normal is not None:\n",
    "                test_token = component['token'] if component['token'] is not None else -1\n",
    "                #print(f\"Before component {component}, dirty-clean diff: \", (range_normal @ (dirty_full_embed[0,test_token] - base_full_embed[0,test_token])).item())\n",
    "            \n",
    "            if component['sublayer'] == 'attn':\n",
    "                dirty = model_layer.ln_1(dirty_full_embed)\n",
    "                clean = model_layer.ln_1(base_full_embed)\n",
    "                \n",
    "                if component['head'] == None:\n",
    "                    clean_new = torch.zeros(dirty.shape).to(\"cuda\")\n",
    "                    dirty_new = torch.zeros(dirty.shape).to(\"cuda\")\n",
    "                    for head in range(16):\n",
    "                        if only_patch_ov:\n",
    "                            dirty_new += compute_single_attn_head(model, layer, head, dirty, qk_xs=base['attn_in'])\n",
    "                        else:\n",
    "                            dirty_new += compute_single_attn_head(model, layer, head, dirty)\n",
    "                        clean_new += compute_single_attn_head(model, layer, head, clean)\n",
    "                    dirty = dirty_new\n",
    "                    clean = clean_new\n",
    "                else:\n",
    "                    if only_patch_ov:\n",
    "                        dirty = compute_single_attn_head(model, layer, component['head'], dirty, qk_xs=base['attn_in'])\n",
    "                    else:\n",
    "                        dirty = compute_single_attn_head(model, layer, component['head'], dirty)\n",
    "                    clean = compute_single_attn_head(model, layer, component['head'], clean)\n",
    "            elif component['sublayer'] == 'mlp':\n",
    "                dirty = model_layer.ln_2(dirty_full_embed)\n",
    "                dirty = model_layer.mlp(dirty)\n",
    "\n",
    "                clean = base['mlp_out']\n",
    "            elif component['sublayer'] == 'embedding':\n",
    "                dirty = dirty_full_embed\n",
    "                clean = base_full_embed\n",
    "    \n",
    "        if only_return_path_diffs:\n",
    "            if component['sublayer'] != 'embedding':\n",
    "                return base_full_embed + clean, dirty_full_embed + dirty\n",
    "            return base_full_embed, dirty_full_embed\n",
    "        \n",
    "        # last step: patch ln_f\n",
    "        base_full_embed = embeddings_pos['ln_f_in']\n",
    "        dirty_full_embed = base_full_embed - clean[:, -1, :] + dirty[:, -1, :]\n",
    "        if no_ln_f:\n",
    "            return base_full_embed, dirty_full_embed\n",
    "        \n",
    "        dirty = model.lm_head(model.transformer.ln_f(dirty_full_embed))\n",
    "        clean = model.lm_head(model.transformer.ln_f(base_full_embed))\n",
    "    return clean, dirty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "692020f9-c932-449f-96d8-bbc598dddf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagate_normal_through_LayerNorm(model, embeddings, layer, normal, ln_1=False, token=None, use_ln_real=False):\n",
    "    if use_ln_real:\n",
    "        with torch.no_grad():\n",
    "            if ln_1:\n",
    "                ln = model.transformer.h[layer].ln_1\n",
    "                x = embeddings['layers'][layer]['ln_1_in'][0]\n",
    "            else:\n",
    "                ln = model.transformer.h[layer].ln_2\n",
    "                x = embeddings['layers'][layer]['ln_2_in'][0]\n",
    "            if token is None:\n",
    "                x = torch.mean(x, axis=-2)\n",
    "            else:\n",
    "                x = x[token]\n",
    "        zeros = torch.zeros(normal.shape).to(\"cuda\")\n",
    "        print(x.shape)\n",
    "        new_normal = get_tangent_plane_given_point(x, ln, normal, zeros)\n",
    "        return new_normal\n",
    "    else:\n",
    "        with torch.no_grad():\n",
    "            if ln_1:\n",
    "                W = model.transformer.h[layer].ln_1.weight\n",
    "                x = embeddings['layers'][layer]['ln_1_in'][0]\n",
    "            else:\n",
    "                W = model.transformer.h[layer].ln_2.weight\n",
    "                x = embeddings['layers'][layer]['ln_2_in'][0]\n",
    "            if token is None:\n",
    "                x_bar = torch.linalg.norm(x, axis=1).mean()\n",
    "            else:\n",
    "                x = x[token]\n",
    "                x_bar = torch.linalg.norm(x)\n",
    "            d = x.shape[-1]\n",
    "            return np.sqrt(d)/x_bar * W * normal\n",
    "\n",
    "def get_head_token_contribs_for_layer(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal):\n",
    "    with torch.no_grad():\n",
    "        attn_module = model.transformer.h[layer].attn.attention\n",
    "        attn_split_weights = split_mat_heads(attn_module.out_proj.weight, attn_module.num_heads, attn_module.head_dim)\n",
    "        attn_split_vals_pos = attn_module._split_heads(embeddings_pos['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "        attn_scores_pos = embeddings_pos['layers'][layer]['attn_out'][2]\n",
    "        attn_dots_pos = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_pos, attn_split_weights, attn_split_vals_pos, range_normal)\n",
    "        attn_contribs_pos = attn_dots_pos * attn_scores_pos[0, :, token_idx, :]\n",
    "\n",
    "        if embeddings_neg is None:\n",
    "            # zero ablation\n",
    "            attn_split_vals_neg = torch.zeros(attn_split_vals_pos.shape).to(\"cuda\")\n",
    "            attn_scores_neg =  torch.zeros(attn_scores_pos.shape).to(\"cuda\")\n",
    "            attn_dots_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "            attn_contribs_neg =  torch.zeros(attn_contribs_pos.shape).to(\"cuda\")\n",
    "        else:\n",
    "            attn_split_vals_neg = attn_module._split_heads(embeddings_neg['layers'][layer]['v_proj_out'], attn_module.num_heads, attn_module.head_dim)\n",
    "            attn_scores_neg = embeddings_neg['layers'][layer]['attn_out'][2]\n",
    "            attn_dots_neg = get_dots_per_head_and_token(token_idx, attn_module, attn_scores_neg, attn_split_weights, attn_split_vals_neg, range_normal)\n",
    "            attn_contribs_neg = attn_dots_neg * attn_scores_neg[0, :, token_idx, :]\n",
    "\n",
    "        attn_contribs = attn_contribs_pos - attn_contribs_neg\n",
    "        attn_contribs_list = []\n",
    "        for head, row in enumerate(attn_contribs):\n",
    "            for j, item in enumerate(row):\n",
    "                attn_contribs_list.append({\n",
    "                    \"path\": {\"head\": head, \"token\": j, \"layer\": layer, \"sublayer\": \"attn\"},\n",
    "                    \"contrib\": item.item()\n",
    "                })\n",
    "    attn_contribs_list = sorted(attn_contribs_list, key=lambda x: abs(x['contrib']), reverse=True)\n",
    "    return attn_contribs_list\n",
    "\n",
    "def get_head_token_contribs(model, embeddings_pos, embeddings_neg, token_idx, range_normal, num_layers=None):\n",
    "    if num_layers is None: num_layers = len(embeddings_pos['layers'])\n",
    "    retlist = []\n",
    "    for layer in range(num_layers):\n",
    "        retlist += get_head_token_contribs_for_layer(model, embeddings_pos, embeddings_neg, layer, token_idx, range_normal)\n",
    "    retlist = sorted(retlist, key=lambda x: abs(x['contrib']), reverse=True)\n",
    "    return retlist\n",
    "    \n",
    "def get_mlp_contribs(model, embeddings_pos, embeddings_neg, token_idx, range_normal, num_layers=None):\n",
    "    if num_layers is None: num_layers = len(embeddings_pos['layers'])\n",
    "    retlist = []\n",
    "    for layer in range(num_layers):\n",
    "        with torch.no_grad():\n",
    "            mlp_y_pos = embeddings_pos['layers'][layer]['mlp_out'][0, token_idx, :]\n",
    "            mlp_y_neg = embeddings_neg['layers'][layer]['mlp_out'][0, token_idx, :]\n",
    "            mlp_dot = range_normal @ mlp_y_pos - range_normal @ mlp_y_neg\n",
    "        retlist.append({\n",
    "            \"path\": {\"head\": -1, \"token\": token_idx, \"layer\": layer, \"sublayer\": \"mlp\"},\n",
    "            \"contrib\": mlp_dot.item()\n",
    "        })\n",
    "    retlist = sorted(retlist, key=lambda x: abs(x['contrib']), reverse=True)\n",
    "    return retlist\n",
    "\n",
    "def get_normal_for_path(model, embeddings_pos, embeddings_neg, range_normal, path, use_ln=True, normalize=False):\n",
    "    normal = range_normal\n",
    "    for node in path:\n",
    "        cur_token = node['token']\n",
    "        cur_layer = node['layer']\n",
    "        cur_sublayer = node['sublayer']\n",
    "        cur_head = node['head']\n",
    "        \n",
    "        use_ln_1 = False\n",
    "        if cur_sublayer == 'attn':\n",
    "            if cur_head != -1:\n",
    "                new_normal = get_attn_normal(model, cur_layer, cur_head, normal)\n",
    "            else:\n",
    "                attn_module = model.transformer.h[cur_layer].attn.attention\n",
    "                attn_module_v_proj = attn_module.v_proj.weight\n",
    "                attn_module_o_proj = attn_module.out_proj.weight\n",
    "                combined_WOV = torch.zeros(2048,2048).to(\"cuda\")\n",
    "                for head in range(attn_module.num_heads):\n",
    "                    combined_WOV += get_ov_mat_for_head(attn_module_v_proj, attn_module_o_proj, 16, 128, head)\n",
    "                new_normal = combined_WOV.T @ normal\n",
    "        elif cur_sublayer == 'mlp':\n",
    "            xpos = embeddings_pos['layers'][cur_layer]['mlp_in'][0,cur_token,:]\n",
    "            xneg = embeddings_neg['layers'][cur_layer]['mlp_in'][0,cur_token,:]\n",
    "            zeros = torch.zeros(normal.shape).to(\"cuda\")\n",
    "            affine = domain_boundary_intersection(xpos, xneg,\n",
    "                model.transformer.h[cur_layer].mlp, normal, zeros, lr=0.02, eps=0.0000001)\n",
    "            new_normal = get_tangent_plane_given_point(affine, model.transformer.h[cur_layer].mlp, normal, zeros)\n",
    "            use_ln_1 = True\n",
    "        else:\n",
    "            raise Exception(\"Invalid sublayer: \" + str(cur_sublayer))\n",
    "        if use_ln:\n",
    "            new_normal = propagate_normal_through_LayerNorm(model, embeddings_pos, cur_layer, new_normal, ln_1=use_ln_1, token=cur_token)\n",
    "        normal = new_normal\n",
    "    if normalize: normal = normal / torch.linalg.norm(normal)\n",
    "    return normal.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1e2865f0-20f5-47f3-b997-30157c9ca97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def patch_get_max_paths(model, embeddings_pos, embeddings_neg, range_normal, max_length, num_paths, per_token=True, use_mlp=True, use_attn=True, starting_path=None, least_attrib=False, no_ln_f=True):\n",
    "    num_tokens = embeddings_pos['layers'][-1]['attn_in'].shape[1]\n",
    "    if per_token: assert(num_tokens == embeddings_neg['layers'][-1]['attn_in'].shape[1])\n",
    "    \n",
    "    embedding_contribs_list = []\n",
    "    \n",
    "    paths = [starting_path]\n",
    "    if starting_path is None:\n",
    "        paths=[ [] ]                     \n",
    "    \n",
    "    for cur_length in range(0, max_length):\n",
    "        print(f\"Paths of length {cur_length+1}:\\n===================\")\n",
    "        cur_contribs_list = []\n",
    "        for path in paths:\n",
    "            try:\n",
    "                cur_token = path[0]['token']\n",
    "                cur_layer = path[0]['layer']\n",
    "                cur_sublayer = path[0]['sublayer']\n",
    "            except:\n",
    "                cur_token = -1\n",
    "                cur_layer = len(model.transformer.h)\n",
    "                cur_sublayer = \"\"\n",
    "            \n",
    "            # TODO: if (cur_layer, cur_sublayer) == (0, 'attn'), then treat this as an embedding\n",
    "            \n",
    "            # append contrib for each head-token, MLP to cur_contribs_list\n",
    "            # first, attention: loop through all layer-head-tokens\n",
    "            if use_attn:\n",
    "                attn_num_layers = cur_layer\n",
    "                if cur_sublayer == 'attn':\n",
    "                    attn_num_layers -= 1\n",
    "                for attn_layer in range(attn_num_layers):\n",
    "                    for head in range(model.transformer.h[0].attn.attention.num_heads):\n",
    "                        if per_token:\n",
    "                            for token in range(num_tokens):\n",
    "                                new_path = [{'layer': attn_layer, 'sublayer': 'attn', 'token': token, 'head': head}] + path\n",
    "                                clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                                contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                                cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "                        else:\n",
    "                            new_path = [{'layer': attn_layer, 'sublayer': 'attn', 'token': None, 'head': head}] + path\n",
    "                            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                            cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            # next, mlp\n",
    "            if use_mlp:\n",
    "                mlp_num_layers = cur_layer - 1\n",
    "                for mlp_layer in range(mlp_num_layers):\n",
    "                    new_path = [{'layer': mlp_layer, 'sublayer': 'mlp', 'token': cur_token, 'head': -1}] + path\n",
    "                    clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                    contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                    cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "                    \n",
    "            # now, embeddings\n",
    "            new_path = [{'layer': 0, 'sublayer': 'embedding', 'token': cur_token, 'head': -1}] + path\n",
    "            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "            embedding_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            \n",
    "        # sort cur_contribs_list, take the top num_paths entries\n",
    "        if type(num_paths) == list:\n",
    "            cur_num_paths = num_paths[cur_length-1]\n",
    "        elif type(num_paths) == int:\n",
    "            cur_num_paths = num_paths\n",
    "        else:\n",
    "            raise Exception(\"Invalid type for num_paths: \" + str(type(num_paths)))\n",
    "        \n",
    "        cur_contribs_list = list(sorted(cur_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))[:cur_num_paths]\n",
    "        for contrib in cur_contribs_list:\n",
    "            print_path(list(reversed(contrib['path'])))\n",
    "            print(\":\", contrib['contrib'])\n",
    "        embedding_contribs_list = list(sorted(embedding_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))[:cur_num_paths]\n",
    "        for contrib in embedding_contribs_list:\n",
    "            print_path(list(reversed(contrib['path'])))\n",
    "            print(\":\", contrib['contrib'])\n",
    "        print()\n",
    "        paths = [x['path'] for x in cur_contribs_list]\n",
    "    return cur_contribs_list, embedding_contribs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b85b27a2-4a51-49d3-8ce7-50dcc2ccf77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def domain_boundary_intersection(x_pos, x_neg, f, range_normal, range_affine, lr=0.01, eps=0.000001):\n",
    "    with torch.enable_grad():\n",
    "        g = lambda t_: (range_normal.detach() @ ( f((1-t_)*x_neg.detach() + t_*x_pos.detach()) - range_affine.detach()))**2\n",
    "        t = torch.Tensor([0.5]).detach().to(\"cuda\")\n",
    "        t.requires_grad_(True)\n",
    "        \n",
    "        optimizer = torch.optim.Adam([t], lr=lr)\n",
    "        \n",
    "        loss = None\n",
    "        prev_loss = None\n",
    "        while loss is None or abs(loss.item()) > eps:\n",
    "            optimizer.zero_grad()\n",
    "            prev_loss = loss\n",
    "            loss = g(t)\n",
    "            if prev_loss is not None and abs(prev_loss.item() - loss.item()) < eps**2: break\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            with torch.no_grad():\n",
    "                t[:] = torch.clamp(t, min=0, max=1)\n",
    "        #print(\"Final loss:\", loss.item())\n",
    "        #print(\"Final t:\", t)\n",
    "        \n",
    "        #if prev_loss is not None: print(abs(prev_loss.item() - loss.item()))\n",
    "        #if t.item() == 0 or t.item() == 1: t = 0.5\n",
    "        return (1-t)*x_neg + t*x_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2549581b-eb8a-421e-80aa-d449a06b497d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_path(path):\n",
    "    for i, node in enumerate(path):\n",
    "        if node['sublayer'] == 'attn':\n",
    "            print(f\"attn{node['layer']}.{node['head']}@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'mlp':\n",
    "            print(f\"mlp{node['layer']}@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'lm_out':\n",
    "            print(f\"lm_out@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'embedding':\n",
    "            print(f\"embedding@{node['token']}\", end=\"\")\n",
    "        if i != len(path)-1: print(\" <= \", end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "29356126-ed1f-4fee-abbe-3ff7f1aa79dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def token2embedding(token):\n",
    "    with torch.no_grad():\n",
    "        retval = model.transformer.wte(tokenizer(token, return_tensors=\"pt\")['input_ids'][0,0].to(\"cuda\"))\n",
    "    return retval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "89c7aa71-f16c-4e2f-99df-c6ff51fcce84",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_normal_for_path(model, embeddings_pos, embeddings_neg, range_normal, path, use_ln=True, use_ln_real=False, normalize=False, return_affines=False):\n",
    "    normal = range_normal\n",
    "    zeros = torch.zeros(normal.shape).to(\"cuda\")\n",
    "    affine = torch.zeros(normal.shape).to(\"cuda\")\n",
    "    range_c = torch.zeros(1).to(\"cuda\")\n",
    "\n",
    "    if return_affines:\n",
    "        affines = [affine]\n",
    "    for node in path:\n",
    "        cur_token = node['token']\n",
    "        cur_layer = node['layer']\n",
    "        cur_sublayer = node['sublayer']\n",
    "        cur_head = node['head']\n",
    "        \n",
    "        use_ln_1 = False\n",
    "        if cur_sublayer == 'attn':\n",
    "            if cur_head != -1:\n",
    "                new_normal = get_attn_normal(model, cur_layer, cur_head, normal)\n",
    "        elif cur_sublayer == 'mlp':\n",
    "            xpos = embeddings_pos['layers'][cur_layer]['mlp_in'][0,cur_token,:]\n",
    "            xneg = embeddings_neg['layers'][cur_layer]['mlp_in'][0,cur_token,:]\n",
    "            zeros = torch.zeros(normal.shape).to(\"cuda\")\n",
    "            new_affine = domain_boundary_intersection(xpos, xneg,\n",
    "                model.transformer.h[cur_layer].mlp, normal, range_c, lr=0.02, eps=0.0000001)\n",
    "            if return_affines:\n",
    "                affines.append(new_affine)\n",
    "            new_normal = get_tangent_plane_given_point(new_affine, model.transformer.h[cur_layer].mlp, normal, zeros)\n",
    "            range_c = (range_c - new_normal @ new_affine).detach().item()\n",
    "            use_ln_1 = True\n",
    "        else:\n",
    "            raise Exception(\"Invalid sublayer: \" + str(cur_sublayer))\n",
    "        if use_ln:\n",
    "            new_normal = propagate_normal_through_LayerNorm(model, embeddings_pos, cur_layer, new_normal, ln_1=use_ln_1, token=cur_token, use_ln_real=use_ln_real)\n",
    "        normal = new_normal\n",
    "    if normalize: normal = normal / torch.linalg.norm(normal)\n",
    "    if return_affines:\n",
    "        return normal.detach(), affines\n",
    "    return normal.detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a3e1c640-feae-4ea8-a621-9c45555c46b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def patch_get_max_paths(model, embeddings_pos, embeddings_neg, range_normal, max_length, num_paths, use_mlp=True, use_attn=True, starting_path=None, least_attrib=False, no_ln_f=True):\n",
    "    num_tokens = embeddings_pos['layers'][-1]['attn_in'].shape[1]\n",
    "    assert(num_tokens == embeddings_neg['layers'][-1]['attn_in'].shape[1])\n",
    "    print(f\"Num tokens: {num_tokens}\")\n",
    "    \n",
    "    embedding_contribs_list = []\n",
    "    \n",
    "    paths = [starting_path]\n",
    "    if starting_path is None:\n",
    "        paths=[ [] ]                     \n",
    "    \n",
    "    for cur_length in range(0, max_length):\n",
    "        print(f\"Paths of length {cur_length+1}:\\n===================\")\n",
    "        cur_contribs_list = []\n",
    "        for path in paths:\n",
    "            try:\n",
    "                cur_token = path[0]['token']\n",
    "                cur_layer = path[0]['layer']\n",
    "                cur_sublayer = path[0]['sublayer']\n",
    "            except:\n",
    "                cur_token = -1\n",
    "                cur_layer = len(model.transformer.h)\n",
    "                cur_sublayer = \"\"\n",
    "            \n",
    "            # TODO: if (cur_layer, cur_sublayer) == (0, 'attn'), then treat this as an embedding\n",
    "            \n",
    "            # append contrib for each head-token, MLP to cur_contribs_list\n",
    "            # first, attention: loop through all layer-head-tokens\n",
    "            if use_attn:\n",
    "                attn_num_layers = cur_layer\n",
    "                if cur_sublayer == 'attn':\n",
    "                    attn_num_layers -= 1\n",
    "                for attn_layer in range(attn_num_layers):\n",
    "                    for head in range(model.transformer.h[0].attn.attention.num_heads):\n",
    "                        for token in range(num_tokens):\n",
    "                            new_path = [{'layer': attn_layer, 'sublayer': 'attn', 'token': token, 'head': head}] + path\n",
    "                            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                            cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            # next, mlp\n",
    "            if use_mlp:\n",
    "                mlp_num_layers = cur_layer - 1\n",
    "                for mlp_layer in range(mlp_num_layers):\n",
    "                    new_path = [{'layer': mlp_layer, 'sublayer': 'mlp', 'token': cur_token, 'head': -1}] + path\n",
    "                    clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                    contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                    cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "                    \n",
    "            # now, embeddings\n",
    "            new_path = [{'layer': 0, 'sublayer': 'embedding', 'token': cur_token, 'head': -1}] + path\n",
    "            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "            embedding_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            \n",
    "        # sort cur_contribs_list, take the top num_paths entries\n",
    "        if type(num_paths) == list:\n",
    "            cur_num_paths = num_paths[cur_length-1]\n",
    "        elif type(num_paths) == int:\n",
    "            cur_num_paths = num_paths\n",
    "        else:\n",
    "            raise Exception(\"Invalid type for num_paths: \" + str(type(num_paths)))\n",
    "        \n",
    "        cur_contribs_list = list(sorted(cur_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))[:cur_num_paths]\n",
    "        for contrib in cur_contribs_list:\n",
    "            print_path(list(reversed(contrib['path'])))\n",
    "            print(\":\", contrib['contrib'])\n",
    "        embedding_contribs_list = list(sorted(embedding_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))[:cur_num_paths]\n",
    "        for contrib in embedding_contribs_list:\n",
    "            print_path(list(reversed(contrib['path'])))\n",
    "            print(\":\", contrib['contrib'])\n",
    "        print()\n",
    "        paths = [x['path'] for x in cur_contribs_list]\n",
    "    return cur_contribs_list, embedding_contribs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "efc23813-046c-4250-8acb-9a9cb36e01f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def patch_get_max_paths_reverse(model, embeddings_pos, embeddings_neg, range_normal, max_length, num_paths, starting_path, use_mlp=True, use_attn=True, least_attrib=False, no_ln_f=True, do_print=True):\n",
    "    num_tokens = embeddings_pos['layers'][-1]['attn_in'].shape[1]\n",
    "    assert(num_tokens == embeddings_neg['layers'][-1]['attn_in'].shape[1])\n",
    "    if do_print: print(f\"Num tokens: {num_tokens}\")\n",
    "    \n",
    "    paths = [starting_path]          \n",
    "    \n",
    "    for cur_length in range(0, max_length):\n",
    "        if do_print: print(f\"Paths of length {cur_length+1}:\\n===================\")\n",
    "        cur_contribs_list = []\n",
    "        for path in paths:\n",
    "            try:\n",
    "                cur_token = path[-1]['token']\n",
    "                cur_layer = path[-1]['layer']\n",
    "                cur_sublayer = path[-1]['sublayer']\n",
    "            except:\n",
    "                cur_token = -1\n",
    "                cur_layer = len(model.transformer.h)\n",
    "                cur_sublayer = \"\"\n",
    "            \n",
    "            # TODO: if (cur_layer, cur_sublayer) == (0, 'attn'), then treat this as an embedding\n",
    "            \n",
    "            # append contrib for each head-token, MLP to cur_contribs_list\n",
    "            # first, attention: loop through all layer-head-tokens\n",
    "            if use_attn:\n",
    "                attn_start_layer = cur_layer+1\n",
    "                for attn_layer in range(attn_start_layer, len(model.transformer.h)):\n",
    "                    for head in range(model.transformer.h[0].attn.attention.num_heads):\n",
    "                        for token in range(num_tokens):\n",
    "                            new_path = path + [{'layer': attn_layer, 'sublayer': 'attn', 'token': token, 'head': head},\n",
    "                                #{'layer': None, 'sublayer': 'dense', 'token': None, 'head': None}\n",
    "                            ]\n",
    "                            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                            cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            # next, mlp\n",
    "            if use_mlp:\n",
    "                mlp_start_layer = cur_layer + 1\n",
    "                if cur_sublayer == 'attn':\n",
    "                    mlp_start_layer = cur_layer\n",
    "                for mlp_layer in range(mlp_start_layer, len(model.transformer.h)):\n",
    "                    new_path = path + [{'layer': mlp_layer, 'sublayer': 'mlp', 'token': cur_token, 'head': -1},\n",
    "                    ]\n",
    "                    clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                    contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                    cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "            \n",
    "        # sort cur_contribs_list, take the top num_paths entries\n",
    "        if type(num_paths) == list:\n",
    "            cur_num_paths = num_paths[cur_length-1]\n",
    "        elif type(num_paths) == int:\n",
    "            cur_num_paths = num_paths\n",
    "        else:\n",
    "            raise Exception(\"Invalid type for num_paths: \" + str(type(num_paths)))\n",
    "        \n",
    "        cur_contribs_list = list(sorted(cur_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))[:cur_num_paths]\n",
    "        if do_print:\n",
    "            for contrib in cur_contribs_list:\n",
    "                print_path(list(reversed(contrib['path'])))\n",
    "                print(\":\", contrib['contrib'])\n",
    "            print()\n",
    "        paths = [x['path'] for x in cur_contribs_list]\n",
    "    return cur_contribs_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d3635024-93a0-400b-b72f-6e8c045fa38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def domain_boundary_intersection(x_pos, x_neg, f, range_normal, range_c, lr=0.01, eps=0.000001):\n",
    "    with torch.enable_grad():\n",
    "        g = lambda t_: (range_normal.detach() @ f((1-t_)*x_neg.detach() + t_*x_pos.detach()) + range_c)\n",
    "        t = torch.Tensor([0.5]).detach().to(device=\"cuda\")\n",
    "        g0 = g(0).detach().item()\n",
    "        g1 = g(1).detach().item()\n",
    "        target = 0\n",
    "        if g0/g1 > 0:\n",
    "            # both end points have same sign\n",
    "            target = (g0+g1)/2 # let's just say that our decision boundary is the average of the two values in this case\n",
    "        t.requires_grad_(True)\n",
    "        \n",
    "        optimizer = torch.optim.Adam([t], lr=lr)\n",
    "        \n",
    "        loss = None\n",
    "        prev_loss = None\n",
    "        while loss is None or abs(loss.item()) > eps:\n",
    "            optimizer.zero_grad()\n",
    "            prev_loss = loss\n",
    "            loss = (g(t) - target)**2\n",
    "            if prev_loss is not None and abs(prev_loss.item() - loss.item()) < eps**2: break\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            with torch.no_grad():\n",
    "                t[:] = torch.clamp(t, min=0, max=1)\n",
    "        #print(f\"g(0)={g(0)}, g(1)={g(1)}\")\n",
    "        #print(\"Final loss:\", loss.item())\n",
    "        #print(\"Final t:\", t)\n",
    "        \n",
    "        #if prev_loss is not None: print(abs(prev_loss.item() - loss.item()))\n",
    "        #if t.item() == 0 or t.item() == 1: t = 0.5\n",
    "        return (1-t)*x_neg + t*x_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5db47aeb-73ff-43b8-8bfa-ef3591d20e0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_path(path):\n",
    "    for i, node in enumerate(path):\n",
    "        if node['sublayer'] == 'attn':\n",
    "            print(f\"attn{node['layer']}.{node['head']}@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'mlp':\n",
    "            print(f\"mlp{node['layer']}@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'lm_out':\n",
    "            print(f\"lm_out@{node['token']}\", end=\"\")\n",
    "        elif node['sublayer'] == 'embedding':\n",
    "            print(f\"embedding@{node['token']}\", end=\"\")\n",
    "        if i != len(path)-1: print(\" <= \", end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c8a0fe83-be60-4a65-ace5-fa8337942680",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tangent_plane_given_point(x_0_new, f, range_normal, range_affine):\n",
    "    # now, find the tangent hyperplane at x_0_new\n",
    "    x_0_new.requires_grad_(True)\n",
    "    g = lambda x: f(x) @ range_normal - ( range_normal @ range_affine)\n",
    "    grad = torch.autograd.grad(g(x_0_new), x_0_new)\n",
    "    \n",
    "    return grad[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "df17a9c8-57ce-4faf-aa1f-6d323cfffdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tangent_plane_given_point2(x_0_new, f, range_normal, c):\n",
    "    # now, find the tangent hyperplane at x_0_new\n",
    "    x_0_new.requires_grad_(True)\n",
    "    g = lambda x: f(x) @ range_normal + c\n",
    "    grad = torch.autograd.grad(g(x_0_new), x_0_new)\n",
    "    \n",
    "    return grad[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "def2fc0e-7993-4815-ba35-dba850213400",
   "metadata": {},
   "outputs": [],
   "source": [
    "def token2embedding(token):\n",
    "    with torch.no_grad():\n",
    "        retval = model.model.embed_tokens(tokenizer(token, return_tensors=\"pt\")['input_ids'][0,0].to(\"cuda\"))\n",
    "    return retval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "beb4b737-b11e-4750-8e61-3023a0ec1014",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward(self, inputs_embeds, layer_indices):\n",
    "    input_shape = inputs_embeds.size()[:-1]\n",
    "    batch_size = inputs_embeds.shape[0]\n",
    "\n",
    "    device = inputs_embeds.device\n",
    "\n",
    "    position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)\n",
    "    position_ids = position_ids.unsqueeze(0)\n",
    "\n",
    "    position_embeds = self.wpe(position_ids)\n",
    "    hidden_states = inputs_embeds + position_embeds\n",
    "\n",
    "    hidden_states = self.drop(hidden_states)\n",
    "    for block in [self.h[i] for i in layer_indices]:\n",
    "        outputs = block(hidden_states)\n",
    "        hidden_states = outputs[0]\n",
    "    return hidden_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4fde619a-0558-47c6-9186-b6af55b3d873",
   "metadata": {},
   "outputs": [],
   "source": [
    "def path_patching_per_token(model, embeddings_pos, embeddings_neg, path, only_patch_ov=False, no_ln_f=False, range_normal=None, only_return_path_diffs=False):\n",
    "    # path is a list like [{'layer': 6, 'head': 6, 'sublayer': 'attn', 'token': 3}, {'layer': 15, 'head': 13, 'sublayer': 'attn', 'token': 3}]\n",
    "    #print(path)\n",
    "    with torch.no_grad():\n",
    "        pos_layer = embeddings_pos['layers'][path[0]['layer']]\n",
    "        if path[0]['sublayer'] == 'attn' or path[0]['sublayer'] == 'embedding':\n",
    "            sublayer = 'ln_1_in'\n",
    "        elif path[0]['sublayer'] == 'mlp':\n",
    "            sublayer = 'ln_2_in'\n",
    "    \n",
    "        clean = pos_layer[sublayer]\n",
    "    \n",
    "        if embeddings_neg is not None:\n",
    "            neg_layer = embeddings_neg['layers'][path[0]['layer']]\n",
    "            dirty = neg_layer[sublayer]\n",
    "        else:\n",
    "            dirty = torch.zeros(clean.shape).to(\"cuda\")\n",
    "\n",
    "        dense_end_path = False\n",
    "        for component in path:\n",
    "            if component['sublayer'] == 'dense':\n",
    "                dense_end_path = True\n",
    "                break\n",
    "            layer = component['layer']\n",
    "            model_layer = model.transformer.h[layer]\n",
    "            base = embeddings_pos['layers'][component['layer']]\n",
    "\n",
    "            if component['sublayer'] == 'attn' or component['sublayer'] == 'embedding':\n",
    "                sublayer = 'ln_1_in'\n",
    "            elif component['sublayer'] == 'mlp':\n",
    "                sublayer = 'ln_2_in'\n",
    "            base_full_embed = base[sublayer]\n",
    "            \n",
    "            if component['token'] is not None:\n",
    "                # TODO: this used to be dirty_full_embed = base_full_embed - clean[:, component['token'], :] + dirty[:, component['token'], :]\n",
    "                dirty_full_embed = base_full_embed.clone()\n",
    "                dirty_full_embed[:, component['token'], :] += dirty[:, component['token'], :] - clean[:, component['token'], :]\n",
    "            else:\n",
    "                dirty_full_embed = base_full_embed - clean + dirty\n",
    "\n",
    "            if range_normal is not None:\n",
    "                test_token = component['token'] if component['token'] is not None else -1\n",
    "                #print(f\"Before component {component}, dirty-clean diff: \", (range_normal @ (dirty_full_embed[0,test_token] - base_full_embed[0,test_token])).item())\n",
    "            \n",
    "            if component['sublayer'] == 'attn':\n",
    "                dirty = model_layer.ln_1(dirty_full_embed)\n",
    "                clean = model_layer.ln_1(base_full_embed)\n",
    "                \n",
    "                if component['head'] == None:\n",
    "                    clean_new = torch.zeros(dirty.shape).to(\"cuda\")\n",
    "                    dirty_new = torch.zeros(dirty.shape).to(\"cuda\")\n",
    "                    for head in range(model_layer.attn.attention.num_heads):\n",
    "                        if only_patch_ov:\n",
    "                            dirty_new += compute_single_attn_head(model, layer, head, dirty, qk_xs=base['attn_in'])\n",
    "                        else:\n",
    "                            dirty_new += compute_single_attn_head(model, layer, head, dirty)\n",
    "                        clean_new += compute_single_attn_head(model, layer, head, clean)\n",
    "                    dirty = dirty_new\n",
    "                    clean = clean_new\n",
    "                else:\n",
    "                    if only_patch_ov:\n",
    "                        dirty = compute_single_attn_head(model, layer, component['head'], dirty, qk_xs=base['attn_in'])\n",
    "                    else:\n",
    "                        dirty = compute_single_attn_head(model, layer, component['head'], dirty)\n",
    "                    clean = compute_single_attn_head(model, layer, component['head'], clean)\n",
    "            elif component['sublayer'] == 'mlp':\n",
    "                dirty = model_layer.ln_2(dirty_full_embed)\n",
    "                dirty = model_layer.mlp(dirty)\n",
    "\n",
    "                clean = base['mlp_out']\n",
    "            elif component['sublayer'] == 'embedding':\n",
    "                dirty = dirty_full_embed\n",
    "                clean = base_full_embed\n",
    "    \n",
    "        if only_return_path_diffs:\n",
    "            if component['sublayer'] != 'embedding':\n",
    "                return base_full_embed + clean, dirty_full_embed + dirty\n",
    "            return base_full_embed, dirty_full_embed\n",
    "\n",
    "        if dense_end_path:\n",
    "            # if dense_end_path, then let model run normally (all layers) on patched input\n",
    "            component = path[-2]\n",
    "            if component['sublayer'] == 'attn':\n",
    "                # if we ended on an attention layer, then we still have to take care of the MLP layer\n",
    "                base_full_embed = base['ln_2_in']\n",
    "                dirty_full_embed = base_full_embed + (dirty - clean)\n",
    "                \n",
    "                dirty = model_layer.ln_2(dirty_full_embed)\n",
    "                dirty = model_layer.mlp(dirty)+dirty_full_embed\n",
    "                clean = base['block_out'][0]\n",
    "            if layer != len(embeddings_pos['layers']):\n",
    "                start_layer = layer + 1\n",
    "                base_full_embed = embeddings_pos['layers'][start_layer]['ln_1_in']\n",
    "                dirty_full_embed = base_full_embed + (dirty-clean)\n",
    "                dirty = forward(model.transformer, dirty_full_embed, range(start_layer, len(embeddings_pos['layers'])))\n",
    "            clean = embeddings_pos['ln_f_in']\n",
    "\n",
    "        # last step: patch ln_f\n",
    "        base_full_embed = embeddings_pos['ln_f_in']\n",
    "        dirty_full_embed = base_full_embed - clean[:, -1, :] + dirty[:, -1, :]\n",
    "        if no_ln_f:\n",
    "            return base_full_embed, dirty_full_embed\n",
    "        \n",
    "        dirty = model.lm_head(model.transformer.ln_f(dirty_full_embed))\n",
    "        clean = model.lm_head(model.transformer.ln_f(base_full_embed))\n",
    "    return clean, dirty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f3f90056-c4e2-4c25-b215-b0f14d5f1ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def patch_get_max_dense(model, embeddings_pos, embeddings_neg, range_normal, least_attrib=False, no_ln_f=True, tokens_list=None):\n",
    "    num_tokens = embeddings_pos['layers'][-1]['attn_in'].shape[1]\n",
    "    assert(num_tokens == embeddings_neg['layers'][-1]['attn_in'].shape[1])\n",
    "    \n",
    "    cur_contribs_list = []\n",
    "\n",
    "    if tokens_list is None: tokens_list = range(num_tokens)\n",
    "        \n",
    "    # append contrib for each head-token, MLP to cur_contribs_list\n",
    "    for layer in tqdm.tqdm(range(len(model.transformer.h)-1)):\n",
    "        for token in tokens_list:\n",
    "            for head in range(model.transformer.h[0].attn.attention.num_heads):\n",
    "                new_path = [{'layer': layer, 'sublayer': 'attn', 'token': token, 'head': head},\n",
    "                            {'layer': None, 'sublayer': 'dense', 'token': None, 'head': None}]\n",
    "                clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "                contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "                cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "                \n",
    "            new_path = [{'layer': layer, 'sublayer': 'mlp', 'token': token, 'head': -1},\n",
    "                       {'layer': None, 'sublayer': 'dense', 'token': None, 'head': None}]\n",
    "            clean, dirty = path_patching_per_token(model, embeddings_pos, embeddings_neg, new_path, only_patch_ov=True, no_ln_f=no_ln_f)\n",
    "            contrib = (range_normal @ clean[0,-1,:] - range_normal @ dirty[0,-1,:]).item()\n",
    "            cur_contribs_list.append({'path': new_path, 'contrib': contrib})\n",
    "    \n",
    "    cur_contribs_list = list(sorted(cur_contribs_list, key=lambda x: x['contrib'], reverse=(not least_attrib)))\n",
    "    for contrib in cur_contribs_list:\n",
    "        print_path(list(reversed(contrib['path'])))\n",
    "        print(\":\", contrib['contrib'])\n",
    "    \n",
    "    return cur_contribs_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0711b22a-cfe2-4d08-869e-509d22293480",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0085788f-7ab6-42b9-8752-01aa7ed3221e",
   "metadata": {},
   "source": [
    "## Model setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7878740e-c72d-4bfb-80ef-469c6df9d10c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig\n",
    "\n",
    "model_str = \"EleutherAI/gpt-neo-1.3b\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_str)\n",
    "config = AutoConfig.from_pretrained(model_str, output_hidden_states=True, output_attentions=True, low_cpu_mem_usage=True)\n",
    "model = AutoModelForCausalLM.from_pretrained(model_str, config=config, low_cpu_mem_usage=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "42233a43-1b80-4f3e-85b0-8f0909892f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46927680-393e-467b-8bdf-20e611ec9f87",
   "metadata": {},
   "source": [
    "## Load the Pile dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "dd4afa07-ac7d-4012-9a84-a8fadb2dc2d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_prompt_and_tokens(prompts, prompt_idx, mid_token, window_size=5, tokenized=True):\n",
    "    prompt = prompts[prompt_idx]\n",
    "    if not tokenized:\n",
    "        prompt = tokenizer(prompt, return_tensors='pt')\n",
    "    prompt_tokens = prompt['input_ids']\n",
    "    print(tokenizer.batch_decode(prompt_tokens[:,max(0,mid_token-window_size):mid_token+window_size]))\n",
    "    print(tokenizer.batch_decode(prompt_tokens[:,mid_token]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e341d2cf-3d2b-4fe7-aa71-8799d09b5fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "pile10k = load_dataset(\"NeelNanda/pile-10k\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "a2d8c1eb-14ba-4b38-9da7-561c208e4d4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (3180 > 2048). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "tokenized_prompts = []\n",
    "for prompt in pile10k['train']['text']:\n",
    "    tokenized_prompts.append(tokenizer(prompt, return_tensors=\"pt\"))\n",
    "tokenized_prompts = slice_tokenized_prompts_into_context_windows(tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "aa00ff2f-84e7-4a34-84e8-24e7e5c51268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "823"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokenized_prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "6846c0c5-3315-4ed2-b825-7e06a562f55c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "538"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_num_tensors()\n",
    "do_free()\n",
    "get_num_tensors()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64d1b18-93b3-4d2b-92fe-d5c4a2e47f91",
   "metadata": {},
   "source": [
    "# Gendered pronouns prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137437ee-9fd8-480c-9999-fdb3e66ad498",
   "metadata": {},
   "source": [
    "## Dataset setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1c9dfa-d6c9-4e7c-94a5-effa6ed0f4d1",
   "metadata": {},
   "source": [
    "### Get names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "acd93ee2-2490-459e-9922-445952b5bbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "male_names = []\n",
    "female_names = []\n",
    "\n",
    "# Gender by Name. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C55G7X.\n",
    "# get top 100 names of each gender from dataset that are one token long\n",
    "with open('name_gender_dataset.csv', newline='') as fp:\n",
    "    reader = csv.reader(fp, delimiter=',')\n",
    "    next(reader)\n",
    "    for row in reader:\n",
    "        name_with_space = ' ' + row[0]\n",
    "        gender = row[1]\n",
    "        name_tokens = tokenizer(name_with_space)['input_ids']\n",
    "        if len(name_tokens) != 1: continue\n",
    "        if gender == 'M' and len(male_names) < 100: male_names.append(row[0])\n",
    "        elif gender == 'F' and len(female_names) < 100: female_names.append(row[0])\n",
    "        if len(male_names) >= 100 and len(female_names) >= 100: break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883676f9-8819-4e68-ac6a-69ff223d1ebb",
   "metadata": {},
   "source": [
    "### Make prompts\n",
    "\n",
    "The \"subject pronoun\" prompts are largely inspired by Mathwin et al. 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eb2b0e2a-e93e-4144-a2b9-a2d79c5b3c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "subj_prompt_templates = [\n",
    "    \"<|endoftext|>So, [NAME] really is a great friend, isn't\",\n",
    "    \"<|endoftext|>Man, [NAME] is so funny, isn't\",\n",
    "    \"<|endoftext|>Really, [NAME] always works so hard, doesn't\"\n",
    "]\n",
    "\n",
    "subj_prompts_male = []\n",
    "for template in subj_prompt_templates:\n",
    "    for name in male_names:\n",
    "        subj_prompts_male.append(template.replace(\"[NAME]\", name))\n",
    "subj_prompts_female = []\n",
    "for template in subj_prompt_templates:\n",
    "    for name in female_names:\n",
    "        subj_prompts_female.append(template.replace(\"[NAME]\", name))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b63568-f0b3-46c1-9aa9-10105101dc6d",
   "metadata": {},
   "source": [
    "## Get embeddings\n",
    "\n",
    "We'll be using embeddings to propagate our feature vectors through nonlinearities. However, we'll only use the embeddings from a single prompt of each gender (for both the occupation prediction and pronoun prediction observables)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d2b3efbf-52df-4ca6-a0d5-315422c75158",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tokens = [' she']\n",
    "neg_tokens = [' he']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9aad3881-b8e1-4380-a235-d4d4a2551707",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "embeddings_A = embeddings_from_prompt_list(model, [subj_prompts_female[0]])\n",
    "embeddings_B = embeddings_from_prompt_list(model, [subj_prompts_male[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5bcd097c-678c-4862-a47a-0200a2c7ccbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable = make_lm_head_normal(pos_tokens, neg_tokens).detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7e544f12-6221-4cad-9eef-c6436126a2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.8895926, -4.6309958)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_A[0]['lm_out'][0,-1] @ observable, embeddings_B[0]['lm_out'][0,-1] @ observable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c062dd12-971d-4f09-ad57-fbdb1cc23a60",
   "metadata": {},
   "source": [
    "## Propagate observable through `ln_f`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "60315fcc-8eea-487e-bf4b-3af09f683536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([50257])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2211494/72145989.py:29: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at /opt/conda/conda-bld/pytorch_1682343997789/work/aten/src/ATen/native/TensorShape.cpp:3571.)\n",
      "  n = n.T.to(\"cuda\", dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "range_normal = make_range_normal(pos_tokens, neg_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "228473b6-7e54-4c49-ace7-fb7b42f093fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros = torch.zeros(range_normal.shape).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "352d8430-f2cd-434c-8be1-cbdf44fe4997",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_f_x0 = torch.from_numpy(embeddings_A[0]['ln_f_in'][:, -1, :]).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "63c5252b-2797-4786-aff6-dde9d26aeb10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 3.068734955036234e-08\n",
      "9.999999999999998e-15\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "ln_f_normal, ln_f_affine = get_normal_and_affine(ln_f_x0[0], model.transformer.ln_f, range_normal,\n",
    "                                     zeros, lr=0.02, eps=0.0000001**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda77260-d9d6-43cc-8ec8-97ee4288fe94",
   "metadata": {},
   "source": [
    "## Evaluate feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5488c2e2-a1a6-4251-af65-cefed14759ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_embeddings_A = embeddings_np_to_torch(embeddings_A[0], free=False)\n",
    "cur_embeddings_B = embeddings_np_to_torch(embeddings_B[0], free=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d8df96f3-fec0-4887-bba6-553fa707935e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subj_feature = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 13, 'sublayer': 'attn', 'token': 3, 'head': 11},\n",
    "        {'layer': 9, 'sublayer': 'attn', 'token': 3, 'head': 1},\n",
    "        {'layer': 6, 'sublayer': 'attn', 'token': 3, 'head': 6},\n",
    "    ], use_ln=False, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b6457270-0ad1-4a73-a090-822f6b0e8cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2048])\n",
      "torch.Size([2048])\n",
      "torch.Size([2048])\n"
     ]
    }
   ],
   "source": [
    "subj_feature_real = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 13, 'sublayer': 'attn', 'token': 3, 'head': 11},\n",
    "        {'layer': 9, 'sublayer': 'attn', 'token': 3, 'head': 1},\n",
    "        {'layer': 6, 'sublayer': 'attn', 'token': 3, 'head': 6},\n",
    "    ], use_ln=True, use_ln_real=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "31c76154-df47-41cf-91ce-82bafc2424a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9977936148643494"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cossim(subj_feature, subj_feature_real).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ab2457f7-b7ab-4cc5-a550-e6be0fe74029",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 300/300 [04:13<00:00,  1.18it/s]\n",
      "100%|██████████| 300/300 [04:08<00:00,  1.21it/s]\n"
     ]
    }
   ],
   "source": [
    "subj_scores_male = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {'normal': subj_feature.detach().cpu().numpy(), 'layer': 1, 'sublayer': 'ln_2_in', 'token': 3, 'name': 'attn6.6'},\n",
    "    {'normal': observable, 'layer': None, 'sublayer': 'lm_out', 'token': -1, 'name': 'subj observable'},\n",
    "], subj_prompts_male, save_out=None)\n",
    "\n",
    "subj_scores_female = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {'normal': subj_feature.detach().cpu().numpy(), 'layer': 1, 'sublayer': 'ln_2_in', 'token': 3, 'name': 'attn6.6'},\n",
    "    {'normal': observable, 'layer': None, 'sublayer': 'lm_out', 'token': -1, 'name': 'subj observable'},\n",
    "], subj_prompts_female, save_out=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "08e961dc-859e-48d5-a29b-43f95322a6c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b06e6660-943a-4014-9767-85aeb97aa3eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1519928f1370>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = subj_scores_male[0]['scores'] + subj_scores_female[0]['scores']\n",
    "ys = subj_scores_male[-1]['scores'] + subj_scores_female[-1]['scores']\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    xs, ys\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(subj_scores_male[0]['scores'][:100], subj_scores_male[-1]['scores'][:100])\n",
    "ax.scatter(subj_scores_female[0]['scores'][:100], subj_scores_female[-1]['scores'][:100])\n",
    "\n",
    "ax.scatter(subj_scores_male[0]['scores'][100:200], subj_scores_male[-1]['scores'][100:200])\n",
    "ax.scatter(subj_scores_female[0]['scores'][100:200], subj_scores_female[-1]['scores'][100:200])\n",
    "\n",
    "ax.scatter(subj_scores_male[0]['scores'][200:300], subj_scores_male[-1]['scores'][200:300])\n",
    "ax.scatter(subj_scores_female[0]['scores'][200:300], subj_scores_female[-1]['scores'][200:300])\n",
    "\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "ax.plot([xmin, xmax], intercept + np.array([xmin, xmax])*slope, label=f'Best fit $r^2 = {r_value**2:.3f}$')\n",
    "plt.xlabel(\"Score for normalized attn6.6 feature vector\")\n",
    "plt.ylabel(\"Language model output score for $n_{subj}$\")\n",
    "plt.title(\"$n_{subj}$ score versus attn6.6 feature vector score\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0833a76-aa02-4907-97df-e1ad9bcd18dd",
   "metadata": {},
   "source": [
    "## Get baseline feature vector (linear regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5952e43f-35e6-4cb2-886f-2b233fedc085",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_embeddings = embeddings_from_prompt_list2(model, subj_prompts_male, 6, 'ln_1_in', token=3)\n",
    "female_embeddings = embeddings_from_prompt_list2(model, subj_prompts_female, 6, 'ln_1_in', token=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "066b3b36-d260-4052-ae50-1ff5a60e7129",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model\n",
    "linear_model = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7ea5ac95-b9c7-46d6-b606-a054a020e332",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 30\n",
    "xs = np.concatenate([female_embeddings[:train_len], male_embeddings[:train_len]])\n",
    "ys = np.concatenate([subj_scores_female[-1]['scores'][:train_len], subj_scores_male[-1]['scores'][:train_len]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b2ab6a49-8f5a-4b3e-a674-d04449581d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.fit(xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f96e874f-57d9-434c-989a-3ad8f83112b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_male_scores = male_embeddings[train_len:] @ linear_model.coef_\n",
    "linear_female_scores = female_embeddings[train_len:] @ linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "97545c6e-2795-434a-a5cc-ecab4897329a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([linear_male_scores, linear_female_scores]),\n",
    "    np.concatenate([subj_scores_male[-1]['scores'][train_len:], subj_scores_female[-1]['scores'][train_len:]])\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(linear_male_scores,\n",
    "            subj_scores_male[-1]['scores'][train_len:], label='Male pronouns')\n",
    "ax.scatter(linear_female_scores,\n",
    "            subj_scores_female[-1]['scores'][train_len:], label='Female pronouns')\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "plt.plot([xmin, xmax], np.array([xmin, xmax])*slope + intercept, label=f'Best fit $r^2 = {r_value**2:.3g}$', color='black')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel(\"Score for linear regression feature vector\")\n",
    "plt.ylabel(\"Language model output score for $n_{subj}$\")\n",
    "plt.title(\"$n_{subj}$ score versus linear regression feature vector score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "09fc8061-6f16-4b8c-8331-b5ed56f5d349",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.2222, device='cuda:0')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cossim(torch.from_numpy(linear_model.coef_).to(\"cuda\"), subj_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd9d034-bcb5-4796-9658-59aef900a248",
   "metadata": {},
   "source": [
    "## Get baseline feature vector (mean difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "147ac53d-3234-4319-85a8-f8c82aa07012",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 30\n",
    "mean_male = np.mean(male_embeddings[:train_len], axis=0)\n",
    "mean_female = np.mean(female_embeddings[:train_len], axis=0)\n",
    "mean_diff = mean_female - mean_male\n",
    "\n",
    "meanvec_male_scores = male_embeddings[train_len:] @ mean_diff\n",
    "meanvec_female_scores = female_embeddings[train_len:] @ mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "e84e0ed0-b084-4670-ab19-9bedcf9dba84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8989503213271111"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([meanvec_male_scores, meanvec_female_scores]),\n",
    "    np.concatenate([subj_scores_male[-1]['scores'][train_len:], subj_scores_female[-1]['scores'][train_len:]])\n",
    ")\n",
    "r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07c7b5e-a759-494b-a895-6c97bfe10317",
   "metadata": {},
   "source": [
    "# American political party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d06e1c89-a348-4da8-ad1a-075ad338d1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tokens = [' Democrat']\n",
    "neg_tokens = [' Republican']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d028e33-55a4-4a03-9c8b-939b266bbe6f",
   "metadata": {},
   "source": [
    "## Get embeddings\n",
    "\n",
    "We'll be using embeddings to propagate our feature vectors through nonlinearities. However, we'll only use the embeddings from a single prompt of each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eb3ff6ca-8de4-43a1-b1a3-0c1b36b8576c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "embeddings_A = embeddings_from_prompt_list(model, [\"Bernie Sanders is a\"])\n",
    "embeddings_B = embeddings_from_prompt_list(model, [\"Ben Carson is a\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fb1004f5-a5cb-4acb-b18d-a44495377114",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable = make_lm_head_normal(pos_tokens, neg_tokens).detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "79867a48-4def-4dfd-a922-260d4adee495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.9339747, -2.9852185)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_A[0]['lm_out'][0,-1] @ observable, embeddings_B[0]['lm_out'][0,-1] @ observable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c075e1-0e1c-4106-9152-efc02689b537",
   "metadata": {},
   "source": [
    "## Propagate observable through `ln_f`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "08aa436f-21b1-48d6-90a7-d5cff2c27807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([50257])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2926536/72145989.py:29: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3637.)\n",
      "  n = n.T.to(\"cuda\", dtype=torch.float32)\n"
     ]
    }
   ],
   "source": [
    "range_normal = make_range_normal(pos_tokens, neg_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0590090c-d470-4d3e-abc8-7932e6af6ef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros = torch.zeros(range_normal.shape).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "77c2527d-9ba5-401e-bf79-45ec00a309d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_f_x0 = torch.from_numpy(embeddings_A[0]['ln_f_in'][:, -1, :]).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ab38d587-66c6-4ca9-9864-2d29df3b5711",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 4.2147854628638015e-07\n",
      "9.999999999999998e-15\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "ln_f_normal, ln_f_affine = get_normal_and_affine(ln_f_x0[0], model.transformer.ln_f, range_normal,\n",
    "                                     zeros, lr=0.02, eps=0.0000001**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aab67b2-b240-4017-a2fb-01b1f9d319dc",
   "metadata": {},
   "source": [
    "## Finding feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "40e1dea9-d213-4bb2-9afc-44ee87756bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_embeddings_A = embeddings_np_to_torch(embeddings_A[0], free=False)\n",
    "cur_embeddings_B = embeddings_np_to_torch(embeddings_B[0], free=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fea1143-2e26-4a64-9a27-86501d6607df",
   "metadata": {},
   "source": [
    "### Path patching recursion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1323ddcc-9cef-4b09-a303-11e6c09194d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num tokens: 4\n",
      "Paths of length 1:\n",
      "===================\n",
      "attn23.1@3 <= attn15.8@1: 0.06096315383911133\n",
      "attn23.6@3 <= attn15.8@1: 0.03802061080932617\n",
      "attn23.13@3 <= attn15.8@1: 0.019914627075195312\n",
      "attn23.12@3 <= attn15.8@1: 0.015712261199951172\n",
      "attn23.8@3 <= attn15.8@1: 0.010550260543823242\n",
      "attn22.2@3 <= attn15.8@1: 0.007642984390258789\n",
      "attn20.0@3 <= attn15.8@1: 0.005660533905029297\n",
      "attn17.4@3 <= attn15.8@1: 0.004119515419006348\n",
      "attn22.11@3 <= attn15.8@1: 0.003922224044799805\n",
      "attn18.7@3 <= attn15.8@1: 0.0037611722946166992\n",
      "\n",
      "Paths of length 2:\n",
      "===================\n",
      "mlp23@3 <= attn23.6@3 <= attn15.8@1: 0.0076410770416259766\n",
      "mlp23@3 <= attn22.11@3 <= attn15.8@1: 0.007506132125854492\n",
      "mlp22@3 <= attn22.11@3 <= attn15.8@1: 0.0051190853118896484\n",
      "mlp23@3 <= attn23.12@3 <= attn15.8@1: 0.004488348960876465\n",
      "mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.00289154052734375\n",
      "mlp22@3 <= attn22.2@3 <= attn15.8@1: 0.002638101577758789\n",
      "mlp23@3 <= attn22.2@3 <= attn15.8@1: 0.0025218725204467773\n",
      "attn23.1@3 <= attn22.11@3 <= attn15.8@1: 0.00249636173248291\n",
      "attn23.1@3 <= attn22.2@3 <= attn15.8@1: 0.0023956298828125\n",
      "attn23.6@3 <= attn22.11@3 <= attn15.8@1: 0.002002239227294922\n",
      "\n",
      "Paths of length 3:\n",
      "===================\n",
      "mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.0013141632080078125\n",
      "attn23.1@3 <= mlp22@3 <= attn22.11@3 <= attn15.8@1: 0.00044989585876464844\n",
      "mlp23@3 <= mlp22@3 <= attn22.2@3 <= attn15.8@1: 0.00044357776641845703\n",
      "mlp23@3 <= attn23.6@3 <= attn22.11@3 <= attn15.8@1: 0.0003962516784667969\n",
      "mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.00033593177795410156\n",
      "mlp23@3 <= attn23.1@3 <= attn22.11@3 <= attn15.8@1: 0.0003285408020019531\n",
      "attn23.1@3 <= mlp22@3 <= attn22.2@3 <= attn15.8@1: 0.0003275871276855469\n",
      "mlp23@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.00029981136322021484\n",
      "mlp23@3 <= mlp22@3 <= attn22.11@3 <= attn15.8@1: 0.0002607107162475586\n",
      "attn23.12@3 <= mlp22@3 <= attn22.2@3 <= attn15.8@1: 0.0002028942108154297\n",
      "\n",
      "Paths of length 4:\n",
      "===================\n",
      "attn23.12@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.0001329183578491211\n",
      "attn23.1@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 7.390975952148438e-05\n",
      "attn23.6@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.935264587402344e-05\n",
      "attn23.13@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.00543212890625e-05\n",
      "attn23.5@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 2.8371810913085938e-05\n",
      "attn23.5@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 2.777576446533203e-05\n",
      "attn23.0@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 2.6702880859375e-05\n",
      "mlp23@3 <= attn23.1@3 <= mlp22@3 <= attn22.2@3 <= attn15.8@1: 2.3484230041503906e-05\n",
      "attn23.1@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 1.823902130126953e-05\n",
      "attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 1.4543533325195312e-05\n",
      "\n",
      "Paths of length 5:\n",
      "===================\n",
      "mlp23@3 <= attn23.13@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 0.0001220703125\n",
      "mlp23@3 <= attn23.6@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 1.0967254638671875e-05\n",
      "mlp23@3 <= attn23.5@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 9.5367431640625e-06\n",
      "mlp23@3 <= attn23.1@3 <= mlp22@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 3.4570693969726562e-06\n",
      "attn23.0@3 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.76837158203125e-07\n",
      "attn23.5@0 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.76837158203125e-07\n",
      "attn23.5@1 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.76837158203125e-07\n",
      "attn23.5@2 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 4.76837158203125e-07\n",
      "attn23.6@0 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 3.5762786865234375e-07\n",
      "attn23.6@1 <= attn22.11@3 <= mlp21@3 <= mlp20@3 <= attn20.0@3 <= attn15.8@1: 3.5762786865234375e-07\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_paths_reverse(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal, 5, 10,\n",
    "    starting_path=[{'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8}]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "2f7b2a49-d7d6-47d3-bb74-d68fe94b04d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23/23 [00:03<00:00,  5.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " <= mlp0@1: 1.2708847522735596\n",
      " <= attn15.8@1: 0.9303088188171387\n",
      " <= mlp12@1: 0.8102269172668457\n",
      " <= attn17.13@1: 0.7485032081604004\n",
      " <= mlp10@1: 0.6533807516098022\n",
      " <= mlp4@1: 0.5240134000778198\n",
      " <= mlp5@1: 0.5184552669525146\n",
      " <= mlp14@1: 0.5010364055633545\n",
      " <= mlp2@1: 0.491127610206604\n",
      " <= attn0.6@1: 0.4787895679473877\n",
      " <= attn0.8@1: 0.47571539878845215\n",
      " <= attn0.13@1: 0.4621565341949463\n",
      " <= attn18.6@1: 0.4133622646331787\n",
      " <= attn15.10@1: 0.4077780246734619\n",
      " <= attn0.14@1: 0.39152801036834717\n",
      " <= mlp1@1: 0.37782812118530273\n",
      " <= attn14.8@1: 0.33358919620513916\n",
      " <= attn16.3@1: 0.3100782632827759\n",
      " <= attn13.3@1: 0.25924253463745117\n",
      " <= attn13.4@1: 0.2572687864303589\n",
      " <= attn0.12@1: 0.24705982208251953\n",
      " <= attn0.9@1: 0.23817837238311768\n",
      " <= attn0.0@1: 0.21292173862457275\n",
      " <= mlp13@1: 0.21222591400146484\n",
      " <= attn20.3@1: 0.2095491886138916\n",
      " <= attn0.11@1: 0.20059669017791748\n",
      " <= mlp15@1: 0.1890195608139038\n",
      " <= attn0.4@1: 0.18078207969665527\n",
      " <= attn2.7@1: 0.16565346717834473\n",
      " <= attn0.15@1: 0.15763437747955322\n",
      " <= mlp11@1: 0.14899420738220215\n",
      " <= attn17.10@1: 0.1368551254272461\n",
      " <= mlp17@1: 0.133050799369812\n",
      " <= attn8.3@1: 0.12635087966918945\n",
      " <= attn7.7@1: 0.12304937839508057\n",
      " <= attn15.7@1: 0.12225842475891113\n",
      " <= attn6.7@1: 0.11693775653839111\n",
      " <= mlp16@1: 0.11151373386383057\n",
      " <= attn16.2@1: 0.11043095588684082\n",
      " <= mlp8@1: 0.10653543472290039\n",
      " <= attn12.1@1: 0.10189235210418701\n",
      " <= attn22.7@1: 0.08892393112182617\n",
      " <= attn15.13@1: 0.08849191665649414\n",
      " <= attn9.10@1: 0.08814787864685059\n",
      " <= attn3.1@1: 0.08551013469696045\n",
      " <= attn1.11@1: 0.08242523670196533\n",
      " <= attn4.15@1: 0.07597124576568604\n",
      " <= attn9.1@1: 0.07326257228851318\n",
      " <= attn19.7@1: 0.0711965560913086\n",
      " <= attn0.3@1: 0.06611192226409912\n",
      " <= attn14.15@1: 0.06408858299255371\n",
      " <= attn1.0@1: 0.06097412109375\n",
      " <= attn21.6@1: 0.06032252311706543\n",
      " <= attn22.11@1: 0.0572206974029541\n",
      " <= attn0.7@1: 0.05700397491455078\n",
      " <= attn6.12@1: 0.05248761177062988\n",
      " <= attn13.10@1: 0.05213963985443115\n",
      " <= attn12.4@1: 0.05075192451477051\n",
      " <= attn7.0@1: 0.05071580410003662\n",
      " <= mlp9@1: 0.049558281898498535\n",
      " <= attn12.12@1: 0.048206448554992676\n",
      " <= mlp19@1: 0.04784727096557617\n",
      " <= attn4.6@1: 0.047209739685058594\n",
      " <= attn1.8@1: 0.0463029146194458\n",
      " <= attn2.1@1: 0.04629158973693848\n",
      " <= attn6.13@1: 0.04407858848571777\n",
      " <= attn9.11@1: 0.04407858848571777\n",
      " <= attn5.11@1: 0.04362797737121582\n",
      " <= attn14.11@1: 0.043187618255615234\n",
      " <= attn6.1@1: 0.04241585731506348\n",
      " <= attn1.1@1: 0.04162287712097168\n",
      " <= attn22.10@1: 0.04152989387512207\n",
      " <= attn17.7@1: 0.040041565895080566\n",
      " <= attn22.14@1: 0.03995013236999512\n",
      " <= attn0.10@1: 0.039536476135253906\n",
      " <= mlp6@1: 0.03904461860656738\n",
      " <= attn8.9@1: 0.03845858573913574\n",
      " <= attn7.8@1: 0.03683269023895264\n",
      " <= attn3.11@1: 0.03610682487487793\n",
      " <= attn17.5@1: 0.035059332847595215\n",
      " <= attn5.4@1: 0.0340954065322876\n",
      " <= attn8.14@1: 0.033988118171691895\n",
      " <= attn7.13@1: 0.03343307971954346\n",
      " <= attn5.15@1: 0.03265345096588135\n",
      " <= attn17.11@1: 0.031989336013793945\n",
      " <= attn18.14@1: 0.031403303146362305\n",
      " <= attn4.1@1: 0.030614495277404785\n",
      " <= attn3.8@1: 0.029198408126831055\n",
      " <= attn2.0@1: 0.02892482280731201\n",
      " <= attn12.11@1: 0.02878713607788086\n",
      " <= attn1.9@1: 0.02869129180908203\n",
      " <= mlp21@1: 0.028494596481323242\n",
      " <= attn2.3@1: 0.027367591857910156\n",
      " <= attn15.3@1: 0.02690887451171875\n",
      " <= attn17.14@1: 0.026386380195617676\n",
      " <= attn9.15@1: 0.02619338035583496\n",
      " <= attn5.9@1: 0.026082158088684082\n",
      " <= attn20.11@1: 0.02582693099975586\n",
      " <= attn12.0@1: 0.024829864501953125\n",
      " <= attn6.3@1: 0.024714350700378418\n",
      " <= attn9.0@1: 0.024562358856201172\n",
      " <= attn3.9@1: 0.02433300018310547\n",
      " <= attn1.14@1: 0.02403271198272705\n",
      " <= attn1.6@1: 0.023966431617736816\n",
      " <= attn14.12@1: 0.02389049530029297\n",
      " <= attn4.9@1: 0.023781418800354004\n",
      " <= attn9.2@1: 0.023688077926635742\n",
      " <= mlp20@1: 0.023317456245422363\n",
      " <= attn7.3@1: 0.022933483123779297\n",
      " <= attn7.11@1: 0.02285027503967285\n",
      " <= attn4.7@1: 0.02284526824951172\n",
      " <= attn5.8@1: 0.0221710205078125\n",
      " <= attn11.1@1: 0.021866917610168457\n",
      " <= attn7.2@1: 0.021659374237060547\n",
      " <= attn4.14@1: 0.021633148193359375\n",
      " <= attn1.15@1: 0.021604537963867188\n",
      " <= attn2.4@1: 0.021533489227294922\n",
      " <= attn9.12@1: 0.021483182907104492\n",
      " <= attn9.14@1: 0.021352171897888184\n",
      " <= attn12.13@1: 0.02121150493621826\n",
      " <= attn12.8@1: 0.02115941047668457\n",
      " <= attn4.3@1: 0.021136999130249023\n",
      " <= attn12.10@1: 0.02103114128112793\n",
      " <= attn12.5@1: 0.021025657653808594\n",
      " <= attn2.15@1: 0.020975351333618164\n",
      " <= attn16.0@1: 0.020934104919433594\n",
      " <= attn12.2@1: 0.0209197998046875\n",
      " <= attn2.9@1: 0.020309925079345703\n",
      " <= attn11.9@1: 0.02006542682647705\n",
      " <= attn0.2@1: 0.020015478134155273\n",
      " <= attn5.1@1: 0.0198667049407959\n",
      " <= attn2.5@1: 0.019677400588989258\n",
      " <= attn11.8@1: 0.019658565521240234\n",
      " <= attn0.1@1: 0.019624948501586914\n",
      " <= attn13.14@1: 0.019600868225097656\n",
      " <= attn2.13@1: 0.01952207088470459\n",
      " <= attn11.3@1: 0.019471049308776855\n",
      " <= attn11.10@1: 0.01946091651916504\n",
      " <= attn6.6@1: 0.01931929588317871\n",
      " <= attn3.4@1: 0.019237637519836426\n",
      " <= attn7.15@1: 0.01904749870300293\n",
      " <= attn4.13@1: 0.019026756286621094\n",
      " <= attn6.10@1: 0.018953800201416016\n",
      " <= attn4.5@1: 0.018944740295410156\n",
      " <= attn6.5@1: 0.01891005039215088\n",
      " <= attn4.8@1: 0.018881559371948242\n",
      " <= attn13.8@1: 0.01888108253479004\n",
      " <= attn13.11@1: 0.018769025802612305\n",
      " <= attn6.0@1: 0.01873469352722168\n",
      " <= attn7.14@1: 0.01862919330596924\n",
      " <= attn11.7@1: 0.01830267906188965\n",
      " <= attn13.15@1: 0.018137097358703613\n",
      " <= attn18.1@1: 0.018085718154907227\n",
      " <= attn7.10@1: 0.01764822006225586\n",
      " <= attn21.13@1: 0.0175856351852417\n",
      " <= attn7.1@1: 0.017544865608215332\n",
      " <= attn5.0@1: 0.017205357551574707\n",
      " <= attn9.5@1: 0.017203807830810547\n",
      " <= attn6.15@1: 0.017135143280029297\n",
      " <= attn21.2@1: 0.017135143280029297\n",
      " <= attn16.5@1: 0.01680731773376465\n",
      " <= attn11.13@1: 0.016587257385253906\n",
      " <= attn4.12@1: 0.016483306884765625\n",
      " <= attn13.9@1: 0.016404032707214355\n",
      " <= attn12.3@1: 0.016397714614868164\n",
      " <= attn16.9@1: 0.016349315643310547\n",
      " <= attn18.12@1: 0.01631474494934082\n",
      " <= attn11.0@1: 0.016282320022583008\n",
      " <= attn11.15@1: 0.016207218170166016\n",
      " <= attn3.6@1: 0.016086578369140625\n",
      " <= attn2.10@1: 0.016027092933654785\n",
      " <= attn21.15@1: 0.015999555587768555\n",
      " <= attn21.10@1: 0.0159224271774292\n",
      " <= attn21.7@1: 0.015774011611938477\n",
      " <= attn9.7@1: 0.015752673149108887\n",
      " <= attn18.9@1: 0.01570916175842285\n",
      " <= attn11.4@1: 0.015568137168884277\n",
      " <= attn6.2@1: 0.01549994945526123\n",
      " <= attn21.3@1: 0.015358209609985352\n",
      " <= attn7.4@1: 0.015246868133544922\n",
      " <= attn12.6@1: 0.015217423439025879\n",
      " <= attn12.14@1: 0.0151824951171875\n",
      " <= attn21.11@1: 0.015132665634155273\n",
      " <= attn21.9@1: 0.015048742294311523\n",
      " <= attn21.8@1: 0.015018224716186523\n",
      " <= attn6.14@1: 0.0149383544921875\n",
      " <= attn3.14@1: 0.01489555835723877\n",
      " <= attn4.4@1: 0.014869213104248047\n",
      " <= attn5.10@1: 0.014847278594970703\n",
      " <= attn21.5@1: 0.014716386795043945\n",
      " <= attn7.9@1: 0.014620780944824219\n",
      " <= attn11.11@1: 0.014594554901123047\n",
      " <= attn7.5@1: 0.014577150344848633\n",
      " <= attn21.0@1: 0.014577150344848633\n",
      " <= attn13.6@1: 0.014338970184326172\n",
      " <= attn9.6@1: 0.014133810997009277\n",
      " <= attn19.6@1: 0.013980507850646973\n",
      " <= attn3.15@1: 0.013860583305358887\n",
      " <= attn15.9@1: 0.013668537139892578\n",
      " <= attn10.3@1: 0.013456463813781738\n",
      " <= attn18.7@1: 0.013369083404541016\n",
      " <= attn8.4@1: 0.013195991516113281\n",
      " <= attn11.14@1: 0.013182878494262695\n",
      " <= attn20.6@1: 0.013171195983886719\n",
      " <= attn20.10@1: 0.013155579566955566\n",
      " <= attn20.4@1: 0.012610673904418945\n",
      " <= attn20.14@1: 0.012558460235595703\n",
      " <= attn20.15@1: 0.01253652572631836\n",
      " <= attn19.8@1: 0.012370467185974121\n",
      " <= attn20.12@1: 0.01228475570678711\n",
      " <= attn2.14@1: 0.01225888729095459\n",
      " <= attn20.1@1: 0.012228727340698242\n",
      " <= attn20.0@1: 0.012219667434692383\n",
      " <= attn16.11@1: 0.012133479118347168\n",
      " <= attn11.2@1: 0.01212930679321289\n",
      " <= attn20.5@1: 0.012099504470825195\n",
      " <= attn3.0@1: 0.012052297592163086\n",
      " <= attn20.9@1: 0.012025237083435059\n",
      " <= attn3.3@1: 0.011957645416259766\n",
      " <= attn20.13@1: 0.011955976486206055\n",
      " <= attn19.4@1: 0.011934518814086914\n",
      " <= attn17.2@1: 0.011876463890075684\n",
      " <= attn19.5@1: 0.0117872953414917\n",
      " <= mlp22@1: 0.011760830879211426\n",
      " <= attn16.10@1: 0.01174306869506836\n",
      " <= attn20.2@1: 0.011713147163391113\n",
      " <= attn5.6@1: 0.011691093444824219\n",
      " <= attn22.3@1: 0.011534690856933594\n",
      " <= attn13.5@1: 0.01148843765258789\n",
      " <= attn19.10@1: 0.011426806449890137\n",
      " <= attn19.14@1: 0.01137077808380127\n",
      " <= attn19.11@1: 0.011323213577270508\n",
      " <= attn19.9@1: 0.011318206787109375\n",
      " <= attn16.1@1: 0.011278629302978516\n",
      " <= attn20.8@1: 0.011250853538513184\n",
      " <= attn22.0@1: 0.011201143264770508\n",
      " <= attn19.3@1: 0.011195659637451172\n",
      " <= attn16.15@1: 0.010964632034301758\n",
      " <= attn19.15@1: 0.010934591293334961\n",
      " <= attn17.1@1: 0.010822772979736328\n",
      " <= attn16.13@1: 0.010753393173217773\n",
      " <= attn5.13@1: 0.010648012161254883\n",
      " <= attn16.7@1: 0.01049816608428955\n",
      " <= attn14.9@1: 0.010329961776733398\n",
      " <= attn16.8@1: 0.010207056999206543\n",
      " <= attn22.2@1: 0.010125398635864258\n",
      " <= attn19.1@1: 0.010067462921142578\n",
      " <= attn16.6@1: 0.01001882553100586\n",
      " <= attn16.12@1: 0.009877443313598633\n",
      " <= attn14.14@1: 0.00967860221862793\n",
      " <= attn14.5@1: 0.009585380554199219\n",
      " <= attn3.2@1: 0.009464383125305176\n",
      " <= attn4.11@1: 0.00940561294555664\n",
      " <= attn21.1@1: 0.009381532669067383\n",
      " <= attn14.6@1: 0.009378433227539062\n",
      " <= attn15.12@1: 0.009252667427062988\n",
      " <= attn14.13@1: 0.009188652038574219\n",
      " <= attn11.6@1: 0.009142279624938965\n",
      " <= attn10.7@1: 0.009117364883422852\n",
      " <= attn22.5@1: 0.009049773216247559\n",
      " <= attn18.5@1: 0.008904695510864258\n",
      " <= attn22.9@1: 0.00876617431640625\n",
      " <= attn10.2@1: 0.00869297981262207\n",
      " <= attn18.4@1: 0.008635282516479492\n",
      " <= attn14.7@1: 0.00860285758972168\n",
      " <= attn22.6@1: 0.008523702621459961\n",
      " <= attn10.14@1: 0.008479714393615723\n",
      " <= attn22.12@1: 0.008375167846679688\n",
      " <= attn14.10@1: 0.008308172225952148\n",
      " <= attn10.9@1: 0.00821983814239502\n",
      " <= attn10.11@1: 0.008175492286682129\n",
      " <= attn18.3@1: 0.008167743682861328\n",
      " <= attn17.12@1: 0.008166074752807617\n",
      " <= attn10.0@1: 0.008094549179077148\n",
      " <= attn10.1@1: 0.008039474487304688\n",
      " <= attn10.10@1: 0.00788426399230957\n",
      " <= attn17.3@1: 0.007873296737670898\n",
      " <= attn7.6@1: 0.007853507995605469\n",
      " <= attn22.15@1: 0.007835865020751953\n",
      " <= attn18.2@1: 0.0074759721755981445\n",
      " <= attn18.10@1: 0.007431387901306152\n",
      " <= attn13.12@1: 0.007398247718811035\n",
      " <= attn17.9@1: 0.00731205940246582\n",
      " <= attn18.15@1: 0.0073089599609375\n",
      " <= attn18.8@1: 0.007252812385559082\n",
      " <= attn22.13@1: 0.007222175598144531\n",
      " <= attn17.0@1: 0.007176518440246582\n",
      " <= attn5.7@1: 0.007017254829406738\n",
      " <= attn3.10@1: 0.007015228271484375\n",
      " <= attn10.15@1: 0.006869077682495117\n",
      " <= attn13.0@1: 0.00686192512512207\n",
      " <= attn22.1@1: 0.006851315498352051\n",
      " <= attn18.0@1: 0.006582140922546387\n",
      " <= attn16.14@1: 0.006484270095825195\n",
      " <= attn18.11@1: 0.006258487701416016\n",
      " <= attn8.1@1: 0.006226181983947754\n",
      " <= attn13.13@1: 0.0059539079666137695\n",
      " <= attn15.15@1: 0.005881905555725098\n",
      " <= attn8.10@1: 0.00569462776184082\n",
      " <= attn8.11@1: 0.005687236785888672\n",
      " <= attn8.7@1: 0.005546092987060547\n",
      " <= attn17.15@1: 0.005485177040100098\n",
      " <= attn11.5@1: 0.005446791648864746\n",
      " <= attn8.15@1: 0.005437612533569336\n",
      " <= attn2.12@1: 0.005436420440673828\n",
      " <= attn13.7@1: 0.005410194396972656\n",
      " <= attn19.12@1: 0.005374431610107422\n",
      " <= attn15.4@1: 0.005293488502502441\n",
      " <= attn8.12@1: 0.005262613296508789\n",
      " <= attn1.12@1: 0.005162715911865234\n",
      " <= attn5.14@1: 0.004907846450805664\n",
      " <= attn18.13@1: 0.0048950910568237305\n",
      " <= attn10.4@1: 0.0047969818115234375\n",
      " <= attn12.15@1: 0.004639148712158203\n",
      " <= attn5.12@1: 0.0043572187423706055\n",
      " <= attn10.13@1: 0.0043146610260009766\n",
      " <= attn12.7@1: 0.0038950443267822266\n",
      " <= attn9.13@1: 0.0031741857528686523\n",
      " <= attn15.6@1: 0.0031315088272094727\n",
      " <= attn10.8@1: 0.002920866012573242\n",
      " <= attn14.4@1: 0.002564668655395508\n",
      " <= attn19.13@1: 0.0025010108947753906\n",
      " <= attn15.5@1: 0.0024504661560058594\n",
      " <= attn10.12@1: 0.0024340152740478516\n",
      " <= attn5.5@1: 0.0023674964904785156\n",
      " <= attn15.2@1: 0.001882314682006836\n",
      " <= attn8.6@1: 0.001581430435180664\n",
      " <= attn8.8@1: 0.0015445947647094727\n",
      " <= attn19.0@1: 0.0013210773468017578\n",
      " <= attn8.13@1: 0.0013060569763183594\n",
      " <= attn21.12@1: 0.0011712312698364258\n",
      " <= attn5.2@1: 0.0011475086212158203\n",
      " <= attn9.8@1: 0.0011096000671386719\n",
      " <= attn1.2@1: -2.2411346435546875e-05\n",
      " <= attn9.4@1: -0.00010943412780761719\n",
      " <= attn21.14@1: -0.00012969970703125\n",
      " <= attn14.1@1: -0.0002453327178955078\n",
      " <= attn14.2@1: -0.0003523826599121094\n",
      " <= attn13.2@1: -0.0009499788284301758\n",
      " <= mlp3@1: -0.0013401508331298828\n",
      " <= attn22.8@1: -0.00176239013671875\n",
      " <= attn8.2@1: -0.0025273561477661133\n",
      " <= attn2.2@1: -0.003106355667114258\n",
      " <= attn10.6@1: -0.0031232833862304688\n",
      " <= attn1.4@1: -0.003634333610534668\n",
      " <= attn15.0@1: -0.00394749641418457\n",
      " <= attn5.3@1: -0.003973841667175293\n",
      " <= attn12.9@1: -0.0040018558502197266\n",
      " <= attn6.9@1: -0.004397869110107422\n",
      " <= attn1.10@1: -0.005247592926025391\n",
      " <= attn17.4@1: -0.005972862243652344\n",
      " <= attn1.5@1: -0.0077702999114990234\n",
      " <= attn10.5@1: -0.008058309555053711\n",
      " <= attn2.6@1: -0.008095026016235352\n",
      " <= attn17.6@1: -0.008219599723815918\n",
      " <= attn1.7@1: -0.008264541625976562\n",
      " <= attn0.5@1: -0.008472681045532227\n",
      " <= attn15.1@1: -0.009553194046020508\n",
      " <= attn3.5@1: -0.009827136993408203\n",
      " <= attn15.11@1: -0.010653972625732422\n",
      " <= attn8.0@1: -0.010864734649658203\n",
      " <= attn7.12@1: -0.011191844940185547\n",
      " <= attn9.3@1: -0.017413854598999023\n",
      " <= attn21.4@1: -0.018065571784973145\n",
      " <= attn15.14@1: -0.018510818481445312\n",
      " <= attn8.5@1: -0.019280672073364258\n",
      " <= attn3.13@1: -0.023917794227600098\n",
      " <= attn4.0@1: -0.026250720024108887\n",
      " <= attn2.8@1: -0.029169678688049316\n",
      " <= attn14.3@1: -0.034307122230529785\n",
      " <= attn11.12@1: -0.03438282012939453\n",
      " <= attn6.4@1: -0.0428922176361084\n",
      " <= attn3.7@1: -0.04294991493225098\n",
      " <= attn1.3@1: -0.04349815845489502\n",
      " <= attn2.11@1: -0.04581403732299805\n",
      " <= attn3.12@1: -0.0466923713684082\n",
      " <= attn9.9@1: -0.047360897064208984\n",
      " <= attn4.10@1: -0.048496246337890625\n",
      " <= mlp18@1: -0.04938840866088867\n",
      " <= attn13.1@1: -0.05540776252746582\n",
      " <= attn22.4@1: -0.057327985763549805\n",
      " <= attn20.7@1: -0.05993318557739258\n",
      " <= attn14.0@1: -0.06309962272644043\n",
      " <= attn1.13@1: -0.06332588195800781\n",
      " <= attn16.4@1: -0.08215093612670898\n",
      " <= attn4.2@1: -0.08246231079101562\n",
      " <= attn6.8@1: -0.08437681198120117\n",
      " <= mlp7@1: -0.11691570281982422\n",
      " <= attn17.8@1: -0.11770272254943848\n",
      " <= attn19.2@1: -0.12253308296203613\n",
      " <= attn6.11@1: -0.13167715072631836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_dense(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal, tokens_list=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "f8aa1a8e-2150-4faf-b6b5-3a043325726c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23/23 [00:03<00:00,  5.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " <= attn15.8@1: 0.9589762687683105\n",
      " <= mlp0@1: 0.868404746055603\n",
      " <= attn17.13@1: 0.7230846881866455\n",
      " <= mlp12@1: 0.6755231618881226\n",
      " <= mlp1@1: 0.5812621116638184\n",
      " <= mlp13@1: 0.5398668050765991\n",
      " <= attn0.8@1: 0.5353808403015137\n",
      " <= mlp14@1: 0.44810569286346436\n",
      " <= mlp6@1: 0.4406622648239136\n",
      " <= mlp10@1: 0.40533876419067383\n",
      " <= attn15.10@1: 0.37952423095703125\n",
      " <= attn22.7@1: 0.3722667694091797\n",
      " <= mlp11@1: 0.3529825210571289\n",
      " <= attn0.3@1: 0.31097662448883057\n",
      " <= attn0.14@1: 0.3101928234100342\n",
      " <= attn13.3@1: 0.298439621925354\n",
      " <= attn0.6@1: 0.2552940845489502\n",
      " <= attn9.1@1: 0.2537471055984497\n",
      " <= attn13.4@1: 0.24928045272827148\n",
      " <= attn0.9@1: 0.2484976053237915\n",
      " <= attn16.3@1: 0.20409941673278809\n",
      " <= attn18.6@1: 0.19736874103546143\n",
      " <= attn0.13@1: 0.19560551643371582\n",
      " <= mlp5@1: 0.18725848197937012\n",
      " <= attn16.2@1: 0.17493915557861328\n",
      " <= attn13.10@1: 0.157118558883667\n",
      " <= attn10.5@1: 0.13760268688201904\n",
      " <= attn0.11@1: 0.13483691215515137\n",
      " <= mlp4@1: 0.1272183656692505\n",
      " <= attn14.8@1: 0.12501728534698486\n",
      " <= mlp9@1: 0.12181425094604492\n",
      " <= attn8.14@1: 0.121634840965271\n",
      " <= attn15.7@1: 0.10595166683197021\n",
      " <= attn17.7@1: 0.10256755352020264\n",
      " <= attn8.9@1: 0.09722113609313965\n",
      " <= attn0.15@1: 0.09495186805725098\n",
      " <= mlp19@1: 0.0876166820526123\n",
      " <= attn8.3@1: 0.08106756210327148\n",
      " <= mlp7@1: 0.07973289489746094\n",
      " <= attn0.12@1: 0.07913672924041748\n",
      " <= mlp2@1: 0.0784611701965332\n",
      " <= attn3.8@1: 0.062080979347229004\n",
      " <= attn4.2@1: 0.062059879302978516\n",
      " <= attn15.13@1: 0.0612412691116333\n",
      " <= attn8.2@1: 0.05865895748138428\n",
      " <= mlp20@1: 0.058527231216430664\n",
      " <= attn0.10@1: 0.05748605728149414\n",
      " <= attn13.12@1: 0.055167555809020996\n",
      " <= attn0.5@1: 0.05345916748046875\n",
      " <= mlp16@1: 0.05271625518798828\n",
      " <= attn2.7@1: 0.05259227752685547\n",
      " <= attn0.2@1: 0.05240893363952637\n",
      " <= attn5.3@1: 0.051300764083862305\n",
      " <= attn0.7@1: 0.05112886428833008\n",
      " <= attn21.6@1: 0.0497584342956543\n",
      " <= attn14.11@1: 0.04915010929107666\n",
      " <= attn1.12@1: 0.04908275604248047\n",
      " <= attn12.4@1: 0.04861295223236084\n",
      " <= attn3.0@1: 0.04767346382141113\n",
      " <= attn17.0@1: 0.04681730270385742\n",
      " <= attn5.0@1: 0.04568004608154297\n",
      " <= attn16.5@1: 0.04457271099090576\n",
      " <= attn3.12@1: 0.04413485527038574\n",
      " <= attn3.1@1: 0.04385685920715332\n",
      " <= attn22.11@1: 0.04204213619232178\n",
      " <= attn5.12@1: 0.042005062103271484\n",
      " <= attn12.0@1: 0.04178261756896973\n",
      " <= attn17.10@1: 0.041140198707580566\n",
      " <= attn22.10@1: 0.03876471519470215\n",
      " <= attn3.4@1: 0.03823554515838623\n",
      " <= attn18.9@1: 0.03742671012878418\n",
      " <= attn4.14@1: 0.03645491600036621\n",
      " <= attn11.5@1: 0.035935163497924805\n",
      " <= mlp21@1: 0.035593390464782715\n",
      " <= attn14.15@1: 0.035372257232666016\n",
      " <= attn0.0@1: 0.03482687473297119\n",
      " <= attn5.7@1: 0.03460693359375\n",
      " <= attn17.9@1: 0.03450775146484375\n",
      " <= attn4.3@1: 0.03392350673675537\n",
      " <= attn0.4@1: 0.033765196800231934\n",
      " <= attn6.8@1: 0.03241991996765137\n",
      " <= attn13.15@1: 0.0319523811340332\n",
      " <= attn1.3@1: 0.03174722194671631\n",
      " <= attn3.14@1: 0.03103315830230713\n",
      " <= attn4.11@1: 0.030363917350769043\n",
      " <= attn6.6@1: 0.029816150665283203\n",
      " <= attn11.12@1: 0.029320478439331055\n",
      " <= attn12.11@1: 0.02860438823699951\n",
      " <= attn12.6@1: 0.028505325317382812\n",
      " <= attn11.9@1: 0.028460979461669922\n",
      " <= attn5.14@1: 0.02830803394317627\n",
      " <= attn11.7@1: 0.02796781063079834\n",
      " <= attn0.1@1: 0.02751767635345459\n",
      " <= attn4.1@1: 0.02746415138244629\n",
      " <= attn4.9@1: 0.027103304862976074\n",
      " <= attn21.14@1: 0.027027368545532227\n",
      " <= attn22.14@1: 0.0267564058303833\n",
      " <= attn14.12@1: 0.026738882064819336\n",
      " <= attn6.2@1: 0.026726841926574707\n",
      " <= attn6.3@1: 0.026654958724975586\n",
      " <= attn19.7@1: 0.025838375091552734\n",
      " <= attn3.2@1: 0.02575206756591797\n",
      " <= attn17.14@1: 0.02557826042175293\n",
      " <= attn7.0@1: 0.02536773681640625\n",
      " <= attn3.6@1: 0.024271607398986816\n",
      " <= attn8.4@1: 0.024204254150390625\n",
      " <= attn10.12@1: 0.02402043342590332\n",
      " <= attn5.6@1: 0.023230433464050293\n",
      " <= mlp18@1: 0.023188471794128418\n",
      " <= attn4.7@1: 0.02289414405822754\n",
      " <= attn5.13@1: 0.02286231517791748\n",
      " <= attn13.6@1: 0.022856950759887695\n",
      " <= attn4.8@1: 0.02274036407470703\n",
      " <= attn8.15@1: 0.022591590881347656\n",
      " <= attn16.9@1: 0.022441387176513672\n",
      " <= attn20.11@1: 0.022156357765197754\n",
      " <= attn12.10@1: 0.021996736526489258\n",
      " <= attn12.8@1: 0.0219498872756958\n",
      " <= attn12.2@1: 0.021739721298217773\n",
      " <= attn11.11@1: 0.021703124046325684\n",
      " <= attn17.5@1: 0.021649837493896484\n",
      " <= attn12.5@1: 0.02163529396057129\n",
      " <= attn5.15@1: 0.02152717113494873\n",
      " <= attn1.10@1: 0.021404027938842773\n",
      " <= attn13.5@1: 0.021352529525756836\n",
      " <= attn12.13@1: 0.0207364559173584\n",
      " <= attn14.9@1: 0.020627379417419434\n",
      " <= attn3.10@1: 0.02056431770324707\n",
      " <= attn2.0@1: 0.02054572105407715\n",
      " <= attn21.12@1: 0.020392179489135742\n",
      " <= attn5.8@1: 0.019909381866455078\n",
      " <= attn13.9@1: 0.019756555557250977\n",
      " <= attn6.15@1: 0.01968073844909668\n",
      " <= attn2.13@1: 0.019668221473693848\n",
      " <= attn9.12@1: 0.019359588623046875\n",
      " <= attn18.4@1: 0.019359350204467773\n",
      " <= attn3.3@1: 0.01921534538269043\n",
      " <= attn13.8@1: 0.019125938415527344\n",
      " <= attn2.4@1: 0.01910245418548584\n",
      " <= attn14.4@1: 0.018734455108642578\n",
      " <= attn11.3@1: 0.01869487762451172\n",
      " <= attn21.4@1: 0.01839613914489746\n",
      " <= attn6.10@1: 0.01819324493408203\n",
      " <= attn4.15@1: 0.01751542091369629\n",
      " <= attn21.2@1: 0.017453432083129883\n",
      " <= attn6.4@1: 0.017240524291992188\n",
      " <= attn7.15@1: 0.017189979553222656\n",
      " <= attn5.10@1: 0.017122268676757812\n",
      " <= attn9.2@1: 0.016956567764282227\n",
      " <= attn7.8@1: 0.01691269874572754\n",
      " <= attn18.1@1: 0.016700267791748047\n",
      " <= attn19.8@1: 0.016671419143676758\n",
      " <= attn5.1@1: 0.0166548490524292\n",
      " <= attn4.5@1: 0.016492486000061035\n",
      " <= attn16.0@1: 0.016432881355285645\n",
      " <= attn19.13@1: 0.016266822814941406\n",
      " <= attn1.6@1: 0.01615917682647705\n",
      " <= attn9.8@1: 0.016080498695373535\n",
      " <= attn22.4@1: 0.01587224006652832\n",
      " <= attn2.8@1: 0.01587080955505371\n",
      " <= attn13.0@1: 0.015848875045776367\n",
      " <= attn3.5@1: 0.015831828117370605\n",
      " <= attn21.3@1: 0.015539884567260742\n",
      " <= attn19.11@1: 0.015488624572753906\n",
      " <= attn7.13@1: 0.01536405086517334\n",
      " <= attn6.0@1: 0.015310049057006836\n",
      " <= attn14.13@1: 0.015305757522583008\n",
      " <= attn2.1@1: 0.015235424041748047\n",
      " <= attn6.14@1: 0.015062689781188965\n",
      " <= attn11.14@1: 0.014818191528320312\n",
      " <= attn4.6@1: 0.01479959487915039\n",
      " <= attn21.10@1: 0.01474308967590332\n",
      " <= attn21.11@1: 0.014620661735534668\n",
      " <= attn21.15@1: 0.014544367790222168\n",
      " <= attn6.5@1: 0.014526963233947754\n",
      " <= attn1.7@1: 0.014479637145996094\n",
      " <= attn5.9@1: 0.014475345611572266\n",
      " <= attn3.9@1: 0.01430666446685791\n",
      " <= attn21.5@1: 0.01395106315612793\n",
      " <= attn13.7@1: 0.013913154602050781\n",
      " <= attn21.0@1: 0.013864278793334961\n",
      " <= attn6.1@1: 0.013770103454589844\n",
      " <= attn7.12@1: 0.013741016387939453\n",
      " <= attn3.7@1: 0.013732075691223145\n",
      " <= attn20.9@1: 0.013683199882507324\n",
      " <= attn18.0@1: 0.013606548309326172\n",
      " <= attn21.13@1: 0.013213038444519043\n",
      " <= attn21.9@1: 0.013125061988830566\n",
      " <= attn21.8@1: 0.01304006576538086\n",
      " <= attn21.1@1: 0.013034224510192871\n",
      " <= attn22.2@1: 0.012985944747924805\n",
      " <= attn20.6@1: 0.012746572494506836\n",
      " <= attn11.13@1: 0.012738585472106934\n",
      " <= attn10.10@1: 0.012709975242614746\n",
      " <= attn14.5@1: 0.012695550918579102\n",
      " <= attn11.8@1: 0.012578487396240234\n",
      " <= attn13.2@1: 0.012535333633422852\n",
      " <= attn16.6@1: 0.012456297874450684\n",
      " <= attn9.0@1: 0.012399911880493164\n",
      " <= attn16.8@1: 0.012258529663085938\n",
      " <= attn14.10@1: 0.012220382690429688\n",
      " <= attn11.10@1: 0.012186050415039062\n",
      " <= attn14.14@1: 0.012096643447875977\n",
      " <= attn1.1@1: 0.012078642845153809\n",
      " <= attn12.3@1: 0.012027382850646973\n",
      " <= attn1.11@1: 0.011908411979675293\n",
      " <= attn21.7@1: 0.011846661567687988\n",
      " <= attn10.13@1: 0.011749744415283203\n",
      " <= attn18.14@1: 0.011502265930175781\n",
      " <= attn14.1@1: 0.011472225189208984\n",
      " <= attn6.13@1: 0.011452674865722656\n",
      " <= attn11.0@1: 0.011359453201293945\n",
      " <= attn11.15@1: 0.011026740074157715\n",
      " <= attn16.15@1: 0.010950922966003418\n",
      " <= attn16.13@1: 0.01077282428741455\n",
      " <= attn16.7@1: 0.010732173919677734\n",
      " <= attn22.0@1: 0.010573267936706543\n",
      " <= attn5.11@1: 0.010566115379333496\n",
      " <= attn20.10@1: 0.010493874549865723\n",
      " <= attn7.2@1: 0.010460376739501953\n",
      " <= attn20.4@1: 0.010345458984375\n",
      " <= attn7.11@1: 0.010317087173461914\n",
      " <= attn17.2@1: 0.010304808616638184\n",
      " <= attn16.11@1: 0.01029670238494873\n",
      " <= attn18.12@1: 0.01023721694946289\n",
      " <= attn2.2@1: 0.009980082511901855\n",
      " <= attn16.14@1: 0.009952068328857422\n",
      " <= attn17.1@1: 0.009801387786865234\n",
      " <= attn14.7@1: 0.009697318077087402\n",
      " <= attn9.6@1: 0.009696722030639648\n",
      " <= attn19.3@1: 0.009577035903930664\n",
      " <= attn16.12@1: 0.009540557861328125\n",
      " <= attn18.15@1: 0.0095292329788208\n",
      " <= attn20.13@1: 0.009465575218200684\n",
      " <= attn10.15@1: 0.009446978569030762\n",
      " <= attn20.0@1: 0.00929248332977295\n",
      " <= attn20.3@1: 0.009123682975769043\n",
      " <= attn20.14@1: 0.009118914604187012\n",
      " <= attn7.1@1: 0.009009599685668945\n",
      " <= attn22.1@1: 0.00900411605834961\n",
      " <= attn7.10@1: 0.00886988639831543\n",
      " <= attn16.1@1: 0.008824348449707031\n",
      " <= attn20.5@1: 0.00882101058959961\n",
      " <= attn10.14@1: 0.008796930313110352\n",
      " <= attn10.2@1: 0.008774995803833008\n",
      " <= attn1.8@1: 0.008736014366149902\n",
      " <= attn19.5@1: 0.008733153343200684\n",
      " <= attn22.9@1: 0.008679866790771484\n",
      " <= attn15.3@1: 0.008579134941101074\n",
      " <= attn20.15@1: 0.008476495742797852\n",
      " <= attn19.10@1: 0.008444786071777344\n",
      " <= attn19.15@1: 0.008392095565795898\n",
      " <= attn16.10@1: 0.008339405059814453\n",
      " <= attn10.1@1: 0.008268356323242188\n",
      " <= attn19.9@1: 0.008249402046203613\n",
      " <= attn6.12@1: 0.008209705352783203\n",
      " <= attn17.6@1: 0.00803065299987793\n",
      " <= attn10.0@1: 0.007820844650268555\n",
      " <= attn17.3@1: 0.007814407348632812\n",
      " <= attn20.1@1: 0.007739067077636719\n",
      " <= attn3.11@1: 0.007691621780395508\n",
      " <= attn18.10@1: 0.0076825618743896484\n",
      " <= attn22.13@1: 0.0075740814208984375\n",
      " <= attn7.5@1: 0.007451534271240234\n",
      " <= attn2.10@1: 0.007344722747802734\n",
      " <= attn2.12@1: 0.007298707962036133\n",
      " <= attn15.2@1: 0.007268190383911133\n",
      " <= attn18.11@1: 0.007196903228759766\n",
      " <= attn11.1@1: 0.007045269012451172\n",
      " <= attn22.3@1: 0.007034659385681152\n",
      " <= attn10.9@1: 0.007028698921203613\n",
      " <= attn10.4@1: 0.006957650184631348\n",
      " <= attn10.7@1: 0.006930708885192871\n",
      " <= attn10.11@1: 0.006869792938232422\n",
      " <= attn13.14@1: 0.006754517555236816\n",
      " <= attn18.2@1: 0.0067424774169921875\n",
      " <= attn19.14@1: 0.006692051887512207\n",
      " <= attn22.12@1: 0.0065038204193115234\n",
      " <= attn17.15@1: 0.006425976753234863\n",
      " <= attn7.9@1: 0.006350040435791016\n",
      " <= attn20.12@1: 0.006280779838562012\n",
      " <= attn18.8@1: 0.005973100662231445\n",
      " <= attn11.4@1: 0.005936741828918457\n",
      " <= attn20.8@1: 0.005892276763916016\n",
      " <= attn3.13@1: 0.0057877302169799805\n",
      " <= attn22.15@1: 0.005746364593505859\n",
      " <= attn10.8@1: 0.005523204803466797\n",
      " <= attn18.7@1: 0.005223631858825684\n",
      " <= attn3.15@1: 0.005006909370422363\n",
      " <= attn12.14@1: 0.005005598068237305\n",
      " <= attn7.6@1: 0.004995584487915039\n",
      " <= attn10.3@1: 0.004965424537658691\n",
      " <= attn2.3@1: 0.004855632781982422\n",
      " <= attn18.13@1: 0.004748940467834473\n",
      " <= attn22.8@1: 0.004717707633972168\n",
      " <= attn8.1@1: 0.004588127136230469\n",
      " <= attn20.2@1: 0.004463911056518555\n",
      " <= attn15.5@1: 0.004261374473571777\n",
      " <= attn7.4@1: 0.004158616065979004\n",
      " <= attn15.0@1: 0.00414431095123291\n",
      " <= attn18.3@1: 0.003913760185241699\n",
      " <= attn9.5@1: 0.0038300752639770508\n",
      " <= attn15.4@1: 0.003658890724182129\n",
      " <= attn9.10@1: 0.0034863948822021484\n",
      " <= attn13.13@1: 0.0034595727920532227\n",
      " <= attn14.2@1: 0.00335538387298584\n",
      " <= attn8.12@1: 0.0029474496841430664\n",
      " <= attn4.10@1: 0.0028960704803466797\n",
      " <= attn2.15@1: 0.0028243064880371094\n",
      " <= attn19.6@1: 0.0027604103088378906\n",
      " <= attn14.6@1: 0.0026769638061523438\n",
      " <= attn19.12@1: 0.0022400617599487305\n",
      " <= attn22.5@1: 0.0021665096282958984\n",
      " <= attn7.14@1: 0.002129197120666504\n",
      " <= attn9.4@1: 0.002041339874267578\n",
      " <= attn9.7@1: 0.0019719600677490234\n",
      " <= attn8.7@1: 0.0019022226333618164\n",
      " <= attn8.6@1: 0.0016075372695922852\n",
      " <= attn12.12@1: 0.0016069412231445312\n",
      " <= attn8.10@1: 0.0014096498489379883\n",
      " <= attn19.4@1: 0.001289963722229004\n",
      " <= attn7.7@1: 0.000667572021484375\n",
      " <= attn19.0@1: 0.0003930330276489258\n",
      " <= attn1.9@1: -0.0002856254577636719\n",
      " <= attn9.9@1: -0.0007174015045166016\n",
      " <= attn17.11@1: -0.0008026361465454102\n",
      " <= attn4.12@1: -0.0012717247009277344\n",
      " <= attn11.6@1: -0.0014978647232055664\n",
      " <= attn15.14@1: -0.0015048980712890625\n",
      " <= attn18.5@1: -0.0016019344329833984\n",
      " <= attn5.2@1: -0.0019919872283935547\n",
      " <= attn2.6@1: -0.002077817916870117\n",
      " <= attn15.6@1: -0.0021173954010009766\n",
      " <= attn2.11@1: -0.0032826662063598633\n",
      " <= attn1.14@1: -0.0034797191619873047\n",
      " <= attn17.4@1: -0.003629326820373535\n",
      " <= attn13.11@1: -0.003953814506530762\n",
      " <= attn12.7@1: -0.004215121269226074\n",
      " <= attn22.6@1: -0.004831790924072266\n",
      " <= attn2.5@1: -0.0050127506256103516\n",
      " <= attn17.12@1: -0.005044341087341309\n",
      " <= attn15.15@1: -0.005091309547424316\n",
      " <= attn12.15@1: -0.005806088447570801\n",
      " <= attn6.9@1: -0.006107687950134277\n",
      " <= attn5.5@1: -0.007118105888366699\n",
      " <= attn7.3@1: -0.007915139198303223\n",
      " <= attn2.14@1: -0.008232593536376953\n",
      " <= attn8.13@1: -0.008280277252197266\n",
      " <= attn15.9@1: -0.008885741233825684\n",
      " <= attn8.8@1: -0.009128570556640625\n",
      " <= attn9.11@1: -0.009482145309448242\n",
      " <= attn12.1@1: -0.009585857391357422\n",
      " <= attn10.6@1: -0.010262012481689453\n",
      " <= attn9.3@1: -0.011536002159118652\n",
      " <= attn15.11@1: -0.011977910995483398\n",
      " <= attn2.9@1: -0.012840986251831055\n",
      " <= attn8.5@1: -0.013512730598449707\n",
      " <= attn1.5@1: -0.013652324676513672\n",
      " <= attn4.0@1: -0.014379501342773438\n",
      " <= attn9.13@1: -0.01636528968811035\n",
      " <= attn1.2@1: -0.016449332237243652\n",
      " <= attn1.4@1: -0.019271016120910645\n",
      " <= attn6.7@1: -0.01934981346130371\n",
      " <= attn4.13@1: -0.01985347270965576\n",
      " <= attn5.4@1: -0.020016908645629883\n",
      " <= attn14.3@1: -0.02076411247253418\n",
      " <= mlp22@1: -0.02200639247894287\n",
      " <= attn15.12@1: -0.022925376892089844\n",
      " <= attn4.4@1: -0.022933006286621094\n",
      " <= attn19.1@1: -0.023038744926452637\n",
      " <= mlp17@1: -0.023239850997924805\n",
      " <= attn15.1@1: -0.024889469146728516\n",
      " <= attn1.15@1: -0.025440335273742676\n",
      " <= attn16.4@1: -0.028504133224487305\n",
      " <= attn9.14@1: -0.030431389808654785\n",
      " <= attn1.0@1: -0.031708717346191406\n",
      " <= attn8.11@1: -0.031829833984375\n",
      " <= attn1.13@1: -0.04417705535888672\n",
      " <= attn13.1@1: -0.04441642761230469\n",
      " <= attn8.0@1: -0.04592573642730713\n",
      " <= attn20.7@1: -0.051308512687683105\n",
      " <= attn11.2@1: -0.0539698600769043\n",
      " <= attn6.11@1: -0.05504584312438965\n",
      " <= mlp15@1: -0.05508732795715332\n",
      " <= attn9.15@1: -0.06079745292663574\n",
      " <= attn12.9@1: -0.06190323829650879\n",
      " <= attn14.0@1: -0.07218766212463379\n",
      " <= attn19.2@1: -0.09650874137878418\n",
      " <= attn17.8@1: -0.12031078338623047\n",
      " <= mlp3@1: -0.21107745170593262\n",
      " <= mlp8@1: -0.32380902767181396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_dense(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal, tokens_list=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e2ad6ee1-876c-4d38-a8fc-641bace5f20f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2048])\n"
     ]
    }
   ],
   "source": [
    "democrat_normal_2 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_2_ln_real = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8},\n",
    "    ], use_ln=True, use_ln_real=True, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2b88044f-6b5f-4f66-b396-2bfdf8416ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9990016222000122"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cossim(democrat_normal_2, democrat_normal_2_ln_real).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "454b82b3-08e2-43b7-a4e3-1a99cb58aa2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "democrat_normal_1 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 1, 'head': 13},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_2 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_3 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 20, 'sublayer': 'attn', 'token': 1, 'head': 3},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_4 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 23, 'sublayer': 'attn', 'token': 3, 'head': 1},\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_5 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 22, 'sublayer': 'attn', 'token': 1, 'head': 7},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_6 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 18, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 16, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 13, 'sublayer': 'attn', 'token': 1, 'head': 3},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "democrat_normal_7 = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        {'layer': 23, 'sublayer': 'attn', 'token': 3, 'head': 6},\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 1, 'head': 8},\n",
    "    ], use_ln=False, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d121e5f7-d260-4655-9bb0-b925db8a63fa",
   "metadata": {},
   "source": [
    "## Make dataset\n",
    "\n",
    "Taken from https://today.yougov.com/ratings/politics/fame/Democrats/all and https://today.yougov.com/ratings/politics/fame/Republicans/all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "62173dd8-e900-41d3-9182-54d0062ac307",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "democrats_list = [\n",
    "    'Barack Obama',\n",
    "    'Joe Biden',\n",
    "    'Bernie Sanders',\n",
    "    'Bill Clinton',\n",
    "    'Hillary Clinton',\n",
    "    'Kamala Harris',\n",
    "    'Jimmy Carter',\n",
    "    'Al Gore',\n",
    "    'Nancy Pelosi',\n",
    "    'Michael Bloomberg',\n",
    "    'Elizabeth Warren',\n",
    "    'John Kerry',\n",
    "    'Alexandria Ocasio-Cortez',\n",
    "    'Andrew Cuomo',\n",
    "    'Charles Schumer',\n",
    "    'Pete Buttigieg',\n",
    "    'Dianne Feinstein',\n",
    "    'Gavin Newsom',\n",
    "    'Beto O\\'Rourke',\n",
    "    'Walter Mondale',\n",
    "    'Madeleine Albright',\n",
    "    'Cory Booker',\n",
    "    'Amy Klobuchar',\n",
    "    'Michael Dukakis',\n",
    "    'Anthony D. Weiner',\n",
    "    'Al Franken',\n",
    "    'John Edwards',\n",
    "    'Joe Manchin',\n",
    "    'Maxine Waters',\n",
    "    'Bill De Blasio',\n",
    "    'Ilhan Omar',\n",
    "    'Adam Schiff',\n",
    "    'Tulsi Gabbard',\n",
    "    'Joe Manchin III',\n",
    "    'Gabrielle Giffords',\n",
    "    'Joseph P. Kennedy III',\n",
    "    'Patrick Kennedy',\n",
    "    'Rashida Tlaib',\n",
    "    'Jay Rockefeller',\n",
    "    'Andrew Yang'\n",
    "]\n",
    "\n",
    "republicans_list = [\n",
    "    'George W. Bush',\n",
    "    'Mike Pence',\n",
    "    'Arnold Schwarzenegger',\n",
    "    'Donald Trump',\n",
    "    'Mitt Romney',\n",
    "    'Ted Cruz',\n",
    "    'Mitch McConnell',\n",
    "    'Ron DeSantis',\n",
    "    'Dick Cheney',\n",
    "    'Chris Christie',\n",
    "    'Sarah Palin',\n",
    "    'Kevn McCarthy',\n",
    "    'Jeb Bush',\n",
    "    'Bob Dole',\n",
    "    'Marco Rubio',\n",
    "    'Rand Paul',\n",
    "    'Paul Ryan',\n",
    "    'Mike Huckabee',\n",
    "    'Ben Carson',\n",
    "    'Dan Quayle',\n",
    "    'Henry Kissinger',\n",
    "    'Lindsey Graham',\n",
    "    'Newt Gingrich',\n",
    "    'Donald Rumsfield',\n",
    "    'Nikki Haley',\n",
    "    'Elizabeth Dole',\n",
    "    'Jared Kushner',\n",
    "    'Greg Abbott',\n",
    "    'Pat Roberts',\n",
    "    'Jeff Sessions',\n",
    "    'Rick Santorum',\n",
    "    'Rick Perry',\n",
    "    'Tim Scott',\n",
    "    'Susan Collins',\n",
    "    'Rick Scott',\n",
    "    'Herman Cain',\n",
    "    'Barry Goldwater',\n",
    "    'Vivek Ramaswamy',\n",
    "    'Orrin Hatch',\n",
    "    'John Ashcroft'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3175ef35-7f2a-4748-acdd-2b996bfef33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "political_party_prompt_template = '{} is a'\n",
    "num_prompts = len(democrats_list)\n",
    "democrat_prompts = [ political_party_prompt_template.format(x) for x in democrats_list ][:num_prompts]\n",
    "republican_prompts = [ political_party_prompt_template.format(x) for x in republicans_list ][:num_prompts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "06b3a691-c332-4592-bf13-8fbcc16f2463",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:27<00:00,  1.45it/s]\n"
     ]
    }
   ],
   "source": [
    "party_scores_democrat = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {\n",
    "        'normal': democrat_normal_1.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn17.13'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_2.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn15.8'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_3.detach().cpu().numpy(),\n",
    "        'layer': 20, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn20.3'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_4.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn21.1 <= attn15.8'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_5.detach().cpu().numpy(),\n",
    "        'layer': 22, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn22.7'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_6.detach().cpu().numpy(),\n",
    "        'layer': 13, 'sublayer': 'ln_1_in', 'token': None, 'name': 'mlp18@-1 <= mlp16@-1 <= attn13.3@1'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_7.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': '(attn23) <= attn15.8 '\n",
    "    },\n",
    "    {\n",
    "        'normal': observable,\n",
    "        'layer': None, 'sublayer': 'lm_out', 'token': -1, 'name': 'observable'\n",
    "    },\n",
    "], democrat_prompts, pre_tokenized=False, save_every=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "cba4bed7-db25-4618-8b2f-c120d4c23565",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:27<00:00,  1.45it/s]\n"
     ]
    }
   ],
   "source": [
    "party_scores_republican = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {\n",
    "        'normal': democrat_normal_1.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn17.13'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_2.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn15.8'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_3.detach().cpu().numpy(),\n",
    "        'layer': 20, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn20.3'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_4.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn21.1 <= attn15.8'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_5.detach().cpu().numpy(),\n",
    "        'layer': 22, 'sublayer': 'ln_1_in', 'token': None, 'name': 'attn22.7'\n",
    "    },\n",
    "    {\n",
    "        'normal': democrat_normal_6.detach().cpu().numpy(),\n",
    "        'layer': 13, 'sublayer': 'ln_1_in', 'token': None, 'name': 'mlp18@-1 <= mlp16@-1 <= attn13.3@1'\n",
    "    },{\n",
    "        'normal': democrat_normal_7.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': '(attn23) <= attn15.8'\n",
    "    },\n",
    "    {\n",
    "        'normal': observable,\n",
    "        'layer': None, 'sublayer': 'lm_out', 'token': -1, 'name': 'observable'\n",
    "    },\n",
    "], republican_prompts, pre_tokenized=False, save_every=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f75ca855-aeb0-4a93-bc39-58a08bc75319",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e2473348-2a21-4476-afe0-592eb0038a4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cur_idx = 1\n",
    "\n",
    "cur_dem_scores = [x.mean() for x in party_scores_democrat[cur_idx]['scores']]\n",
    "cur_rep_scores = [x.mean() for x in party_scores_republican[cur_idx]['scores']]\n",
    "\n",
    "#fit, fit_info = np.polynomial.Polynomial.fit(np.concatenate([cur_dem_scores, cur_rep_scores]),\n",
    "#    np.concatenate([party_scores_democrat[-1]['scores'], party_scores_republican[-1]['scores']]), 1, full=True)\n",
    "#fit = fit.convert()\n",
    "#r2 = (1 - fit_info[0] / (np.concatenate([cur_dem_scores, cur_rep_scores]).size * np.concatenate([cur_dem_scores, cur_rep_scores]).var()))[0\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([cur_dem_scores, cur_rep_scores]),\n",
    "    np.concatenate([party_scores_democrat[-1]['scores'], party_scores_republican[-1]['scores']])\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(cur_dem_scores,\n",
    "            party_scores_democrat[-1]['scores'], label='Democrat')\n",
    "ax.scatter(cur_rep_scores,\n",
    "            party_scores_republican[-1]['scores'], label='Republican')\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "plt.plot([xmin, xmax], np.array([xmin, xmax])*slope + intercept, label=f'Best fit $r^2 = {r_value**2:.3g}$', color='black')\n",
    "plt.title(party_scores_democrat[cur_idx]['info']['name'])\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a9d24ba6-08ed-433a-9392-236db2101728",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "75a368fb-d9fb-4c96-9447-f78148a19c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cur_idx = -2\n",
    "\n",
    "cur_dem_scores = [x.mean() for x in party_scores_democrat[cur_idx]['scores']]\n",
    "cur_rep_scores = [x.mean() for x in party_scores_republican[cur_idx]['scores']]\n",
    "\n",
    "#fit, fit_info = np.polynomial.Polynomial.fit(np.concatenate([cur_dem_scores, cur_rep_scores]),\n",
    "#    np.concatenate([party_scores_democrat[-1]['scores'], party_scores_republican[-1]['scores']]), 1, full=True)\n",
    "#fit = fit.convert()\n",
    "#r2 = (1 - fit_info[0] / (np.concatenate([cur_dem_scores, cur_rep_scores]).size * np.concatenate([cur_dem_scores, cur_rep_scores]).var()))[0\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([cur_dem_scores, cur_rep_scores]),\n",
    "    np.concatenate([party_scores_democrat[-1]['scores'], party_scores_republican[-1]['scores']])\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(cur_dem_scores,\n",
    "            party_scores_democrat[-1]['scores'], label='Democrat')\n",
    "ax.scatter(cur_rep_scores,\n",
    "            party_scores_republican[-1]['scores'], label='Republican')\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "plt.plot([xmin, xmax], np.array([xmin, xmax])*slope + intercept, label=f'Best fit $r^2 = {r_value**2:.3g}$', color='black')\n",
    "plt.title(party_scores_democrat[cur_idx]['info']['name'])\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "68019c0e-8774-4855-85a7-e8875db4e233",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/40 [00:27<?, ?it/s]\n",
      "  0%|          | 0/40 [00:27<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "democrat_embeddings = embeddings_from_prompt_list2(model, democrat_prompts, 17, 'ln_1_in', token=-4)\n",
    "republican_embeddings = embeddings_from_prompt_list2(model, republican_prompts, 17, 'ln_1_in', token=-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "11876c41-be3a-4160-8ee9-a770944dad2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model\n",
    "linear_model = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "3d3b02f0-5dd1-42e8-9299-675ba15e3e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 30\n",
    "xs = np.concatenate([democrat_embeddings[:train_len], republican_embeddings[:train_len]])\n",
    "ys = np.concatenate([party_scores_democrat[-1]['scores'][:train_len], party_scores_republican[-1]['scores'][:train_len]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "044f1ca1-ce70-4486-93cd-6ff5ba6d9ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.fit(xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "442fb4a9-76e8-421e-bd66-df7220817a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_democrat_scores = democrat_embeddings[train_len:] @ linear_model.coef_\n",
    "logistic_republican_scores = republican_embeddings[train_len:] @ linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "255952e5-16b5-4279-bf59-21f42d8097b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([logistic_democrat_scores, logistic_republican_scores]),\n",
    "    np.concatenate([party_scores_democrat[-1]['scores'][train_len:], party_scores_republican[-1]['scores'][train_len:]])\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(logistic_democrat_scores,\n",
    "            party_scores_democrat[-1]['scores'][train_len:], label='Democrat')\n",
    "ax.scatter(logistic_republican_scores,\n",
    "            party_scores_republican[-1]['scores'][train_len:], label='Republican')\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "plt.plot([xmin, xmax], np.array([xmin, xmax])*slope + intercept, label=f'Best fit $r^2 = {r_value**2:.3g}$', color='black')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "bbfd5552-5033-4db2-bdf6-80a70c8aa38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_democrat_scores = democrat_embeddings @ linear_model.coef_\n",
    "logistic_republican_scores = republican_embeddings @ linear_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "a3ce59fa-7b39-4a93-992d-da5392066c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([logistic_democrat_scores, logistic_republican_scores]),\n",
    "    np.concatenate([party_scores_democrat[-1]['scores'], party_scores_republican[-1]['scores']])\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(logistic_democrat_scores,\n",
    "            party_scores_democrat[-1]['scores'], label='Democrat')\n",
    "ax.scatter(logistic_republican_scores,\n",
    "            party_scores_republican[-1]['scores'], label='Republican')\n",
    "\n",
    "xmin, xmax = ax.get_xlim()\n",
    "plt.plot([xmin, xmax], np.array([xmin, xmax])*slope + intercept, label=f'Best fit $r^2 = {r_value**2:.3g}$', color='black')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80bfb6ca-bdab-4851-aa7e-b9071861bf67",
   "metadata": {},
   "source": [
    "## Mean difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "310470a9-b53e-47c7-a73b-0d83c8de3ae1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/40 [00:20<?, ?it/s]\n",
      "  0%|          | 0/40 [00:20<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "democrat_embeddings = embeddings_from_prompt_list2(model, democrat_prompts, 17, 'ln_1_in', token=-4)\n",
    "republican_embeddings = embeddings_from_prompt_list2(model, republican_prompts, 17, 'ln_1_in', token=-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e8ef4ea5-eacc-4d69-a8d9-82f563097c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 30\n",
    "mean_dem = np.mean(democrat_embeddings[:train_len], axis=0)\n",
    "mean_rep = np.mean(republican_embeddings[:train_len], axis=0)\n",
    "mean_diff = mean_dem - mean_rep\n",
    "\n",
    "meanvec_dem_scores = democrat_embeddings[train_len:] @ mean_diff\n",
    "meanvec_rep_scores = republican_embeddings[train_len:] @ mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "510a7704-4b85-4ad0-9fff-7d779f68f334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.060505737483767155"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(\n",
    "    np.concatenate([meanvec_dem_scores, meanvec_rep_scores]),\n",
    "    np.concatenate([party_scores_democrat[-1]['scores'][train_len:], party_scores_republican[-1]['scores'][train_len:]])\n",
    ")\n",
    "r_value**2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afbbb8e-06a0-4547-8741-4d3afc842d5a",
   "metadata": {},
   "source": [
    "# Python vs. C code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde9b7bb-0c05-4cf9-be7c-e4f8040d15d7",
   "metadata": {},
   "source": [
    "## Get embeddings\n",
    "\n",
    "We'll be using embeddings to propagate our feature vectors through nonlinearities. However, we'll only use the embeddings from a single prompt of each case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e2ea5210-dacb-4d3a-a6ff-3210287e196f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tokens1 = ['){']\n",
    "neg_tokens1 = ['):']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ea38a2f1-3be0-4d87-aa9e-328c38f64a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "embeddings_A1 = embeddings_from_prompt_list(model, [\"int myfunc(int iters, float x\"])\n",
    "embeddings_B1 = embeddings_from_prompt_list(model, [\"def myfunc(  iters,  x\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a4ee91e0-a2e6-4acd-bae1-5dbe111710da",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable1 = make_lm_head_normal(pos_tokens1, neg_tokens1).detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "27b3671e-8c52-4920-bdaf-edf16f42cbc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.2220597, -1.4241419)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_A1[0]['lm_out'][0,-1] @ observable1, embeddings_B1[0]['lm_out'][0,-1] @ observable1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b4a001af-d758-4f14-a7ea-88596143e977",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tokens2 = [' #']\n",
    "neg_tokens2 = [' //']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a16cfed8-e9d0-485a-8dbf-002630aedab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "embeddings_A2 = embeddings_from_prompt_list(model, [\"\\n\\t\\tint myvar = oldvar + 1\"])\n",
    "embeddings_B2 = embeddings_from_prompt_list(model, [\"\\n    myvar = oldvar + 1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3ece1b78-5c2b-4450-8648-4c5b963e2a19",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable2 = make_lm_head_normal(pos_tokens2, neg_tokens2).detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "653b97a5-7a42-4052-a746-d162cd0ed9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.3225803, 1.7800093)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_A2[0]['lm_out'][0,-1] @ observable2, embeddings_B2[0]['lm_out'][0,-1] @ observable2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0799cc-6e44-4e3e-8433-e79723dc454f",
   "metadata": {},
   "source": [
    "## Propagate observable through `ln_f`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5bcade19-baf6-4436-8b4c-ce53d51bca29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([50257])\n"
     ]
    }
   ],
   "source": [
    "range_normal1 = make_range_normal(pos_tokens1, neg_tokens1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "77c86a19-eb03-4c71-8ee1-ef75b553b4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros = torch.zeros(range_normal1.shape).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c582ae6c-20f0-4c30-ad6b-10124bdc7f2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_f_x0_1 = torch.from_numpy(embeddings_A1[0]['ln_f_in'][:, -1, :]).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ff4823e3-b959-4f6f-927f-38c7731d015c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 8.381903171539307e-09\n",
      "9.999999999999998e-15\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "ln_f_normal1, ln_f_affine1 = get_normal_and_affine(ln_f_x0_1[0], model.transformer.ln_f, range_normal1,\n",
    "                                     zeros, lr=0.02, eps=0.0000001**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "940ff7ea-70c0-49a5-ac46-c37f2b96b503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([50257])\n"
     ]
    }
   ],
   "source": [
    "range_normal2 = make_range_normal(pos_tokens2, neg_tokens2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3b6de323-d2fc-427f-9ba1-ae69c92614c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_f_x0_2 = torch.from_numpy(embeddings_A2[0]['ln_f_in'][:, -1, :]).to(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c748e212-b47c-4077-b529-d296241de62a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final loss: 2.0123991362197557e-08\n",
      "9.999999999999998e-15\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "ln_f_normal2, ln_f_affine2 = get_normal_and_affine(ln_f_x0_2[0], model.transformer.ln_f, range_normal2,\n",
    "                                     zeros, lr=0.02, eps=0.0000001**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7440a6-5d2a-4abd-b519-7b3209a72c73",
   "metadata": {},
   "source": [
    "## Finding feature vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0c8ec769-070a-455b-b1d7-afc3da91c247",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_embeddings_A1 = embeddings_np_to_torch(embeddings_A1[0], free=False)\n",
    "cur_embeddings_B1 = embeddings_np_to_torch(embeddings_B1[0], free=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0692e01e-22e1-4339-86e1-30bd1e9b0de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_embeddings_A2 = embeddings_np_to_torch(embeddings_A2[0], free=False)\n",
    "cur_embeddings_B2 = embeddings_np_to_torch(embeddings_B2[0], free=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438373c3-9461-4b66-ab84-c972135ab945",
   "metadata": {},
   "source": [
    "### Path patching recursion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab58749-7301-4b99-a44c-5e9d6b03ad41",
   "metadata": {},
   "source": [
    "### Observable 1 ( C braces versus Python colon )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "513eab0b-5715-4445-a52d-91c1b9f2bcac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num tokens: 10\n",
      "Paths of length 1:\n",
      "===================\n",
      "mlp16@-1: 0.7218482494354248\n",
      "mlp19@-1: 0.32622528076171875\n",
      "mlp14@-1: 0.272205114364624\n",
      "attn17.4@3: 0.26427578926086426\n",
      "mlp18@-1: 0.2147061824798584\n",
      "attn17.1@3: 0.1893143653869629\n",
      "attn8.4@9: 0.17688822746276855\n",
      "mlp17@-1: 0.15833091735839844\n",
      "attn11.6@3: 0.140366792678833\n",
      "mlp5@-1: 0.13943767547607422\n",
      "mlp20@-1: 0.1332411766052246\n",
      "attn17.14@8: 0.12776756286621094\n",
      "attn12.1@9: 0.12544894218444824\n",
      "mlp8@-1: 0.12133073806762695\n",
      "attn15.5@4: 0.10443592071533203\n",
      "attn7.6@8: 0.10190320014953613\n",
      "attn16.9@4: 0.10162949562072754\n",
      "attn4.14@8: 0.09666728973388672\n",
      "attn7.13@8: 0.0961461067199707\n",
      "attn23.1@8: 0.09449410438537598\n",
      "embedding@-1: 0.0\n",
      "\n",
      "Paths of length 2:\n",
      "===================\n",
      "mlp18@-1 <= mlp16@-1: 0.23207855224609375\n",
      "mlp14@-1 <= mlp9@-1: 0.12980198860168457\n",
      "mlp8@-1 <= attn7.11@4: 0.12040185928344727\n",
      "attn17.4@3 <= attn8.4@1: 0.09862089157104492\n",
      "mlp20@-1 <= mlp5@-1: 0.08646655082702637\n",
      "mlp20@-1 <= mlp17@-1: 0.08066463470458984\n",
      "mlp20@-1 <= mlp15@-1: 0.08040285110473633\n",
      "mlp20@-1 <= mlp9@-1: 0.07884407043457031\n",
      "mlp20@-1 <= attn7.11@4: 0.0785989761352539\n",
      "mlp16@-1 <= attn7.11@4: 0.0771493911743164\n",
      "attn17.4@3 <= mlp13@3: 0.07530832290649414\n",
      "mlp17@-1 <= attn11.7@8: 0.07235288619995117\n",
      "mlp19@-1 <= mlp12@-1: 0.07176542282104492\n",
      "mlp20@-1 <= attn15.3@8: 0.0707399845123291\n",
      "mlp16@-1 <= mlp11@-1: 0.0681772232055664\n",
      "attn17.4@3 <= mlp15@3: 0.06734848022460938\n",
      "attn17.14@8 <= mlp2@8: 0.06634688377380371\n",
      "mlp17@-1 <= attn9.7@8: 0.0661160945892334\n",
      "mlp14@-1 <= attn13.10@8: 0.06550097465515137\n",
      "mlp8@-1 <= mlp2@-1: 0.0654304027557373\n",
      "attn15.5@4 <= embedding@4: 0.0016665458679199219\n",
      "attn16.9@4 <= embedding@4: 0.0007381439208984375\n",
      "attn17.14@8 <= embedding@8: 0.0002918243408203125\n",
      "mlp19@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp17@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "embedding@-1: 0.0\n",
      "mlp16@-1 <= embedding@-1: 0.0\n",
      "attn17.4@3 <= embedding@3: 0.0\n",
      "mlp18@-1 <= embedding@-1: 0.0\n",
      "attn17.1@3 <= embedding@3: 0.0\n",
      "attn8.4@9 <= embedding@9: 0.0\n",
      "attn11.6@3 <= embedding@3: 0.0\n",
      "mlp5@-1 <= embedding@-1: 0.0\n",
      "attn12.1@9 <= embedding@9: 0.0\n",
      "mlp8@-1 <= embedding@-1: 0.0\n",
      "mlp20@-1 <= embedding@-1: -2.384185791015625e-07\n",
      "attn23.1@8 <= embedding@8: -0.0002570152282714844\n",
      "attn4.14@8 <= embedding@8: -0.0016849040985107422\n",
      "attn7.13@8 <= embedding@8: -0.0019366741180419922\n",
      "\n",
      "Paths of length 3:\n",
      "===================\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1: 0.10980820655822754\n",
      "mlp20@-1 <= mlp15@-1 <= mlp8@-1: 0.062122344970703125\n",
      "mlp20@-1 <= mlp9@-1 <= mlp4@-1: 0.058637142181396484\n",
      "mlp19@-1 <= mlp12@-1 <= mlp8@-1: 0.05771160125732422\n",
      "attn17.4@3 <= mlp13@3 <= mlp8@3: 0.05348062515258789\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1: 0.046181440353393555\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1: 0.043694257736206055\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3: 0.04285717010498047\n",
      "attn17.4@3 <= mlp15@3 <= mlp12@3: 0.04085683822631836\n",
      "attn17.14@8 <= mlp2@8 <= mlp0@8: 0.03978323936462402\n",
      "mlp16@-1 <= mlp11@-1 <= mlp3@-1: 0.036919593811035156\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1: 0.03570127487182617\n",
      "mlp16@-1 <= mlp11@-1 <= attn8.9@9: 0.02951669692993164\n",
      "mlp20@-1 <= mlp9@-1 <= attn7.1@8: 0.028576374053955078\n",
      "mlp20@-1 <= mlp9@-1 <= attn8.14@9: 0.02844858169555664\n",
      "mlp18@-1 <= mlp16@-1 <= attn7.3@8: 0.0274202823638916\n",
      "mlp20@-1 <= mlp17@-1 <= mlp14@-1: 0.027112245559692383\n",
      "attn17.4@3 <= mlp13@3 <= mlp5@3: 0.027083635330200195\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4: 0.026735305786132812\n",
      "mlp20@-1 <= mlp5@-1 <= mlp3@-1: 0.02529430389404297\n",
      "attn15.5@4 <= embedding@4: 0.0016665458679199219\n",
      "mlp8@-1 <= attn7.11@4 <= embedding@4: 0.0008711814880371094\n",
      "attn16.9@4 <= embedding@4: 0.0007381439208984375\n",
      "mlp17@-1 <= attn11.7@8 <= embedding@8: 0.0006997585296630859\n",
      "mlp14@-1 <= attn13.10@8 <= embedding@8: 0.0005750656127929688\n",
      "mlp17@-1 <= attn9.7@8 <= embedding@8: 0.0003085136413574219\n",
      "mlp20@-1 <= attn7.11@4 <= embedding@4: 0.00030612945556640625\n",
      "attn17.14@8 <= embedding@8: 0.0002918243408203125\n",
      "attn17.14@8 <= mlp2@8 <= embedding@8: 0.00019216537475585938\n",
      "mlp19@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp17@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= mlp9@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp19@-1 <= mlp12@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "embedding@-1: 0.0\n",
      "mlp16@-1 <= embedding@-1: 0.0\n",
      "attn17.4@3 <= embedding@3: 0.0\n",
      "mlp18@-1 <= embedding@-1: 0.0\n",
      "attn17.1@3 <= embedding@3: 0.0\n",
      "attn8.4@9 <= embedding@9: 0.0\n",
      "\n",
      "Paths of length 4:\n",
      "===================\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1: 0.03382444381713867\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1: 0.02842235565185547\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8: 0.021991968154907227\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3: 0.019730091094970703\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp11@-1: 0.018808364868164062\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp8@-1: 0.018413305282592773\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= attn6.13@8: 0.018386363983154297\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp3@-1: 0.01601696014404297\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp4@-1: 0.015149831771850586\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4: 0.014622211456298828\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn8.4@9: 0.014056205749511719\n",
      "mlp16@-1 <= mlp11@-1 <= mlp3@-1 <= attn2.11@8: 0.013783931732177734\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp5@-1: 0.01337575912475586\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn6.13@8: 0.01313924789428711\n",
      "mlp20@-1 <= mlp9@-1 <= mlp4@-1 <= attn3.11@8: 0.012024164199829102\n",
      "mlp20@-1 <= mlp17@-1 <= mlp14@-1 <= mlp9@-1: 0.011837005615234375\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= attn4.3@0: 0.011750221252441406\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn8.2@9: 0.011539936065673828\n",
      "mlp20@-1 <= mlp17@-1 <= mlp14@-1 <= mlp7@-1: 0.011507987976074219\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= attn0.9@4: 0.011472940444946289\n",
      "attn15.5@4 <= embedding@4: 0.0016665458679199219\n",
      "mlp8@-1 <= attn7.11@4 <= embedding@4: 0.0008711814880371094\n",
      "attn17.14@8 <= mlp2@8 <= mlp0@8 <= embedding@8: 0.0008115768432617188\n",
      "attn16.9@4 <= embedding@4: 0.0007381439208984375\n",
      "mlp17@-1 <= attn11.7@8 <= embedding@8: 0.0006997585296630859\n",
      "mlp14@-1 <= attn13.10@8 <= embedding@8: 0.0005750656127929688\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= embedding@4: 0.0003592967987060547\n",
      "mlp17@-1 <= attn9.7@8 <= embedding@8: 0.0003085136413574219\n",
      "mlp20@-1 <= attn7.11@4 <= embedding@4: 0.00030612945556640625\n",
      "attn17.14@8 <= embedding@8: 0.0002918243408203125\n",
      "attn17.14@8 <= mlp2@8 <= embedding@8: 0.00019216537475585938\n",
      "mlp19@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp17@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= mlp9@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp19@-1 <= mlp12@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp19@-1 <= mlp12@-1 <= mlp8@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "embedding@-1: 0.0\n",
      "mlp16@-1 <= embedding@-1: 0.0\n",
      "\n",
      "Paths of length 5:\n",
      "===================\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= mlp2@-1: 0.01053619384765625\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0: 0.008713722229003906\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.9@8: 0.008101463317871094\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp3@-1: 0.007473468780517578\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.11@0: 0.007106304168701172\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= mlp0@8: 0.006861209869384766\n",
      "mlp16@-1 <= mlp11@-1 <= mlp3@-1 <= attn2.11@8 <= mlp0@8: 0.0068531036376953125\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp1@4: 0.005796909332275391\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn2.4@8: 0.005665302276611328\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp0@4: 0.0054662227630615234\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp8@-1 <= mlp6@-1: 0.0053882598876953125\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp4@-1 <= attn3.9@8: 0.005366325378417969\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp5@4: 0.005076408386230469\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp11@-1 <= attn7.11@4: 0.004828453063964844\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= attn6.13@8 <= mlp1@8: 0.004818439483642578\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn4.2@8: 0.0047588348388671875\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn2.11@8: 0.004657268524169922\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp11@-1 <= mlp5@-1: 0.004564762115478516\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= mlp1@8: 0.004547595977783203\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp11@-1 <= mlp6@-1: 0.004141092300415039\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= attn0.9@4 <= embedding@4: 0.011472940444946289\n",
      "attn15.5@4 <= embedding@4: 0.0016665458679199219\n",
      "mlp8@-1 <= attn7.11@4 <= embedding@4: 0.0008711814880371094\n",
      "attn17.14@8 <= mlp2@8 <= mlp0@8 <= embedding@8: 0.0008115768432617188\n",
      "attn16.9@4 <= embedding@4: 0.0007381439208984375\n",
      "mlp17@-1 <= attn11.7@8 <= embedding@8: 0.0006997585296630859\n",
      "mlp14@-1 <= attn13.10@8 <= embedding@8: 0.0005750656127929688\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= embedding@4: 0.0003592967987060547\n",
      "mlp17@-1 <= attn9.7@8 <= embedding@8: 0.0003085136413574219\n",
      "mlp20@-1 <= attn7.11@4 <= embedding@4: 0.00030612945556640625\n",
      "attn17.14@8 <= embedding@8: 0.0002918243408203125\n",
      "attn17.14@8 <= mlp2@8 <= embedding@8: 0.00019216537475585938\n",
      "mlp20@-1 <= mlp9@-1 <= mlp4@-1 <= attn3.11@8 <= embedding@8: 8.368492126464844e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn6.13@8 <= embedding@8: 4.482269287109375e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= embedding@8: 1.430511474609375e-05\n",
      "mlp19@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp17@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp14@-1 <= mlp9@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "mlp19@-1 <= mlp12@-1 <= embedding@-1: 2.384185791015625e-07\n",
      "\n",
      "Paths of length 6:\n",
      "===================\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn2.11@8 <= mlp0@8: 0.003980398178100586\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp1@4 <= attn0.9@4: 0.002642393112182617\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= attn6.13@8 <= mlp1@8 <= attn0.14@8: 0.0025136470794677734\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp3@-1 <= attn2.14@8: 0.002422332763671875\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0 <= mlp0@0: 0.002415180206298828\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.9@8 <= mlp1@8: 0.0023496150970458984\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp4@-1 <= attn3.9@8 <= mlp1@8: 0.0022597312927246094\n",
      "attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0 <= attn0.7@0: 0.002250194549560547\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp1@4 <= attn0.8@4: 0.0018792152404785156\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn4.2@8 <= mlp1@8: 0.0018422603607177734\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp3@-1 <= attn2.11@8: 0.0017161369323730469\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp8@-1 <= mlp6@-1 <= attn3.9@8: 0.0016820430755615234\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= mlp1@8 <= attn0.14@8: 0.0015635490417480469\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn2.11@8 <= attn0.14@8: 0.0014905929565429688\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= mlp1@8 <= attn0.8@8: 0.0014774799346923828\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp1@4 <= attn0.4@4: 0.0014772415161132812\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn2.4@8 <= mlp0@8: 0.0013926029205322266\n",
      "mlp18@-1 <= mlp16@-1 <= mlp14@-1 <= mlp8@-1 <= mlp6@-1 <= attn4.8@8: 0.0013804435729980469\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp5@4 <= mlp0@4: 0.0013561248779296875\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= mlp1@8 <= attn0.9@8: 0.0013089179992675781\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= attn0.9@4 <= embedding@4: 0.011472940444946289\n",
      "attn15.5@4 <= embedding@4: 0.0016665458679199219\n",
      "mlp8@-1 <= attn7.11@4 <= embedding@4: 0.0008711814880371094\n",
      "attn17.14@8 <= mlp2@8 <= mlp0@8 <= embedding@8: 0.0008115768432617188\n",
      "attn16.9@4 <= embedding@4: 0.0007381439208984375\n",
      "mlp17@-1 <= attn11.7@8 <= embedding@8: 0.0006997585296630859\n",
      "mlp14@-1 <= attn13.10@8 <= embedding@8: 0.0005750656127929688\n",
      "mlp16@-1 <= attn7.11@4 <= mlp1@4 <= embedding@4: 0.0003592967987060547\n",
      "mlp17@-1 <= attn9.7@8 <= embedding@8: 0.0003085136413574219\n",
      "mlp20@-1 <= attn7.11@4 <= embedding@4: 0.00030612945556640625\n",
      "attn17.14@8 <= embedding@8: 0.0002918243408203125\n",
      "attn17.14@8 <= mlp2@8 <= embedding@8: 0.00019216537475585938\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.11@4 <= mlp0@4 <= embedding@4: 0.00013780593872070312\n",
      "mlp20@-1 <= mlp9@-1 <= mlp4@-1 <= attn3.11@8 <= embedding@8: 8.368492126464844e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn6.13@8 <= embedding@8: 4.482269287109375e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn4.2@8 <= embedding@8: 3.4332275390625e-05\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp4@-1 <= attn3.9@8 <= embedding@8: 1.8596649169921875e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8 <= embedding@8: 1.430511474609375e-05\n",
      "mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.9@8 <= embedding@8: 1.3828277587890625e-05\n",
      "mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= attn6.13@8 <= mlp1@8 <= embedding@8: 1.3113021850585938e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_paths(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal, 6, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5948b90a-98a5-49bd-a661-4d920d3558bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num tokens: 10\n",
      "Paths of length 1:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1: 0.019365549087524414\n",
      "attn17.4@3 <= attn8.4@1 <= mlp4@1: 0.008618831634521484\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.5@0: 0.007427215576171875\n",
      "attn17.4@3 <= attn8.4@1 <= attn5.9@0: 0.006720066070556641\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1: 0.006449460983276367\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.8@0: 0.005681037902832031\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0: 0.005432605743408203\n",
      "attn17.4@3 <= attn8.4@1 <= attn5.7@0: 0.004769325256347656\n",
      "attn17.4@3 <= attn8.4@1 <= attn6.0@0: 0.004183530807495117\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.4@0: 0.0035715103149414062\n",
      "attn17.4@3 <= attn8.4@1 <= embedding@1: 0.0\n",
      "\n",
      "Paths of length 2:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0: 0.003844022750854492\n",
      "attn17.4@3 <= attn8.4@1 <= mlp4@1 <= attn2.12@0: 0.003532886505126953\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0: 0.003392934799194336\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= mlp0@1: 0.0031054019927978516\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= mlp4@1: 0.0030393600463867188\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.5@0 <= mlp1@0: 0.0029680728912353516\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= attn5.15@0: 0.0028824806213378906\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.8@0 <= mlp1@0: 0.0026721954345703125\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0: 0.002598285675048828\n",
      "attn17.4@3 <= attn8.4@1 <= attn6.0@0 <= mlp1@0: 0.0024580955505371094\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= embedding@0: 0.0001323223114013672\n",
      "attn17.4@3 <= attn8.4@1 <= attn5.9@0 <= embedding@0: 4.863739013671875e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.5@0 <= embedding@0: 4.38690185546875e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.8@0 <= embedding@0: 1.621246337890625e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.4@0 <= embedding@0: 4.76837158203125e-07\n",
      "attn17.4@3 <= attn8.4@1 <= embedding@1: 0.0\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= embedding@1: 0.0\n",
      "attn17.4@3 <= attn8.4@1 <= mlp4@1 <= embedding@1: 0.0\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= embedding@1: 0.0\n",
      "attn17.4@3 <= attn8.4@1 <= attn6.0@0 <= embedding@0: -1.9550323486328125e-05\n",
      "\n",
      "Paths of length 3:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0: 0.0027780532836914062\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp0@0: 0.002130270004272461\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0: 0.0015630722045898438\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0: 0.0015530586242675781\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0: 0.0015499591827392578\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.0@0: 0.0014317035675048828\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= attn5.15@0 <= mlp1@0: 0.0012674331665039062\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= mlp4@1 <= attn2.3@0: 0.0012259483337402344\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.4@0: 0.0012116432189941406\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.6@0: 0.00112152099609375\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0 <= embedding@0: 0.003392934799194336\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= embedding@0: 0.0001323223114013672\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= embedding@0: 5.0067901611328125e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn5.9@0 <= embedding@0: 4.863739013671875e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.5@0 <= embedding@0: 4.38690185546875e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.5@0 <= mlp1@0 <= embedding@0: 1.6450881958007812e-05\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.8@0 <= embedding@0: 1.621246337890625e-05\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= attn5.15@0 <= embedding@0: 8.106231689453125e-06\n",
      "attn17.4@3 <= attn8.4@1 <= attn4.4@0 <= embedding@0: 4.76837158203125e-07\n",
      "attn17.4@3 <= attn8.4@1 <= embedding@1: 0.0\n",
      "\n",
      "Paths of length 4:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0: 0.002445697784423828\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0: 0.001399993896484375\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0: 0.0011746883392333984\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.3@0: 0.0010831356048583984\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.2@0: 0.0009322166442871094\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= mlp4@1 <= attn2.3@0 <= mlp0@0: 0.0006062984466552734\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.12@0: 0.0004076957702636719\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.1@0: 0.0003960132598876953\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.14@0: 0.0003185272216796875\n",
      "attn17.4@3 <= attn8.4@1 <= mlp6@1 <= mlp4@1 <= attn2.3@0 <= attn0.11@0: 0.0001437664031982422\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0 <= embedding@0: 0.003392934799194336\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0 <= embedding@0: 0.0015630722045898438\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0 <= embedding@0: 0.0015530586242675781\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0 <= embedding@0: 0.0015499591827392578\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.0@0 <= embedding@0: 0.0014317035675048828\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.4@0 <= embedding@0: 0.0012116432189941406\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.6@0 <= embedding@0: 0.00112152099609375\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= embedding@0: 0.0001323223114013672\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= embedding@0: 5.245208740234375e-05\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= embedding@0: 5.0067901611328125e-05\n",
      "\n",
      "Paths of length 5:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0 <= embedding@0: 0.003392934799194336\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0 <= embedding@0: 0.002445697784423828\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0 <= embedding@0: 0.0015630722045898438\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0 <= embedding@0: 0.0015530586242675781\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0 <= embedding@0: 0.0015499591827392578\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.0@0 <= embedding@0: 0.0014317035675048828\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0 <= embedding@0: 0.001399993896484375\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.4@0 <= embedding@0: 0.0012116432189941406\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0 <= embedding@0: 0.0011746883392333984\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.6@0 <= embedding@0: 0.00112152099609375\n",
      "\n",
      "Paths of length 6:\n",
      "===================\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0 <= embedding@0: 0.003392934799194336\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0 <= embedding@0: 0.002445697784423828\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.9@0 <= embedding@0: 0.0015630722045898438\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0 <= embedding@0: 0.0015530586242675781\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0 <= embedding@0: 0.0015499591827392578\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.0@0 <= embedding@0: 0.0014317035675048828\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.11@0 <= embedding@0: 0.001399993896484375\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= attn0.4@0 <= embedding@0: 0.0012116432189941406\n",
      "attn17.4@3 <= attn8.4@1 <= mlp5@1 <= attn3.11@0 <= mlp1@0 <= attn0.15@0 <= embedding@0: 0.0011746883392333984\n",
      "attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= mlp1@0 <= attn0.6@0 <= embedding@0: 0.00112152099609375\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_paths(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal, 6, 10,\n",
    "    starting_path=list(reversed([\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 4},\n",
    "        {'layer': 8, 'sublayer': 'attn', 'token': 1, 'head': 4}\n",
    "    ])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bc30e9-5e4f-4191-b578-abfbf748a961",
   "metadata": {},
   "source": [
    "### Observable 2 ( C comments vs. Python comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "572de2cc-e810-4ec4-ba5f-804e3dcd4af7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num tokens: 11\n",
      "Paths of length 1:\n",
      "===================\n",
      "mlp11@-1: 0.18851017951965332\n",
      "attn15.0@3: 0.16901814937591553\n",
      "mlp17@-1: 0.14439892768859863\n",
      "mlp4@-1: 0.12646138668060303\n",
      "mlp13@-1: 0.07311081886291504\n",
      "attn8.14@10: 0.07209467887878418\n",
      "mlp10@-1: 0.07191348075866699\n",
      "attn5.7@3: 0.0657120943069458\n",
      "attn7.2@3: 0.0644998550415039\n",
      "attn11.4@9: 0.05682945251464844\n",
      "attn21.2@3: 0.05209839344024658\n",
      "attn19.13@3: 0.051744818687438965\n",
      "attn20.15@3: 0.04951179027557373\n",
      "attn15.2@3: 0.048478007316589355\n",
      "attn13.2@6: 0.04301488399505615\n",
      "attn20.5@1: 0.04234802722930908\n",
      "mlp15@-1: 0.04005551338195801\n",
      "attn6.7@10: 0.03989887237548828\n",
      "mlp2@-1: 0.03574967384338379\n",
      "attn18.3@9: 0.03522956371307373\n",
      "embedding@-1: 0.0\n",
      "\n",
      "Paths of length 2:\n",
      "===================\n",
      "mlp17@-1 <= mlp14@-1: 0.11241638660430908\n",
      "mlp17@-1 <= mlp7@-1: 0.10261166095733643\n",
      "mlp17@-1 <= mlp11@-1: 0.08681726455688477\n",
      "mlp13@-1 <= mlp7@-1: 0.08635938167572021\n",
      "mlp15@-1 <= mlp7@-1: 0.08448898792266846\n",
      "mlp10@-1 <= mlp7@-1: 0.0748131275177002\n",
      "attn15.0@3 <= mlp6@3: 0.06942367553710938\n",
      "mlp11@-1 <= mlp8@-1: 0.06170618534088135\n",
      "mlp11@-1 <= mlp9@-1: 0.0513690710067749\n",
      "mlp15@-1 <= mlp12@-1: 0.05060136318206787\n",
      "mlp17@-1 <= mlp12@-1: 0.04298412799835205\n",
      "mlp15@-1 <= mlp10@-1: 0.0400921106338501\n",
      "mlp11@-1 <= mlp7@-1: 0.03719377517700195\n",
      "mlp17@-1 <= mlp9@-1: 0.03627300262451172\n",
      "mlp15@-1 <= mlp8@-1: 0.03543508052825928\n",
      "mlp13@-1 <= attn11.14@6: 0.03492927551269531\n",
      "attn13.2@6 <= mlp11@6: 0.03319966793060303\n",
      "mlp11@-1 <= attn8.4@4: 0.03280484676361084\n",
      "mlp13@-1 <= attn9.5@6: 0.029158949851989746\n",
      "attn11.4@9 <= mlp5@9: 0.028656363487243652\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn5.7@3 <= embedding@3: 0.00048291683197021484\n",
      "attn15.2@3 <= embedding@3: 0.0001837015151977539\n",
      "attn20.5@1 <= embedding@1: 0.00015354156494140625\n",
      "attn7.2@3 <= embedding@3: 5.1975250244140625e-05\n",
      "attn19.13@3 <= embedding@3: 3.230571746826172e-05\n",
      "mlp15@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp13@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "attn8.14@10 <= embedding@10: 1.1920928955078125e-07\n",
      "embedding@-1: 0.0\n",
      "attn6.7@10 <= embedding@10: 0.0\n",
      "mlp2@-1 <= embedding@-1: 0.0\n",
      "attn18.3@9 <= embedding@9: 0.0\n",
      "attn11.4@9 <= embedding@9: -1.1920928955078125e-07\n",
      "attn13.2@6 <= embedding@6: -1.1920928955078125e-07\n",
      "mlp17@-1 <= embedding@-1: -3.5762786865234375e-07\n",
      "mlp11@-1 <= embedding@-1: -4.76837158203125e-07\n",
      "mlp4@-1 <= embedding@-1: -4.76837158203125e-07\n",
      "mlp10@-1 <= embedding@-1: -5.960464477539062e-07\n",
      "attn20.15@3 <= embedding@3: -0.00020813941955566406\n",
      "\n",
      "Paths of length 3:\n",
      "===================\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1: 0.06497156620025635\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1: 0.049089670181274414\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1: 0.042789578437805176\n",
      "mlp11@-1 <= mlp8@-1 <= mlp2@-1: 0.03955984115600586\n",
      "mlp11@-1 <= mlp9@-1 <= mlp7@-1: 0.03816103935241699\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1: 0.030269145965576172\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1: 0.029964804649353027\n",
      "mlp17@-1 <= mlp7@-1 <= mlp2@-1: 0.029896974563598633\n",
      "mlp11@-1 <= mlp9@-1 <= mlp2@-1: 0.028743743896484375\n",
      "mlp17@-1 <= mlp14@-1 <= mlp4@-1: 0.02687251567840576\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1: 0.024662017822265625\n",
      "attn13.2@6 <= mlp11@6 <= attn9.7@3: 0.02432727813720703\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1: 0.023862361907958984\n",
      "mlp17@-1 <= mlp11@-1 <= mlp6@-1: 0.02336418628692627\n",
      "mlp17@-1 <= mlp12@-1 <= mlp5@-1: 0.022897839546203613\n",
      "mlp15@-1 <= mlp8@-1 <= mlp2@-1: 0.022513389587402344\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1: 0.021655917167663574\n",
      "mlp17@-1 <= mlp12@-1 <= mlp2@-1: 0.020238280296325684\n",
      "mlp17@-1 <= mlp7@-1 <= mlp5@-1: 0.01717698574066162\n",
      "mlp17@-1 <= mlp11@-1 <= mlp8@-1: 0.016914010047912598\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn5.7@3 <= embedding@3: 0.00048291683197021484\n",
      "attn15.2@3 <= embedding@3: 0.0001837015151977539\n",
      "attn20.5@1 <= embedding@1: 0.00015354156494140625\n",
      "attn7.2@3 <= embedding@3: 5.1975250244140625e-05\n",
      "attn19.13@3 <= embedding@3: 3.230571746826172e-05\n",
      "mlp15@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp12@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp10@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp13@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "attn8.14@10 <= embedding@10: 1.1920928955078125e-07\n",
      "mlp13@-1 <= mlp7@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "mlp13@-1 <= attn11.14@6 <= embedding@6: 1.1920928955078125e-07\n",
      "mlp13@-1 <= attn9.5@6 <= embedding@6: 1.1920928955078125e-07\n",
      "embedding@-1: 0.0\n",
      "attn6.7@10 <= embedding@10: 0.0\n",
      "mlp2@-1 <= embedding@-1: 0.0\n",
      "attn18.3@9 <= embedding@9: 0.0\n",
      "\n",
      "Paths of length 4:\n",
      "===================\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1: 0.023994803428649902\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1 <= mlp6@-1: 0.020870327949523926\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1: 0.015929818153381348\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1: 0.014329910278320312\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp7@-1: 0.014124512672424316\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp1@-1: 0.01252448558807373\n",
      "mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn6.13@9: 0.010588526725769043\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp8@-1: 0.009345531463623047\n",
      "mlp17@-1 <= mlp11@-1 <= mlp8@-1 <= mlp3@-1: 0.009336113929748535\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn3.11@3: 0.009317755699157715\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn3.15@3: 0.008735418319702148\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1 <= attn8.4@4: 0.008231401443481445\n",
      "mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.15@3: 0.008229851722717285\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= mlp2@-1: 0.007364392280578613\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn5.7@3: 0.007275700569152832\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1: 0.007039785385131836\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn5.1@3: 0.006859540939331055\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp6@-1: 0.006843209266662598\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1: 0.00661921501159668\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp7@-1: 0.0066127777099609375\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn5.7@3 <= embedding@3: 0.00048291683197021484\n",
      "attn15.2@3 <= embedding@3: 0.0001837015151977539\n",
      "attn20.5@1 <= embedding@1: 0.00015354156494140625\n",
      "attn7.2@3 <= embedding@3: 5.1975250244140625e-05\n",
      "attn19.13@3 <= embedding@3: 3.230571746826172e-05\n",
      "mlp15@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp12@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp10@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= mlp2@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp13@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "attn8.14@10 <= embedding@10: 1.1920928955078125e-07\n",
      "mlp13@-1 <= mlp7@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "mlp13@-1 <= attn11.14@6 <= embedding@6: 1.1920928955078125e-07\n",
      "mlp13@-1 <= attn9.5@6 <= embedding@6: 1.1920928955078125e-07\n",
      "embedding@-1: 0.0\n",
      "attn6.7@10 <= embedding@10: 0.0\n",
      "\n",
      "Paths of length 5:\n",
      "===================\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1: 0.025972962379455566\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp7@-1 <= mlp5@-1: 0.010417461395263672\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1: 0.009948253631591797\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.4@3: 0.008308053016662598\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1 <= mlp6@-1 <= mlp2@-1: 0.008252978324890137\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp8@-1 <= mlp6@-1: 0.008203387260437012\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1: 0.007849454879760742\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= attn5.7@3 <= mlp0@3: 0.007621407508850098\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1: 0.006972312927246094\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp7@-1 <= mlp5@-1: 0.006189584732055664\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.4@3: 0.005856752395629883\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp8@-1 <= mlp5@-1: 0.0055849552154541016\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.8@3: 0.004948258399963379\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.8@3: 0.004717707633972168\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp6@-1 <= mlp2@-1: 0.004181027412414551\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.11@3: 0.004135608673095703\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.7@1: 0.0037528276443481445\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.5@10: 0.003548145294189453\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.5@10: 0.00338590145111084\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1 <= mlp6@-1 <= attn2.11@3: 0.0033113956451416016\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn5.7@3 <= embedding@3: 0.00048291683197021484\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1 <= embedding@1: 0.0003390312194824219\n",
      "attn15.2@3 <= embedding@3: 0.0001837015151977539\n",
      "attn20.5@1 <= embedding@1: 0.00015354156494140625\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn3.15@3 <= embedding@3: 9.810924530029297e-05\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1 <= embedding@1: 6.341934204101562e-05\n",
      "attn7.2@3 <= embedding@3: 5.1975250244140625e-05\n",
      "attn19.13@3 <= embedding@3: 3.230571746826172e-05\n",
      "mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.15@3 <= embedding@3: 1.2278556823730469e-05\n",
      "mlp15@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp12@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp10@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= mlp2@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp13@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "attn8.14@10 <= embedding@10: 1.1920928955078125e-07\n",
      "mlp13@-1 <= mlp7@-1 <= embedding@-1: 1.1920928955078125e-07\n",
      "\n",
      "Paths of length 6:\n",
      "===================\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.4@3 <= mlp1@3: 0.0034914016723632812\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1 <= attn0.8@1: 0.002714395523071289\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= mlp0@-1: 0.002601146697998047\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1 <= attn0.2@1: 0.0025787353515625\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1 <= attn0.4@1: 0.002441883087158203\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.4@3 <= mlp1@3: 0.0024105310440063477\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= attn1.3@1: 0.002315998077392578\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.11@3 <= mlp0@3: 0.002310037612915039\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp8@-1 <= mlp6@-1 <= attn5.7@3: 0.0018180608749389648\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= attn1.3@1: 0.0018123388290405273\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= mlp0@-1: 0.0018041133880615234\n",
      "mlp17@-1 <= mlp12@-1 <= mlp9@-1 <= mlp6@-1 <= attn2.11@3 <= mlp0@3: 0.0017396211624145508\n",
      "mlp17@-1 <= mlp14@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp1@-1: 0.0016624927520751953\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= attn1.3@3: 0.001558065414428711\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.8@3 <= mlp1@3: 0.0015338659286499023\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= mlp2@-1 <= attn1.3@2: 0.0014908313751220703\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.8@3 <= mlp1@3: 0.001487135887145996\n",
      "mlp17@-1 <= mlp14@-1 <= mlp12@-1 <= mlp7@-1 <= mlp5@-1 <= mlp1@-1: 0.0014473199844360352\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1 <= attn0.3@1: 0.0013878345489501953\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.8@3 <= mlp0@3: 0.0013557672500610352\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn5.7@3 <= embedding@3: 0.00048291683197021484\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1 <= embedding@1: 0.0003390312194824219\n",
      "attn15.2@3 <= embedding@3: 0.0001837015151977539\n",
      "attn20.5@1 <= embedding@1: 0.00015354156494140625\n",
      "mlp11@-1 <= mlp7@-1 <= mlp5@-1 <= attn3.15@3 <= embedding@3: 9.810924530029297e-05\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn3.4@3 <= embedding@3: 6.973743438720703e-05\n",
      "mlp11@-1 <= mlp9@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.7@1 <= embedding@1: 6.461143493652344e-05\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= attn2.7@1 <= embedding@1: 6.341934204101562e-05\n",
      "attn7.2@3 <= embedding@3: 5.1975250244140625e-05\n",
      "mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.11@3 <= embedding@3: 3.314018249511719e-05\n",
      "attn19.13@3 <= embedding@3: 3.230571746826172e-05\n",
      "mlp11@-1 <= mlp8@-1 <= mlp5@-1 <= attn3.2@1 <= mlp1@1 <= embedding@1: 2.586841583251953e-05\n",
      "mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.15@3 <= embedding@3: 1.2278556823730469e-05\n",
      "mlp15@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp12@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp10@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp8@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "mlp15@-1 <= mlp7@-1 <= mlp5@-1 <= embedding@-1: 3.5762786865234375e-07\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_paths(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2, 6, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "dc04e952-3e61-47f5-a4e8-ffd7eb30dcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.1235, device='cuda:0')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_normal_test1 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        #attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0: 0.008713722229003906\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 14},\n",
    "        {'layer': 13, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 6, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 4, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 2, 'sublayer': 'attn', 'token': 0, 'head': 11},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "c_normal_test2 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        #mlp11@-1 <= mlp8@-1 <= mlp6@-1 <= mlp4@-1 <= attn2.11@3: 0.004135608673095703\n",
    "        {'layer': 11, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 8, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 6, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 4, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 2, 'sublayer': 'attn', 'token': 3, 'head': 11},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "cossim(c_normal_test1, c_normal_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "4e03b64b-f8ee-430c-916d-990bf31f8da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.2404, device='cuda:0')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_normal_test1 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        #attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0: 0.008713722229003906\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 4},\n",
    "        {'layer': 8, 'sublayer': 'attn', 'token': 1, 'head': 4},\n",
    "        {'layer': 3, 'sublayer': 'attn', 'token': 0, 'head': 11},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "c_normal_test2 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 11, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 7, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 5, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 3, 'sublayer': 'attn', 'token': 3, 'head': 11},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "cossim(c_normal_test1, c_normal_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "b91b3cf6-e462-46bd-b616-eb931a3f346d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num tokens: 11\n",
      "Paths of length 1:\n",
      "===================\n",
      "attn15.0@3 <= mlp6@3: 0.06942367553710938\n",
      "attn15.0@3 <= mlp8@3: 0.025379538536071777\n",
      "attn15.0@3 <= mlp7@3: 0.017655134201049805\n",
      "attn15.0@3 <= mlp4@3: 0.01650869846343994\n",
      "attn15.0@3 <= mlp0@3: 0.009404301643371582\n",
      "attn15.0@3 <= attn7.13@2: 0.00679624080657959\n",
      "attn15.0@3 <= attn6.2@3: 0.006493568420410156\n",
      "attn15.0@3 <= attn0.4@3: 0.005913138389587402\n",
      "attn15.0@3 <= attn7.11@3: 0.004835367202758789\n",
      "attn15.0@3 <= attn12.11@2: 0.0039795637130737305\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "\n",
      "Paths of length 2:\n",
      "===================\n",
      "attn15.0@3 <= mlp6@3 <= mlp1@3: 0.010957837104797363\n",
      "attn15.0@3 <= mlp7@3 <= mlp5@3: 0.009499430656433105\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3: 0.007728219032287598\n",
      "attn15.0@3 <= mlp6@3 <= mlp0@3: 0.005631566047668457\n",
      "attn15.0@3 <= mlp8@3 <= attn7.13@2: 0.005337715148925781\n",
      "attn15.0@3 <= mlp8@3 <= mlp0@3: 0.004730820655822754\n",
      "attn15.0@3 <= mlp6@3 <= attn0.9@3: 0.004168391227722168\n",
      "attn15.0@3 <= mlp8@3 <= mlp2@3: 0.003625035285949707\n",
      "attn15.0@3 <= mlp8@3 <= attn6.3@3: 0.0036143064498901367\n",
      "attn15.0@3 <= mlp8@3 <= attn7.6@2: 0.0033735036849975586\n",
      "attn15.0@3 <= attn0.4@3 <= embedding@3: 0.005913138389587402\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn15.0@3 <= mlp8@3 <= embedding@3: 0.0005955696105957031\n",
      "attn15.0@3 <= attn7.11@3 <= embedding@3: 0.0001150369644165039\n",
      "attn15.0@3 <= attn6.2@3 <= embedding@3: 0.00010979175567626953\n",
      "attn15.0@3 <= attn7.13@2 <= embedding@2: 9.524822235107422e-05\n",
      "attn15.0@3 <= attn12.11@2 <= embedding@2: -3.337860107421875e-05\n",
      "attn15.0@3 <= mlp4@3 <= embedding@3: -0.00011849403381347656\n",
      "attn15.0@3 <= mlp6@3 <= embedding@3: -0.00016617774963378906\n",
      "attn15.0@3 <= mlp7@3 <= embedding@3: -0.00023424625396728516\n",
      "\n",
      "Paths of length 3:\n",
      "===================\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= mlp1@3: 0.002587556838989258\n",
      "attn15.0@3 <= mlp6@3 <= mlp1@3 <= attn0.9@3: 0.0023969411849975586\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= mlp0@3: 0.002078533172607422\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= attn2.7@1: 0.0019501447677612305\n",
      "attn15.0@3 <= mlp6@3 <= mlp1@3 <= attn0.14@3: 0.0017760992050170898\n",
      "attn15.0@3 <= mlp7@3 <= mlp5@3 <= mlp0@3: 0.0017200708389282227\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= attn1.2@3: 0.0014210939407348633\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= attn1.5@3: 0.0013341903686523438\n",
      "attn15.0@3 <= mlp6@3 <= mlp1@3 <= attn0.8@3: 0.0012576580047607422\n",
      "attn15.0@3 <= mlp7@3 <= mlp5@3 <= attn3.11@3: 0.0011749267578125\n",
      "attn15.0@3 <= attn0.4@3 <= embedding@3: 0.005913138389587402\n",
      "attn15.0@3 <= mlp6@3 <= attn0.9@3 <= embedding@3: 0.004168391227722168\n",
      "attn15.0@3 <= embedding@3: 0.0012077093124389648\n",
      "attn15.0@3 <= mlp8@3 <= embedding@3: 0.0005955696105957031\n",
      "attn15.0@3 <= mlp6@3 <= mlp0@3 <= embedding@3: 0.0002936124801635742\n",
      "attn15.0@3 <= mlp6@3 <= mlp1@3 <= embedding@3: 0.00016367435455322266\n",
      "attn15.0@3 <= attn7.11@3 <= embedding@3: 0.0001150369644165039\n",
      "attn15.0@3 <= attn6.2@3 <= embedding@3: 0.00010979175567626953\n",
      "attn15.0@3 <= mlp7@3 <= mlp4@3 <= embedding@3: 9.5367431640625e-05\n",
      "attn15.0@3 <= attn7.13@2 <= embedding@2: 9.524822235107422e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "_ = patch_get_max_paths(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2, 3, 10,\n",
    "    starting_path=[\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 0}\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "43669207-20bd-4eea-a518-3fbdd9eb5737",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_normal_test = get_normal_for_path(model, cur_embeddings_A, cur_embeddings_B, ln_f_normal,\n",
    "    [\n",
    "        #attn17.4@3 <= attn8.4@1 <= attn3.11@0 <= attn0.9@0\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 3, 'head': 0},\n",
    "        {'layer': 0, 'sublayer': 'attn', 'token': 3, 'head': 4},\n",
    "    ], use_ln=False, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "88fdae17-d8c8-44d1-ad48-ae4b05ee6cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs1_normal1 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 4},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal2 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 8, 'head': 14},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal3 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 4, 'head': 5},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal4 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 7, 'sublayer': 'attn', 'token': 8, 'head': 6},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal5 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 16, 'sublayer': 'attn', 'token': 4, 'head': 9},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal6 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 16, 'sublayer': 'mlp', 'token': -1, 'head': -1},\n",
    "        {'layer': 7, 'sublayer': 'attn', 'token': 4, 'head': 11},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal7 = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 17, 'sublayer': 'attn', 'token': 3, 'head': 4},\n",
    "        {'layer': 13, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 6, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 4, 'sublayer': 'mlp', 'token': 3, 'head': -1},\n",
    "        {'layer': 2, 'sublayer': 'attn', 'token': 0, 'head': 12},\n",
    "    ], use_ln=False, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4aa831e8-4894-47ed-ab2c-b542b60f702b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2048])\n"
     ]
    }
   ],
   "source": [
    "obs1_normal5_no_ln = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 16, 'sublayer': 'attn', 'token': 4, 'head': 9},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs1_normal5_ln_real = get_normal_for_path(model, cur_embeddings_A1, cur_embeddings_B1, ln_f_normal1,\n",
    "    [\n",
    "        {'layer': 16, 'sublayer': 'attn', 'token': 4, 'head': 9},\n",
    "    ], use_ln=True, use_ln_real=True, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4cf3b889-26c9-4fa1-b4a2-2c9201475633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993550777435303"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cossim(obs1_normal5_no_ln, obs1_normal5_ln_real).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b0f34d86-3ddb-4998-9b73-42fc6c443242",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs2_normal1 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 15, 'sublayer': 'attn', 'token': 3, 'head': 0},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs2_normal2 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 5, 'sublayer': 'attn', 'token': 3, 'head': 7},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs2_normal3 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 7, 'sublayer': 'attn', 'token': 3, 'head': 2},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs2_normal4 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 19, 'sublayer': 'attn', 'token': 3, 'head': 13},\n",
    "    ], use_ln=False, normalize=True\n",
    ")\n",
    "\n",
    "obs2_normal5 = get_normal_for_path(model, cur_embeddings_A2, cur_embeddings_B2, ln_f_normal2,\n",
    "    [\n",
    "        {'layer': 13, 'sublayer': 'attn', 'token': 6, 'head': 2},\n",
    "        {'layer': 11, 'sublayer': 'mlp', 'token': 6, 'head': -1},\n",
    "        {'layer': 9, 'sublayer': 'attn', 'token': 3, 'head': 7},\n",
    "    ], use_ln=False, normalize=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959c93a9-6e0b-438d-b369-bf5e936a7ac8",
   "metadata": {},
   "source": [
    "## Set up Python vs C dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a7497dc3-cf6c-42a3-92bd-9e0aa531fbf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_dataset = load_dataset(\"andstor/the_pile_github\", 'c', streaming=True)\n",
    "python_dataset = load_dataset(\"andstor/the_pile_github\", 'python', streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "e12b8da2-ac93-4f32-aeaa-4a47856ec851",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 111/220 [00:16<00:16,  6.76it/s]"
     ]
    }
   ],
   "source": [
    "python_rows = []\n",
    "c_rows = []\n",
    "\n",
    "rows_num = 110\n",
    "pbar = tqdm.tqdm(total=2*rows_num)\n",
    "for row in python_dataset['train']:\n",
    "    if len(python_rows) < rows_num:\n",
    "        python_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(python_rows) >= rows_num: break\n",
    "for row in c_dataset['train']:\n",
    "    if len(c_rows) < rows_num:\n",
    "        c_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(c_rows) >= rows_num: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "340814d9-89d1-4a5d-87c8-8fe166fd5ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_tokenized_prompts = []\n",
    "for prompt in c_rows:\n",
    "    c_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "c_tokenized_prompts = slice_tokenized_prompts_into_context_windows(c_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "46bf8355-01f9-4098-9d36-05c7eba7075d",
   "metadata": {},
   "outputs": [],
   "source": [
    "python_tokenized_prompts = []\n",
    "for prompt in python_rows:\n",
    "    python_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "python_tokenized_prompts = slice_tokenized_prompts_into_context_windows(python_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ee6440-8f4f-4d91-a137-6c983a3edef9",
   "metadata": {},
   "source": [
    "## Run on dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "e9effbd4-57ea-48c6-9c9d-c1c1d9346e26",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A\n",
      "1it [00:01,  1.25s/it]\u001b[A\n",
      "2it [00:02,  1.23s/it]\u001b[A\n",
      "3it [00:03,  1.21s/it]\u001b[A\n",
      "4it [00:04,  1.18s/it]\u001b[A\n",
      "5it [00:06,  1.19s/it]\u001b[A\n",
      "6it [00:07,  1.20s/it]\u001b[A\n",
      "7it [00:08,  1.20s/it]\u001b[A\n",
      "8it [00:09,  1.20s/it]\u001b[A\n",
      "9it [00:10,  1.21s/it]\u001b[A\n",
      "10it [00:12,  1.21s/it]\u001b[A\n",
      "11it [00:13,  1.21s/it]\u001b[A\n",
      "12it [00:14,  1.20s/it]\u001b[A\n",
      "13it [00:15,  1.20s/it]\u001b[A\n",
      "14it [00:16,  1.20s/it]\u001b[A\n",
      "15it [00:18,  1.20s/it]\u001b[A\n",
      "16it [00:19,  1.20s/it]\u001b[A\n",
      "17it [00:20,  1.25s/it]\u001b[A\n",
      "18it [00:21,  1.23s/it]\u001b[A\n",
      "19it [00:23,  1.22s/it]\u001b[A\n",
      "20it [00:24,  1.22s/it]\u001b[A\n",
      "21it [00:25,  1.21s/it]\u001b[A\n",
      "22it [00:26,  1.21s/it]\u001b[A\n",
      "23it [00:27,  1.21s/it]\u001b[A\n",
      "24it [00:29,  1.21s/it]\u001b[A\n",
      "25it [00:30,  1.21s/it]\u001b[A\n",
      "26it [00:31,  1.18s/it]\u001b[A\n",
      "27it [00:32,  1.19s/it]\u001b[A\n",
      "28it [00:33,  1.20s/it]\u001b[A\n",
      "29it [00:35,  1.21s/it]\u001b[A\n",
      "30it [00:36,  1.21s/it]\u001b[A\n",
      "31it [00:37,  1.23s/it]\u001b[A\n",
      "32it [00:38,  1.19s/it]\u001b[A\n",
      "33it [00:39,  1.21s/it]\u001b[A\n",
      "34it [00:41,  1.18s/it]\u001b[A\n",
      "35it [00:42,  1.21s/it]\u001b[A\n",
      "36it [00:43,  1.22s/it]\u001b[A\n",
      "37it [00:44,  1.24s/it]\u001b[A\n",
      "38it [00:46,  1.24s/it]\u001b[A\n",
      "39it [00:47,  1.25s/it]\u001b[A\n",
      "40it [00:48,  1.25s/it]\u001b[A\n",
      "41it [00:49,  1.25s/it]\u001b[A\n",
      "42it [00:51,  1.26s/it]\u001b[A\n",
      "43it [00:52,  1.27s/it]\u001b[A\n",
      "44it [00:53,  1.26s/it]\u001b[A\n",
      "45it [00:54,  1.26s/it]\u001b[A\n",
      "46it [00:56,  1.26s/it]\u001b[A\n",
      "47it [00:57,  1.26s/it]\u001b[A\n",
      "48it [00:58,  1.26s/it]\u001b[A\n",
      "49it [00:59,  1.26s/it]\u001b[A\n",
      "50it [01:01,  1.26s/it]\u001b[A\n",
      "51it [01:02,  1.27s/it]\u001b[A\n",
      "52it [01:03,  1.26s/it]\u001b[A\n",
      "53it [01:04,  1.26s/it]\u001b[A\n",
      "54it [01:06,  1.26s/it]\u001b[A\n",
      "55it [01:07,  1.26s/it]\u001b[A\n",
      "56it [01:08,  1.26s/it]\u001b[A\n",
      "57it [01:10,  1.26s/it]\u001b[A\n",
      "58it [01:11,  1.26s/it]\u001b[A\n",
      "59it [01:12,  1.27s/it]\u001b[A\n",
      "60it [01:13,  1.26s/it]\u001b[A\n",
      "61it [01:15,  1.26s/it]\u001b[A\n",
      "62it [01:16,  1.27s/it]\u001b[A\n",
      "63it [01:17,  1.27s/it]\u001b[A\n",
      "64it [01:18,  1.26s/it]\u001b[A\n",
      "65it [01:20,  1.24s/it]\u001b[A\n",
      "66it [01:21,  1.25s/it]\u001b[A\n",
      "67it [01:22,  1.26s/it]\u001b[A\n",
      "68it [01:23,  1.26s/it]\u001b[A\n",
      "69it [01:25,  1.29s/it]\u001b[A\n",
      "70it [01:26,  1.27s/it]\u001b[A\n",
      "71it [01:27,  1.25s/it]\u001b[A\n",
      "72it [01:28,  1.24s/it]\u001b[A\n",
      "73it [01:30,  1.23s/it]\u001b[A\n",
      "74it [01:31,  1.22s/it]\u001b[A\n",
      "75it [01:32,  1.21s/it]\u001b[A\n",
      "76it [01:33,  1.21s/it]\u001b[A\n",
      "77it [01:34,  1.21s/it]\u001b[A\n",
      "78it [01:36,  1.22s/it]\u001b[A\n",
      "79it [01:37,  1.21s/it]\u001b[A\n",
      "80it [01:38,  1.21s/it]\u001b[A\n",
      "81it [01:39,  1.21s/it]\u001b[A\n",
      "82it [01:40,  1.21s/it]\u001b[A\n",
      "83it [01:42,  1.21s/it]\u001b[A\n",
      "84it [01:43,  1.21s/it]\u001b[A\n",
      "85it [01:44,  1.20s/it]\u001b[A\n",
      "86it [01:45,  1.21s/it]\u001b[A\n",
      "87it [01:46,  1.21s/it]\u001b[A\n",
      "88it [01:48,  1.21s/it]\u001b[A\n",
      "89it [01:49,  1.21s/it]\u001b[A\n",
      "90it [01:50,  1.21s/it]\u001b[A\n",
      "91it [01:51,  1.21s/it]\u001b[A\n",
      "92it [01:53,  1.21s/it]\u001b[A\n",
      "93it [01:54,  1.21s/it]\u001b[A\n",
      "94it [01:55,  1.20s/it]\u001b[A\n",
      "95it [01:56,  1.21s/it]\u001b[A\n",
      "96it [01:57,  1.21s/it]\u001b[A\n",
      "97it [01:59,  1.21s/it]\u001b[A\n",
      "98it [02:00,  1.21s/it]\u001b[A\n",
      "99it [02:01,  1.21s/it]\u001b[A\n",
      "100it [02:02,  1.20s/it]\u001b[A\n",
      "101it [02:03,  1.21s/it]\u001b[A\n",
      "102it [02:05,  1.21s/it]\u001b[A\n",
      "103it [02:06,  1.21s/it]\u001b[A\n",
      "104it [02:07,  1.21s/it]\u001b[A\n",
      "105it [02:08,  1.21s/it]\u001b[A\n",
      "106it [02:09,  1.21s/it]\u001b[A\n",
      "107it [02:11,  1.21s/it]\u001b[A\n",
      "108it [02:12,  1.20s/it]\u001b[A\n",
      "109it [02:13,  1.20s/it]\u001b[A\n",
      "110it [02:14,  1.20s/it]\u001b[A\n",
      "111it [02:15,  1.21s/it]\u001b[A\n",
      "112it [02:17,  1.22s/it]\u001b[A\n",
      "113it [02:18,  1.21s/it]\u001b[A\n",
      "114it [02:19,  1.21s/it]\u001b[A\n",
      "115it [02:20,  1.21s/it]\u001b[A\n",
      "116it [02:22,  1.21s/it]\u001b[A\n",
      "117it [02:23,  1.20s/it]\u001b[A\n",
      "118it [02:24,  1.20s/it]\u001b[A\n",
      "119it [02:25,  1.20s/it]\u001b[A\n",
      "120it [02:26,  1.21s/it]\u001b[A\n",
      "121it [02:28,  1.21s/it]\u001b[A\n",
      "122it [02:29,  1.21s/it]\u001b[A\n",
      "123it [02:30,  1.21s/it]\u001b[A\n",
      "124it [02:31,  1.21s/it]\u001b[A\n",
      "125it [02:32,  1.20s/it]\u001b[A\n",
      "126it [02:34,  1.20s/it]\u001b[A\n",
      "127it [02:35,  1.20s/it]\u001b[A\n",
      "128it [02:36,  1.20s/it]\u001b[A\n",
      "129it [02:37,  1.22s/it]\u001b[A\n",
      "130it [02:38,  1.21s/it]\u001b[A\n",
      "131it [02:40,  1.21s/it]\u001b[A\n",
      "132it [02:41,  1.21s/it]\u001b[A\n",
      "133it [02:42,  1.21s/it]\u001b[A\n",
      "134it [02:43,  1.21s/it]\u001b[A\n",
      "135it [02:44,  1.20s/it]\u001b[A\n",
      "136it [02:46,  1.20s/it]\u001b[A\n",
      "137it [02:47,  1.21s/it]\u001b[A\n",
      "138it [02:48,  1.21s/it]\u001b[A\n",
      "139it [02:49,  1.21s/it]\u001b[A\n",
      "140it [02:51,  1.21s/it]\u001b[A\n",
      "141it [02:52,  1.21s/it]\u001b[A\n",
      "142it [02:53,  1.21s/it]\u001b[A\n",
      "143it [02:54,  1.21s/it]\u001b[A\n",
      "144it [02:55,  1.21s/it]\u001b[A\n",
      "145it [02:57,  1.20s/it]\u001b[A\n",
      "146it [02:58,  1.21s/it]\u001b[A\n",
      "147it [02:59,  1.21s/it]\u001b[A\n",
      "148it [03:00,  1.21s/it]\u001b[A\n",
      "149it [03:01,  1.21s/it]\u001b[A\n",
      "150it [03:03,  1.20s/it]\u001b[A\n",
      "151it [03:04,  1.21s/it]\u001b[A\n",
      "152it [03:05,  1.21s/it]\u001b[A\n",
      "153it [03:06,  1.21s/it]\u001b[A\n",
      "154it [03:07,  1.22s/it]\u001b[A\n",
      "155it [03:09,  1.21s/it]\u001b[A\n",
      "156it [03:10,  1.21s/it]\u001b[A\n",
      "157it [03:11,  1.21s/it]\u001b[A\n",
      "158it [03:12,  1.21s/it]\u001b[A\n",
      "159it [03:13,  1.20s/it]\u001b[A\n",
      "160it [03:15,  1.20s/it]\u001b[A\n",
      "161it [03:16,  1.21s/it]\u001b[A\n",
      "162it [03:17,  1.21s/it]\u001b[A\n",
      "163it [03:18,  1.22s/it]\u001b[A\n",
      "164it [03:20,  1.21s/it]\u001b[A\n",
      "165it [03:21,  1.21s/it]\u001b[A\n",
      "166it [03:22,  1.21s/it]\u001b[A\n",
      "167it [03:23,  1.21s/it]\u001b[A\n",
      "168it [03:24,  1.20s/it]\u001b[A\n",
      "169it [03:26,  1.23s/it]\u001b[A\n",
      "170it [03:27,  1.22s/it]\u001b[A\n",
      "171it [03:28,  1.22s/it]\u001b[A\n",
      "172it [03:29,  1.22s/it]\u001b[A\n",
      "173it [03:30,  1.21s/it]\u001b[A\n",
      "174it [03:32,  1.21s/it]\u001b[A\n",
      "175it [03:33,  1.21s/it]\u001b[A\n",
      "176it [03:34,  1.21s/it]\u001b[A\n",
      "177it [03:35,  1.21s/it]\u001b[A\n",
      "178it [03:36,  1.21s/it]\u001b[A\n",
      "179it [03:38,  1.21s/it]\u001b[A\n",
      "180it [03:39,  1.21s/it]\u001b[A\n",
      "181it [03:40,  1.21s/it]\u001b[A\n",
      "182it [03:41,  1.21s/it]\u001b[A\n",
      "183it [03:43,  1.21s/it]\u001b[A\n",
      "184it [03:44,  1.21s/it]\u001b[A\n",
      "185it [03:45,  1.20s/it]\u001b[A\n",
      "186it [03:46,  1.21s/it]\u001b[A\n",
      "187it [03:47,  1.21s/it]\u001b[A\n",
      "188it [03:49,  1.21s/it]\u001b[A\n",
      "189it [03:50,  1.21s/it]\u001b[A\n",
      "190it [03:51,  1.21s/it]\u001b[A\n",
      "191it [03:52,  1.21s/it]\u001b[A\n",
      "192it [03:53,  1.21s/it]\u001b[A\n",
      "193it [03:55,  1.21s/it]\u001b[A\n",
      "194it [03:56,  1.21s/it]\u001b[A\n",
      "195it [03:57,  1.21s/it]\u001b[A\n",
      "196it [03:58,  1.21s/it]\u001b[A\n",
      "197it [03:59,  1.21s/it]\u001b[A\n",
      "198it [04:01,  1.21s/it]\u001b[A\n",
      "199it [04:02,  1.21s/it]\u001b[A\n",
      "200it [04:03,  1.20s/it]\u001b[A\n",
      "201it [04:04,  1.21s/it]\u001b[A\n",
      "202it [04:05,  1.21s/it]\u001b[A\n",
      "203it [04:07,  1.21s/it]\u001b[A\n",
      "204it [04:08,  1.21s/it]\u001b[A\n",
      "205it [04:09,  1.21s/it]\u001b[A\n",
      "206it [04:10,  1.21s/it]\u001b[A\n",
      "207it [04:12,  1.21s/it]\u001b[A\n",
      "208it [04:13,  1.21s/it]\u001b[A\n",
      "209it [04:14,  1.21s/it]\u001b[A\n",
      "210it [04:15,  1.20s/it]\u001b[A\n",
      "211it [04:16,  1.21s/it]\u001b[A\n",
      "212it [04:18,  1.21s/it]\u001b[A\n",
      "213it [04:19,  1.21s/it]\u001b[A\n",
      "214it [04:20,  1.21s/it]\u001b[A\n",
      "215it [04:21,  1.21s/it]\u001b[A\n",
      "216it [04:22,  1.21s/it]\u001b[A\n",
      "217it [04:24,  1.21s/it]\u001b[A\n",
      "218it [04:25,  1.20s/it]\u001b[A\n",
      "219it [04:26,  1.20s/it]\u001b[A\n",
      "220it [04:27,  1.21s/it]\u001b[A\n",
      "221it [04:28,  1.22s/it]\u001b[A\n",
      "222it [04:30,  1.21s/it]\u001b[A\n",
      "223it [04:31,  1.21s/it]\u001b[A\n",
      "224it [04:32,  1.21s/it]\u001b[A\n",
      "225it [04:33,  1.21s/it]\u001b[A\n",
      "226it [04:34,  1.21s/it]\u001b[A\n",
      "227it [04:36,  1.21s/it]\u001b[A\n",
      "228it [04:37,  1.20s/it]\u001b[A\n",
      "229it [04:38,  1.21s/it]\u001b[A\n",
      "230it [04:39,  1.21s/it]\u001b[A\n",
      "231it [04:41,  1.21s/it]\u001b[A\n",
      "232it [04:42,  1.21s/it]\u001b[A\n",
      "233it [04:43,  1.21s/it]\u001b[A\n",
      "234it [04:44,  1.21s/it]\u001b[A\n",
      "235it [04:45,  1.21s/it]\u001b[A\n",
      "236it [04:47,  1.20s/it]\u001b[A\n",
      "237it [04:48,  1.21s/it]\u001b[A\n",
      "238it [04:49,  1.21s/it]\u001b[A\n",
      "239it [04:50,  1.22s/it]\u001b[A\n",
      "240it [04:51,  1.22s/it]\u001b[A\n",
      "241it [04:53,  1.24s/it]\u001b[A\n",
      "242it [04:54,  1.24s/it]\u001b[A\n",
      "243it [04:55,  1.25s/it]\u001b[A\n",
      "244it [04:57,  1.25s/it]\u001b[A\n",
      "245it [04:58,  1.26s/it]\u001b[A\n",
      "246it [04:59,  1.27s/it]\u001b[A\n",
      "247it [05:00,  1.26s/it]\u001b[A\n",
      "248it [05:02,  1.26s/it]\u001b[A\n",
      "249it [05:03,  1.26s/it]\u001b[A\n",
      "250it [05:04,  1.26s/it]\u001b[A\n",
      "251it [05:05,  1.27s/it]\u001b[A\n",
      "252it [05:07,  1.27s/it]\u001b[A\n",
      "253it [05:08,  1.27s/it]\u001b[A\n",
      "254it [05:09,  1.27s/it]\u001b[A\n",
      "255it [05:10,  1.27s/it]\u001b[A\n",
      "256it [05:12,  1.27s/it]\u001b[A\n",
      "257it [05:13,  1.27s/it]\u001b[A\n",
      "258it [05:14,  1.26s/it]\u001b[A\n",
      "259it [05:16,  1.26s/it]\u001b[A\n",
      "260it [05:17,  1.26s/it]\u001b[A\n",
      "261it [05:18,  1.28s/it]\u001b[A\n",
      "262it [05:19,  1.27s/it]\u001b[A\n",
      "263it [05:21,  1.27s/it]\u001b[A\n",
      "264it [05:22,  1.26s/it]\u001b[A\n",
      "265it [05:23,  1.26s/it]\u001b[A\n",
      "266it [05:24,  1.25s/it]\u001b[A\n",
      "267it [05:26,  1.26s/it]\u001b[A\n",
      "268it [05:27,  1.24s/it]\u001b[A\n",
      "269it [05:28,  1.23s/it]\u001b[A\n",
      "270it [05:29,  1.22s/it]\u001b[A\n",
      "271it [05:30,  1.22s/it]\u001b[A\n",
      "272it [05:32,  1.22s/it]\u001b[A\n",
      "273it [05:33,  1.21s/it]\u001b[A\n",
      "274it [05:34,  1.21s/it]\u001b[A\n",
      "275it [05:35,  1.21s/it]\u001b[A\n",
      "276it [05:36,  1.22s/it]\u001b[A\n",
      "277it [05:38,  1.21s/it]\u001b[A\n",
      "278it [05:39,  1.21s/it]\u001b[A\n",
      "279it [05:40,  1.21s/it]\u001b[A\n",
      "280it [05:41,  1.21s/it]\u001b[A\n",
      "281it [05:43,  1.21s/it]\u001b[A\n",
      "282it [05:44,  1.21s/it]\u001b[A\n",
      "283it [05:45,  1.21s/it]\u001b[A\n",
      "284it [05:46,  1.21s/it]\u001b[A\n",
      "285it [05:47,  1.21s/it]\u001b[A\n",
      "286it [05:49,  1.21s/it]\u001b[A\n",
      "287it [05:50,  1.21s/it]\u001b[A\n",
      "288it [05:51,  1.21s/it]\u001b[A\n",
      "289it [05:52,  1.21s/it]\u001b[A\n",
      "290it [05:53,  1.21s/it]\u001b[A\n",
      "291it [05:55,  1.21s/it]\u001b[A\n",
      "292it [05:56,  1.21s/it]\u001b[A\n",
      "293it [05:57,  1.22s/it]\u001b[A\n",
      "294it [05:58,  1.21s/it]\u001b[A\n",
      "295it [05:59,  1.21s/it]\u001b[A\n",
      "296it [06:01,  1.21s/it]\u001b[A\n",
      "297it [06:02,  1.21s/it]\u001b[A\n",
      "298it [06:03,  1.21s/it]\u001b[A\n",
      "299it [06:04,  1.20s/it]\u001b[A\n",
      "300it [06:05,  1.20s/it]\u001b[A\n",
      "301it [06:07,  1.22s/it]\u001b[A\n",
      "302it [06:08,  1.21s/it]\u001b[A\n",
      "303it [06:09,  1.19s/it]\u001b[A\n",
      "304it [06:10,  1.19s/it]\u001b[A\n",
      "305it [06:11,  1.20s/it]\u001b[A\n",
      "306it [06:13,  1.20s/it]\u001b[A\n",
      "307it [06:14,  1.20s/it]\u001b[A\n",
      "308it [06:15,  1.20s/it]\u001b[A\n",
      "309it [06:16,  1.20s/it]\u001b[A\n",
      "310it [06:18,  1.21s/it]\u001b[A\n",
      "311it [06:19,  1.21s/it]\u001b[A\n",
      "312it [06:20,  1.21s/it]\u001b[A\n",
      "313it [06:21,  1.21s/it]\u001b[A\n",
      "314it [06:22,  1.21s/it]\u001b[A\n",
      "315it [06:24,  1.21s/it]\u001b[A\n",
      "316it [06:25,  1.20s/it]\u001b[A\n",
      "317it [06:26,  1.20s/it]\u001b[A\n",
      "318it [06:27,  1.21s/it]\u001b[A\n",
      "319it [06:28,  1.21s/it]\u001b[A\n",
      "320it [06:30,  1.21s/it]\u001b[A\n",
      "321it [06:31,  1.21s/it]\u001b[A\n",
      "322it [06:32,  1.21s/it]\u001b[A\n",
      "323it [06:33,  1.21s/it]\u001b[A\n",
      "324it [06:34,  1.21s/it]\u001b[A\n",
      "325it [06:36,  1.21s/it]\u001b[A\n",
      "326it [06:37,  1.20s/it]\u001b[A\n",
      "327it [06:38,  1.21s/it]\u001b[A\n",
      "328it [06:39,  1.21s/it]\u001b[A\n",
      "329it [06:41,  1.21s/it]\u001b[A\n",
      "330it [06:42,  1.21s/it]\u001b[A\n",
      "331it [06:43,  1.21s/it]\u001b[A\n",
      "332it [06:44,  1.21s/it]\u001b[A\n",
      "333it [06:45,  1.21s/it]\u001b[A\n",
      "334it [06:47,  1.21s/it]\u001b[A\n",
      "335it [06:48,  1.21s/it]\u001b[A\n",
      "336it [06:49,  1.21s/it]\u001b[A\n",
      "337it [06:50,  1.21s/it]\u001b[A\n",
      "338it [06:51,  1.21s/it]\u001b[A\n",
      "339it [06:53,  1.21s/it]\u001b[A\n",
      "340it [06:54,  1.21s/it]\u001b[A\n",
      "341it [06:55,  1.21s/it]\u001b[A\n",
      "342it [06:56,  1.21s/it]\u001b[A\n",
      "343it [06:57,  1.21s/it]\u001b[A\n",
      "344it [06:59,  1.21s/it]\u001b[A\n",
      "345it [07:00,  1.21s/it]\u001b[A\n",
      "346it [07:01,  1.21s/it]\u001b[A\n",
      "347it [07:02,  1.21s/it]\u001b[A\n",
      "348it [07:03,  1.21s/it]\u001b[A\n",
      "349it [07:05,  1.21s/it]\u001b[A\n",
      "350it [07:06,  1.20s/it]\u001b[A\n",
      "351it [07:07,  1.21s/it]\u001b[A\n",
      "352it [07:08,  1.22s/it]\u001b[A\n",
      "353it [07:10,  1.22s/it]\u001b[A\n",
      "354it [07:11,  1.21s/it]\u001b[A\n",
      "355it [07:12,  1.21s/it]\u001b[A\n",
      "356it [07:13,  1.21s/it]\u001b[A\n",
      "357it [07:14,  1.21s/it]\u001b[A\n",
      "358it [07:16,  1.21s/it]\u001b[A\n",
      "359it [07:17,  1.20s/it]\u001b[A\n",
      "360it [07:18,  1.20s/it]\u001b[A\n",
      "361it [07:19,  1.22s/it]\u001b[A\n",
      "362it [07:20,  1.22s/it]\u001b[A\n",
      "363it [07:22,  1.21s/it]\u001b[A\n",
      "364it [07:23,  1.21s/it]\u001b[A\n",
      "365it [07:24,  1.21s/it]\u001b[A\n",
      "366it [07:25,  1.21s/it]\u001b[A\n",
      "367it [07:27,  1.23s/it]\u001b[A\n",
      "368it [07:28,  1.22s/it]\u001b[A\n",
      "369it [07:29,  1.22s/it]\u001b[A\n",
      "370it [07:30,  1.21s/it]\u001b[A\n",
      "371it [07:31,  1.22s/it]\u001b[A\n",
      "372it [07:33,  1.22s/it]\u001b[A\n",
      "373it [07:34,  1.21s/it]\u001b[A\n",
      "374it [07:35,  1.21s/it]\u001b[A\n",
      "375it [07:36,  1.22s/it]\u001b[A\n",
      "376it [07:37,  1.21s/it]\u001b[A\n",
      "377it [07:39,  1.21s/it]\u001b[A\n",
      "378it [07:40,  1.21s/it]\u001b[A\n",
      "379it [07:41,  1.21s/it]\u001b[A\n",
      "380it [07:42,  1.21s/it]\u001b[A\n",
      "381it [07:43,  1.21s/it]\u001b[A\n",
      "382it [07:45,  1.21s/it]\u001b[A\n",
      "383it [07:46,  1.21s/it]\u001b[A\n",
      "384it [07:47,  1.22s/it]\u001b[A\n",
      "385it [07:48,  1.21s/it]\u001b[A\n",
      "386it [07:50,  1.21s/it]\u001b[A\n",
      "387it [07:51,  1.21s/it]\u001b[A\n",
      "388it [07:52,  1.21s/it]\u001b[A\n",
      "389it [07:53,  1.21s/it]\u001b[A\n",
      "390it [07:54,  1.21s/it]\u001b[A\n",
      "391it [07:56,  1.21s/it]\u001b[A\n",
      "392it [07:57,  1.22s/it]\u001b[A\n",
      "393it [07:58,  1.21s/it]\u001b[A\n",
      "394it [07:59,  1.21s/it]\u001b[A\n",
      "395it [08:00,  1.21s/it]\u001b[A\n",
      "396it [08:02,  1.21s/it]\u001b[A\n",
      "397it [08:03,  1.21s/it]\u001b[A\n",
      "398it [08:04,  1.21s/it]\u001b[A\n",
      "399it [08:05,  1.20s/it]\u001b[A\n",
      "400it [08:06,  1.20s/it]\u001b[A\n",
      "401it [08:08,  1.22s/it]\u001b[A\n",
      "402it [08:09,  1.22s/it]\u001b[A\n",
      "403it [08:10,  1.22s/it]\u001b[A\n",
      "404it [08:11,  1.23s/it]\u001b[A\n",
      "405it [08:13,  1.24s/it]\u001b[A\n",
      "406it [08:14,  1.24s/it]\u001b[A\n",
      "407it [08:15,  1.25s/it]\u001b[A\n",
      "408it [08:16,  1.25s/it]\u001b[A\n",
      "409it [08:18,  1.26s/it]\u001b[A\n",
      "410it [08:19,  1.26s/it]\u001b[A\n",
      "411it [08:20,  1.27s/it]\u001b[A\n",
      "412it [08:21,  1.25s/it]\u001b[A\n",
      "413it [08:23,  1.24s/it]\u001b[A\n",
      "414it [08:24,  1.23s/it]\u001b[A\n",
      "415it [08:25,  1.22s/it]\u001b[A\n",
      "416it [08:26,  1.21s/it]\u001b[A\n",
      "417it [08:28,  1.22s/it]\u001b[A\n",
      "418it [08:29,  1.22s/it]\u001b[A\n",
      "419it [08:30,  1.21s/it]\u001b[A\n",
      "420it [08:31,  1.21s/it]\u001b[A\n",
      "421it [08:32,  1.22s/it]\u001b[A\n",
      "422it [08:34,  1.21s/it]\u001b[A\n",
      "423it [08:35,  1.21s/it]\u001b[A\n",
      "424it [08:36,  1.21s/it]\u001b[A\n",
      "425it [08:37,  1.21s/it]\u001b[A\n",
      "426it [08:38,  1.22s/it]\u001b[A\n",
      "427it [08:40,  1.21s/it]\u001b[A\n",
      "428it [08:41,  1.21s/it]\u001b[A\n",
      "429it [08:42,  1.21s/it]\u001b[A\n",
      "430it [08:43,  1.21s/it]\u001b[A\n",
      "431it [08:44,  1.21s/it]\u001b[A\n",
      "432it [08:46,  1.21s/it]\u001b[A\n",
      "433it [08:47,  1.21s/it]\u001b[A\n",
      "434it [08:48,  1.22s/it]\u001b[A\n",
      "435it [08:49,  1.21s/it]\u001b[A\n",
      "436it [08:51,  1.21s/it]\u001b[A\n",
      "437it [08:52,  1.21s/it]\u001b[A\n",
      "438it [08:53,  1.21s/it]\u001b[A\n",
      "439it [08:54,  1.21s/it]\u001b[A\n",
      "440it [08:55,  1.21s/it]\u001b[A\n",
      "441it [08:57,  1.22s/it]\u001b[A\n",
      "442it [08:58,  1.21s/it]\u001b[A\n",
      "443it [08:59,  1.22s/it]\u001b[A\n",
      "444it [09:00,  1.21s/it]\u001b[A\n",
      "445it [09:01,  1.21s/it]\u001b[A\n",
      "446it [09:03,  1.21s/it]\u001b[A\n",
      "447it [09:04,  1.21s/it]\u001b[A\n",
      "448it [09:05,  1.21s/it]\u001b[A\n",
      "449it [09:06,  1.20s/it]\u001b[A\n",
      "450it [09:07,  1.20s/it]\u001b[A\n",
      "451it [09:09,  1.21s/it]\u001b[A\n",
      "452it [09:10,  1.21s/it]\u001b[A\n",
      "453it [09:11,  1.21s/it]\u001b[A\n",
      "454it [09:12,  1.21s/it]\u001b[A\n",
      "455it [09:13,  1.21s/it]\u001b[A\n",
      "456it [09:15,  1.21s/it]\u001b[A\n",
      "457it [09:16,  1.21s/it]\u001b[A\n",
      "458it [09:17,  1.20s/it]\u001b[A\n",
      "459it [09:18,  1.20s/it]\u001b[A\n",
      "460it [09:20,  1.20s/it]\u001b[A\n",
      "461it [09:21,  1.21s/it]\u001b[A\n",
      "462it [09:22,  1.21s/it]\u001b[A\n",
      "463it [09:23,  1.21s/it]\u001b[A\n",
      "464it [09:24,  1.21s/it]\u001b[A\n",
      "465it [09:26,  1.21s/it]\u001b[A\n",
      "466it [09:27,  1.22s/it]\u001b[A\n",
      "467it [09:28,  1.22s/it]\u001b[A\n",
      "468it [09:29,  1.21s/it]\u001b[A\n",
      "469it [09:30,  1.21s/it]\u001b[A\n",
      "470it [09:32,  1.21s/it]\u001b[A\n",
      "471it [09:33,  1.22s/it]\u001b[A\n",
      "472it [09:34,  1.21s/it]\u001b[A\n",
      "473it [09:35,  1.21s/it]\u001b[A\n",
      "474it [09:36,  1.21s/it]\u001b[A\n",
      "475it [09:38,  1.21s/it]\u001b[A\n",
      "476it [09:39,  1.21s/it]\u001b[A\n",
      "477it [09:40,  1.21s/it]\u001b[A\n",
      "478it [09:41,  1.21s/it]\u001b[A\n",
      "479it [09:43,  1.20s/it]\u001b[A\n",
      "480it [09:44,  1.20s/it]\u001b[A\n",
      "481it [09:45,  1.21s/it]\u001b[A\n",
      "482it [09:46,  1.21s/it]\u001b[A\n",
      "483it [09:47,  1.21s/it]\u001b[A\n",
      "484it [09:49,  1.21s/it]\u001b[A\n",
      "485it [09:50,  1.21s/it]\u001b[A\n",
      "486it [09:51,  1.21s/it]\u001b[A\n",
      "487it [09:52,  1.21s/it]\u001b[A\n",
      "488it [09:53,  1.20s/it]\u001b[A\n",
      "489it [09:55,  1.20s/it]\u001b[A\n",
      "490it [09:56,  1.20s/it]\u001b[A\n",
      "491it [09:57,  1.21s/it]\u001b[A\n",
      "492it [09:58,  1.21s/it]\u001b[A\n",
      "493it [09:59,  1.21s/it]\u001b[A\n",
      "494it [10:01,  1.21s/it]\u001b[A\n",
      "495it [10:02,  1.21s/it]\u001b[A\n",
      "496it [10:03,  1.20s/it]\u001b[A\n",
      "497it [10:04,  1.20s/it]\u001b[A\n",
      "498it [10:05,  1.20s/it]\u001b[A\n",
      "499it [10:07,  1.20s/it]\u001b[A\n",
      "500it [10:08,  1.20s/it]\u001b[A\n",
      "501it [10:09,  1.21s/it]\u001b[A\n",
      "502it [10:10,  1.21s/it]\u001b[A\n",
      "503it [10:11,  1.21s/it]\u001b[A\n",
      "504it [10:13,  1.21s/it]\u001b[A\n",
      "505it [10:14,  1.21s/it]\u001b[A\n",
      "506it [10:15,  1.21s/it]\u001b[A\n",
      "507it [10:16,  1.21s/it]\u001b[A\n",
      "508it [10:18,  1.20s/it]\u001b[A\n",
      "509it [10:19,  1.21s/it]\u001b[A\n",
      "510it [10:20,  1.21s/it]\u001b[A\n",
      "511it [10:21,  1.22s/it]\u001b[A\n",
      "512it [10:22,  1.21s/it]\u001b[A\n",
      "513it [10:24,  1.21s/it]\u001b[A\n",
      "514it [10:25,  1.21s/it]\u001b[A\n",
      "515it [10:26,  1.21s/it]\u001b[A\n",
      "516it [10:27,  1.21s/it]\u001b[A\n",
      "517it [10:28,  1.21s/it]\u001b[A\n",
      "518it [10:30,  1.23s/it]\u001b[A\n",
      "519it [10:31,  1.24s/it]\u001b[A\n",
      "520it [10:32,  1.25s/it]\u001b[A\n",
      "521it [10:34,  1.26s/it]\u001b[A\n",
      "522it [10:35,  1.26s/it]\u001b[A\n",
      "523it [10:36,  1.26s/it]\u001b[A\n",
      "524it [10:37,  1.26s/it]\u001b[A\n",
      "525it [10:39,  1.27s/it]\u001b[A\n",
      "526it [10:40,  1.27s/it]\u001b[A\n",
      "527it [10:41,  1.27s/it]\u001b[A\n",
      "528it [10:42,  1.27s/it]\u001b[A\n",
      "529it [10:44,  1.27s/it]\u001b[A\n",
      "530it [10:45,  1.26s/it]\u001b[A\n",
      "531it [10:46,  1.27s/it]\u001b[A\n",
      "532it [10:47,  1.27s/it]\u001b[A\n",
      "533it [10:49,  1.28s/it]\u001b[A\n",
      "534it [10:50,  1.27s/it]\u001b[A\n",
      "535it [10:51,  1.27s/it]\u001b[A\n",
      "536it [10:53,  1.27s/it]\u001b[A\n",
      "537it [10:54,  1.26s/it]\u001b[A\n",
      "538it [10:55,  1.26s/it]\u001b[A\n",
      "539it [10:56,  1.26s/it]\u001b[A\n",
      "540it [10:58,  1.26s/it]\u001b[A\n",
      "541it [10:59,  1.28s/it]\u001b[A\n",
      "542it [11:00,  1.27s/it]\u001b[A\n",
      "543it [11:01,  1.27s/it]\u001b[A\n",
      "544it [11:03,  1.27s/it]\u001b[A\n",
      "545it [11:04,  1.27s/it]\u001b[A\n",
      "546it [11:05,  1.26s/it]\u001b[A\n",
      "547it [11:06,  1.26s/it]\u001b[A\n",
      "548it [11:08,  1.26s/it]\u001b[A\n",
      "549it [11:09,  1.27s/it]\u001b[A\n",
      "550it [11:10,  1.25s/it]\u001b[A\n",
      "551it [11:11,  1.24s/it]\u001b[A\n",
      "552it [11:13,  1.23s/it]\u001b[A\n",
      "553it [11:14,  1.23s/it]\u001b[A\n",
      "554it [11:15,  1.22s/it]\u001b[A\n",
      "555it [11:16,  1.21s/it]\u001b[A\n",
      "556it [11:17,  1.21s/it]\u001b[A\n",
      "557it [11:19,  1.21s/it]\u001b[A\n",
      "558it [11:20,  1.22s/it]\u001b[A\n",
      "559it [11:21,  1.21s/it]\u001b[A\n",
      "560it [11:22,  1.21s/it]\u001b[A\n",
      "561it [11:24,  1.22s/it]\u001b[A\n",
      "562it [11:25,  1.22s/it]\u001b[A\n",
      "563it [11:26,  1.21s/it]\u001b[A\n",
      "564it [11:27,  1.23s/it]\u001b[A\n",
      "565it [11:28,  1.23s/it]\u001b[A\n",
      "566it [11:30,  1.22s/it]\u001b[A\n",
      "567it [11:31,  1.21s/it]\u001b[A\n",
      "568it [11:32,  1.21s/it]\u001b[A\n",
      "569it [11:33,  1.21s/it]\u001b[A\n",
      "570it [11:34,  1.21s/it]\u001b[A\n",
      "571it [11:36,  1.22s/it]\u001b[A\n",
      "572it [11:37,  1.22s/it]\u001b[A\n",
      "573it [11:38,  1.22s/it]\u001b[A\n",
      "574it [11:39,  1.22s/it]\u001b[A\n",
      "575it [11:41,  1.21s/it]\u001b[A\n",
      "576it [11:42,  1.21s/it]\u001b[A\n",
      "577it [11:43,  1.21s/it]\u001b[A\n",
      "578it [11:44,  1.21s/it]\u001b[A\n",
      "579it [11:45,  1.21s/it]\u001b[A\n",
      "580it [11:47,  1.21s/it]\u001b[A\n",
      "581it [11:48,  1.22s/it]\u001b[A\n",
      "582it [11:49,  1.22s/it]\u001b[A\n",
      "583it [11:50,  1.21s/it]\u001b[A\n",
      "584it [11:51,  1.21s/it]\u001b[A\n",
      "585it [11:53,  1.21s/it]\u001b[A\n",
      "586it [11:54,  1.22s/it]\u001b[A\n",
      "587it [11:55,  1.23s/it]\u001b[A\n",
      "588it [11:56,  1.24s/it]\u001b[A\n",
      "589it [11:58,  1.22s/it]\u001b[A\n",
      "590it [11:59,  1.24s/it]\u001b[A\n",
      "591it [12:00,  1.26s/it]\u001b[A\n",
      "592it [12:01,  1.25s/it]\u001b[A\n",
      "593it [12:03,  1.26s/it]\u001b[A\n",
      "594it [12:04,  1.26s/it]\u001b[A\n",
      "595it [12:05,  1.24s/it]\u001b[A\n",
      "596it [12:06,  1.20s/it]\u001b[A\n",
      "597it [12:07,  1.20s/it]\u001b[A\n",
      "598it [12:09,  1.21s/it]\u001b[A\n",
      "599it [12:10,  1.21s/it]\u001b[A\n",
      "600it [12:11,  1.21s/it]\u001b[A\n",
      "601it [12:12,  1.22s/it]\u001b[A\n",
      "602it [12:14,  1.21s/it]\u001b[A\n",
      "603it [12:15,  1.21s/it]\u001b[A\n",
      "604it [12:16,  1.21s/it]\u001b[A\n",
      "605it [12:17,  1.21s/it]\u001b[A\n",
      "606it [12:18,  1.22s/it]\u001b[A\n",
      "607it [12:20,  1.21s/it]\u001b[A\n",
      "608it [12:21,  1.21s/it]\u001b[A\n",
      "609it [12:22,  1.21s/it]\u001b[A\n",
      "610it [12:23,  1.21s/it]\u001b[A\n",
      "611it [12:24,  1.22s/it]\u001b[A\n",
      "612it [12:26,  1.22s/it]\u001b[A\n",
      "613it [12:27,  1.21s/it]\u001b[A\n",
      "614it [12:28,  1.21s/it]\u001b[A\n",
      "615it [12:29,  1.22s/it]\u001b[A\n",
      "616it [12:31,  1.21s/it]\u001b[A\n",
      "617it [12:32,  1.21s/it]\u001b[A\n",
      "618it [12:33,  1.21s/it]\u001b[A\n",
      "619it [12:34,  1.21s/it]\u001b[A\n",
      "620it [12:35,  1.21s/it]\u001b[A\n",
      "621it [12:37,  1.22s/it]\u001b[A\n",
      "622it [12:38,  1.22s/it]\u001b[A\n",
      "623it [12:39,  1.22s/it]\u001b[A\n",
      "624it [12:40,  1.22s/it]\u001b[A\n",
      "625it [12:41,  1.21s/it]\u001b[A\n",
      "626it [12:43,  1.21s/it]\u001b[A\n",
      "627it [12:44,  1.21s/it]\u001b[A\n",
      "628it [12:45,  1.21s/it]\u001b[A\n",
      "629it [12:46,  1.21s/it]\u001b[A\n",
      "630it [12:47,  1.21s/it]\u001b[A\n",
      "631it [12:49,  1.22s/it]\u001b[A\n",
      "632it [12:50,  1.22s/it]\u001b[A\n",
      "633it [12:51,  1.22s/it]\u001b[A\n",
      "634it [12:52,  1.21s/it]\u001b[A\n",
      "635it [12:54,  1.21s/it]\u001b[A\n",
      "636it [12:55,  1.21s/it]\u001b[A\n",
      "637it [12:56,  1.21s/it]\u001b[A\n",
      "638it [12:57,  1.21s/it]\u001b[A\n",
      "639it [12:58,  1.21s/it]\u001b[A\n",
      "640it [13:00,  1.22s/it]\u001b[A\n",
      "641it [13:01,  1.22s/it]\u001b[A\n",
      "642it [13:02,  1.22s/it]\u001b[A\n",
      "643it [13:03,  1.21s/it]\u001b[A\n",
      "644it [13:04,  1.21s/it]\u001b[A\n",
      "645it [13:06,  1.21s/it]\u001b[A\n",
      "646it [13:07,  1.21s/it]\u001b[A\n",
      "647it [13:08,  1.21s/it]\u001b[A\n",
      "648it [13:09,  1.22s/it]\u001b[A\n",
      "649it [13:11,  1.21s/it]\u001b[A\n",
      "650it [13:12,  1.21s/it]\u001b[A\n",
      "651it [13:13,  1.22s/it]\u001b[A\n",
      "652it [13:14,  1.22s/it]\u001b[A\n",
      "653it [13:15,  1.21s/it]\u001b[A\n",
      "654it [13:17,  1.21s/it]\u001b[A\n",
      "655it [13:18,  1.21s/it]\u001b[A\n",
      "656it [13:19,  1.21s/it]\u001b[A\n",
      "657it [13:20,  1.22s/it]\u001b[A\n",
      "658it [13:21,  1.21s/it]\u001b[A\n",
      "659it [13:23,  1.20s/it]\u001b[A\n",
      "660it [13:24,  1.20s/it]\u001b[A\n",
      "661it [13:25,  1.22s/it]\u001b[A\n",
      "662it [13:26,  1.21s/it]\u001b[A\n",
      "663it [13:28,  1.23s/it]\u001b[A\n",
      "664it [13:29,  1.23s/it]\u001b[A\n",
      "665it [13:30,  1.21s/it]\u001b[A\n",
      "666it [13:31,  1.21s/it]\u001b[A\n",
      "667it [13:32,  1.21s/it]\u001b[A\n",
      "668it [13:34,  1.20s/it]\u001b[A\n",
      "669it [13:35,  1.20s/it]\u001b[A\n",
      "670it [13:36,  1.21s/it]\u001b[A\n",
      "671it [13:37,  1.22s/it]\u001b[A\n",
      "672it [13:38,  1.22s/it]\u001b[A\n",
      "673it [13:40,  1.22s/it]\u001b[A\n",
      "674it [13:41,  1.21s/it]\u001b[A\n",
      "675it [13:42,  1.21s/it]\u001b[A\n",
      "676it [13:43,  1.21s/it]\u001b[A\n",
      "677it [13:44,  1.21s/it]\u001b[A\n",
      "678it [13:46,  1.21s/it]\u001b[A\n",
      "679it [13:47,  1.21s/it]\u001b[A\n",
      "680it [13:48,  1.22s/it]\u001b[A\n",
      "681it [13:49,  1.22s/it]\u001b[A\n",
      "682it [13:51,  1.22s/it]\u001b[A\n",
      "683it [13:52,  1.21s/it]\u001b[A\n",
      "684it [13:53,  1.21s/it]\u001b[A\n",
      "685it [13:54,  1.21s/it]\u001b[A\n",
      "686it [13:55,  1.21s/it]\u001b[A\n",
      "687it [13:57,  1.21s/it]\u001b[A\n",
      "688it [13:58,  1.21s/it]\u001b[A\n",
      "689it [13:59,  1.22s/it]\u001b[A\n",
      "690it [14:00,  1.21s/it]\u001b[A\n",
      "691it [14:01,  1.22s/it]\u001b[A\n",
      "692it [14:03,  1.22s/it]\u001b[A\n",
      "693it [14:04,  1.21s/it]\u001b[A\n",
      "694it [14:05,  1.21s/it]\u001b[A\n",
      "695it [14:06,  1.21s/it]\u001b[A\n",
      "696it [14:07,  1.18s/it]\u001b[A\n",
      "697it [14:09,  1.20s/it]\u001b[A\n",
      "698it [14:10,  1.20s/it]\u001b[A\n",
      "699it [14:11,  1.20s/it]\u001b[A\n",
      "700it [14:12,  1.20s/it]\u001b[A\n",
      "701it [14:14,  1.22s/it]\u001b[A\n",
      "702it [14:15,  1.21s/it]\u001b[A\n",
      "703it [14:16,  1.21s/it]\u001b[A\n",
      "704it [14:17,  1.21s/it]\u001b[A\n",
      "705it [14:18,  1.21s/it]\u001b[A\n",
      "706it [14:20,  1.22s/it]\u001b[A\n",
      "707it [14:21,  1.21s/it]\u001b[A\n",
      "708it [14:22,  1.21s/it]\u001b[A\n",
      "709it [14:23,  1.21s/it]\u001b[A\n",
      "710it [14:24,  1.21s/it]\u001b[A\n",
      "711it [14:26,  1.22s/it]\u001b[A\n",
      "712it [14:27,  1.22s/it]\u001b[A\n",
      "713it [14:28,  1.21s/it]\u001b[A\n",
      "714it [14:29,  1.22s/it]\u001b[A\n",
      "715it [14:31,  1.22s/it]\u001b[A\n",
      "716it [14:32,  1.21s/it]\u001b[A\n",
      "717it [14:33,  1.21s/it]\u001b[A\n",
      "718it [14:34,  1.16s/it]\u001b[A\n",
      "719it [14:35,  1.18s/it]\u001b[A\n",
      "720it [14:36,  1.19s/it]\u001b[A\n",
      "721it [14:38,  1.19s/it]\u001b[A\n",
      "722it [14:39,  1.20s/it]\u001b[A\n",
      "723it [14:40,  1.20s/it]\u001b[A\n",
      "724it [14:41,  1.20s/it]\u001b[A\n",
      "725it [14:42,  1.20s/it]\u001b[A\n",
      "726it [14:44,  1.20s/it]\u001b[A\n",
      "727it [14:45,  1.20s/it]\u001b[A\n",
      "728it [14:46,  1.21s/it]\u001b[A\n",
      "729it [14:47,  1.19s/it]\u001b[A\n",
      "730it [14:48,  1.19s/it]\u001b[A\n",
      "731it [14:50,  1.22s/it]\u001b[A\n",
      "732it [14:51,  1.21s/it]\u001b[A\n",
      "733it [14:52,  1.21s/it]\u001b[A\n",
      "734it [14:53,  1.21s/it]\u001b[A\n",
      "735it [14:54,  1.21s/it]\u001b[A\n",
      "736it [14:56,  1.20s/it]\u001b[A\n",
      "737it [14:57,  1.20s/it]\u001b[A\n",
      "738it [14:58,  1.20s/it]\u001b[A\n",
      "739it [14:59,  1.21s/it]\u001b[A\n",
      "740it [15:01,  1.21s/it]\u001b[A\n",
      "741it [15:02,  1.22s/it]\u001b[A\n",
      "742it [15:03,  1.22s/it]\u001b[A\n",
      "743it [15:04,  1.21s/it]\u001b[A\n",
      "744it [15:05,  1.21s/it]\u001b[A\n",
      "745it [15:06,  1.17s/it]\u001b[A\n",
      "746it [15:08,  1.18s/it]\u001b[A\n",
      "747it [15:09,  1.19s/it]\u001b[A\n",
      "748it [15:10,  1.20s/it]\u001b[A\n",
      "749it [15:11,  1.20s/it]\u001b[A\n",
      "750it [15:13,  1.20s/it]\u001b[A\n",
      "751it [15:14,  1.21s/it]\u001b[A\n",
      "752it [15:15,  1.21s/it]\u001b[A\n",
      "753it [15:16,  1.21s/it]\u001b[A\n",
      "754it [15:17,  1.21s/it]\u001b[A\n",
      "755it [15:19,  1.21s/it]\u001b[A\n",
      "756it [15:20,  1.23s/it]\u001b[A\n",
      "757it [15:21,  1.22s/it]\u001b[A\n",
      "758it [15:22,  1.22s/it]\u001b[A\n",
      "759it [15:24,  1.22s/it]\u001b[A\n",
      "760it [15:25,  1.24s/it]\u001b[A\n",
      "761it [15:26,  1.26s/it]\u001b[A\n",
      "762it [15:27,  1.27s/it]\u001b[A\n",
      "763it [15:29,  1.29s/it]\u001b[A\n",
      "764it [15:30,  1.28s/it]\u001b[A\n",
      "765it [15:31,  1.27s/it]\u001b[A\n",
      "766it [15:32,  1.27s/it]\u001b[A\n",
      "767it [15:34,  1.27s/it]\u001b[A\n",
      "768it [15:35,  1.27s/it]\u001b[A\n",
      "769it [15:36,  1.27s/it]\u001b[A\n",
      "770it [15:38,  1.27s/it]\u001b[A\n",
      "771it [15:39,  1.29s/it]\u001b[A\n",
      "772it [15:40,  1.29s/it]\u001b[A\n",
      "773it [15:41,  1.29s/it]\u001b[A\n",
      "774it [15:43,  1.30s/it]\u001b[A\n",
      "775it [15:44,  1.30s/it]\u001b[A\n",
      "776it [15:45,  1.29s/it]\u001b[A\n",
      "777it [15:47,  1.28s/it]\u001b[A\n",
      "778it [15:48,  1.28s/it]\u001b[A\n",
      "779it [15:49,  1.28s/it]\u001b[A\n",
      "780it [15:50,  1.27s/it]\u001b[A\n",
      "781it [15:52,  1.28s/it]\u001b[A\n",
      "782it [15:53,  1.28s/it]\u001b[A\n",
      "783it [15:54,  1.28s/it]\u001b[A\n",
      "784it [15:56,  1.27s/it]\u001b[A\n",
      "785it [15:57,  1.27s/it]\u001b[A\n",
      "786it [15:58,  1.27s/it]\u001b[A\n",
      "787it [15:59,  1.27s/it]\u001b[A\n",
      "788it [16:01,  1.27s/it]\u001b[A\n",
      "789it [16:02,  1.27s/it]\u001b[A\n",
      "790it [16:03,  1.27s/it]\u001b[A\n",
      "791it [16:04,  1.22s/it]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "c_python_scores_c_only = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {\n",
    "        'normal': obs1_normal1.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.4@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal2.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.14@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal3.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn15.5@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal4.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn7.6@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal5.detach().cpu().numpy(),\n",
    "        'layer': 16, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn16.9@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal6.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 mlp16@-1 <= attn7.11@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal7.detach().cpu().numpy(),\n",
    "        'layer': 2, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0'\n",
    "    },\n",
    "\n",
    "    \n",
    "    \n",
    "    {\n",
    "        'normal': obs2_normal1.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn15.0@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal2.detach().cpu().numpy(),\n",
    "        'layer': 5, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn5.7@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal3.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn7.2@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal4.detach().cpu().numpy(),\n",
    "        'layer': 19, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn19.13@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal5.detach().cpu().numpy(),\n",
    "        'layer': 9, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn13.2@6 <= mlp11@6 <= attn9.7@3'\n",
    "    },\n",
    "], c_tokenized_prompts, pre_tokenized=True, save_every=10, save_out=\"c_python_scores_c_only_new.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1e27f3c6-75ff-484f-891f-8f26955c085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(c_python_scores_c_only, open(\"c_python_scores_c_only.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "83f4dcc8-06bb-4053-afef-1a8de37aa032",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A\n",
      "1it [00:00,  1.00it/s]\u001b[A\n",
      "2it [00:01,  1.03it/s]\u001b[A\n",
      "3it [00:02,  1.03it/s]\u001b[A\n",
      "4it [00:03,  1.04it/s]\u001b[A\n",
      "5it [00:04,  1.04it/s]\u001b[A\n",
      "6it [00:05,  1.04it/s]\u001b[A\n",
      "7it [00:06,  1.04it/s]\u001b[A\n",
      "8it [00:07,  1.04it/s]\u001b[A\n",
      "9it [00:08,  1.04it/s]\u001b[A\n",
      "10it [00:09,  1.04it/s]\u001b[A\n",
      "11it [00:10,  1.03it/s]\u001b[A\n",
      "12it [00:11,  1.03it/s]\u001b[A\n",
      "13it [00:12,  1.04it/s]\u001b[A\n",
      "14it [00:13,  1.04it/s]\u001b[A\n",
      "15it [00:14,  1.04it/s]\u001b[A\n",
      "16it [00:15,  1.04it/s]\u001b[A\n",
      "17it [00:16,  1.04it/s]\u001b[A\n",
      "18it [00:17,  1.04it/s]\u001b[A\n",
      "19it [00:18,  1.04it/s]\u001b[A\n",
      "20it [00:19,  1.04it/s]\u001b[A\n",
      "21it [00:20,  1.04it/s]\u001b[A\n",
      "22it [00:21,  1.03it/s]\u001b[A\n",
      "23it [00:22,  1.04it/s]\u001b[A\n",
      "24it [00:23,  1.04it/s]\u001b[A\n",
      "25it [00:24,  1.04it/s]\u001b[A\n",
      "26it [00:25,  1.04it/s]\u001b[A\n",
      "27it [00:25,  1.04it/s]\u001b[A\n",
      "28it [00:26,  1.04it/s]\u001b[A\n",
      "29it [00:27,  1.04it/s]\u001b[A\n",
      "30it [00:28,  1.04it/s]\u001b[A\n",
      "31it [00:29,  1.04it/s]\u001b[A\n",
      "32it [00:30,  1.04it/s]\u001b[A\n",
      "33it [00:31,  1.03it/s]\u001b[A\n",
      "34it [00:32,  1.03it/s]\u001b[A\n",
      "35it [00:33,  1.04it/s]\u001b[A\n",
      "36it [00:34,  1.04it/s]\u001b[A\n",
      "37it [00:35,  1.04it/s]\u001b[A\n",
      "38it [00:36,  1.04it/s]\u001b[A\n",
      "39it [00:37,  1.04it/s]\u001b[A\n",
      "40it [00:38,  1.04it/s]\u001b[A\n",
      "41it [00:39,  1.04it/s]\u001b[A\n",
      "42it [00:40,  1.04it/s]\u001b[A\n",
      "43it [00:41,  1.03it/s]\u001b[A\n",
      "44it [00:42,  1.04it/s]\u001b[A\n",
      "45it [00:43,  1.04it/s]\u001b[A\n",
      "46it [00:44,  1.04it/s]\u001b[A\n",
      "47it [00:45,  1.04it/s]\u001b[A\n",
      "48it [00:46,  1.04it/s]\u001b[A\n",
      "49it [00:47,  1.04it/s]\u001b[A\n",
      "50it [00:48,  1.04it/s]\u001b[A\n",
      "51it [00:49,  1.04it/s]\u001b[A\n",
      "52it [00:50,  1.04it/s]\u001b[A\n",
      "53it [00:51,  1.04it/s]\u001b[A\n",
      "54it [00:52,  1.03it/s]\u001b[A\n",
      "55it [00:52,  1.04it/s]\u001b[A\n",
      "56it [00:53,  1.04it/s]\u001b[A\n",
      "57it [00:54,  1.04it/s]\u001b[A\n",
      "58it [00:55,  1.04it/s]\u001b[A\n",
      "59it [00:56,  1.04it/s]\u001b[A\n",
      "60it [00:57,  1.04it/s]\u001b[A\n",
      "61it [00:58,  1.04it/s]\u001b[A\n",
      "62it [00:59,  1.04it/s]\u001b[A\n",
      "63it [01:00,  1.04it/s]\u001b[A\n",
      "64it [01:01,  1.03it/s]\u001b[A\n",
      "65it [01:02,  1.04it/s]\u001b[A\n",
      "66it [01:03,  1.04it/s]\u001b[A\n",
      "67it [01:04,  1.04it/s]\u001b[A\n",
      "68it [01:05,  1.04it/s]\u001b[A\n",
      "69it [01:06,  1.04it/s]\u001b[A\n",
      "70it [01:07,  1.04it/s]\u001b[A\n",
      "71it [01:08,  1.03it/s]\u001b[A\n",
      "72it [01:09,  1.04it/s]\u001b[A\n",
      "73it [01:10,  1.04it/s]\u001b[A\n",
      "74it [01:11,  1.04it/s]\u001b[A\n",
      "75it [01:12,  1.03it/s]\u001b[A\n",
      "76it [01:13,  1.04it/s]\u001b[A\n",
      "77it [01:14,  1.04it/s]\u001b[A\n",
      "78it [01:15,  1.04it/s]\u001b[A\n",
      "79it [01:16,  1.04it/s]\u001b[A\n",
      "80it [01:17,  1.04it/s]\u001b[A\n",
      "81it [01:17,  1.04it/s]\u001b[A\n",
      "82it [01:18,  1.04it/s]\u001b[A\n",
      "83it [01:19,  1.04it/s]\u001b[A\n",
      "84it [01:20,  1.04it/s]\u001b[A\n",
      "85it [01:21,  1.03it/s]\u001b[A\n",
      "86it [01:22,  1.03it/s]\u001b[A\n",
      "87it [01:23,  1.04it/s]\u001b[A\n",
      "88it [01:24,  1.04it/s]\u001b[A\n",
      "89it [01:25,  1.04it/s]\u001b[A\n",
      "90it [01:26,  1.04it/s]\u001b[A\n",
      "91it [01:27,  1.04it/s]\u001b[A\n",
      "92it [01:28,  1.04it/s]\u001b[A\n",
      "93it [01:29,  1.04it/s]\u001b[A\n",
      "94it [01:30,  1.01it/s]\u001b[A\n",
      "95it [01:31,  1.02it/s]\u001b[A\n",
      "96it [01:32,  1.03it/s]\u001b[A\n",
      "97it [01:33,  1.03it/s]\u001b[A\n",
      "98it [01:34,  1.04it/s]\u001b[A\n",
      "99it [01:35,  1.04it/s]\u001b[A\n",
      "100it [01:36,  1.04it/s]\u001b[A\n",
      "101it [01:37,  1.04it/s]\u001b[A\n",
      "102it [01:38,  1.03it/s]\u001b[A\n",
      "103it [01:39,  1.04it/s]\u001b[A\n",
      "104it [01:40,  1.04it/s]\u001b[A\n",
      "105it [01:41,  1.03it/s]\u001b[A\n",
      "106it [01:42,  1.03it/s]\u001b[A\n",
      "107it [01:43,  1.04it/s]\u001b[A\n",
      "108it [01:44,  1.04it/s]\u001b[A\n",
      "109it [01:45,  1.04it/s]\u001b[A\n",
      "110it [01:45,  1.04it/s]\u001b[A\n",
      "111it [01:46,  1.04it/s]\u001b[A\n",
      "112it [01:47,  1.04it/s]\u001b[A\n",
      "113it [01:48,  1.04it/s]\u001b[A\n",
      "114it [01:49,  1.04it/s]\u001b[A\n",
      "115it [01:50,  1.03it/s]\u001b[A\n",
      "116it [01:51,  1.04it/s]\u001b[A\n",
      "117it [01:52,  1.04it/s]\u001b[A\n",
      "118it [01:53,  1.04it/s]\u001b[A\n",
      "119it [01:54,  1.04it/s]\u001b[A\n",
      "120it [01:55,  1.04it/s]\u001b[A\n",
      "121it [01:56,  1.04it/s]\u001b[A\n",
      "122it [01:57,  1.04it/s]\u001b[A\n",
      "123it [01:58,  1.04it/s]\u001b[A\n",
      "124it [01:59,  1.04it/s]\u001b[A\n",
      "125it [02:00,  1.04it/s]\u001b[A\n",
      "126it [02:01,  1.03it/s]\u001b[A\n",
      "127it [02:02,  1.04it/s]\u001b[A\n",
      "128it [02:03,  1.04it/s]\u001b[A\n",
      "129it [02:04,  1.04it/s]\u001b[A\n",
      "130it [02:05,  1.04it/s]\u001b[A\n",
      "131it [02:06,  1.04it/s]\u001b[A\n",
      "132it [02:07,  1.04it/s]\u001b[A\n",
      "133it [02:08,  1.04it/s]\u001b[A\n",
      "134it [02:09,  1.03it/s]\u001b[A\n",
      "135it [02:10,  1.04it/s]\u001b[A\n",
      "136it [02:11,  1.03it/s]\u001b[A\n",
      "137it [02:12,  1.03it/s]\u001b[A\n",
      "138it [02:12,  1.04it/s]\u001b[A\n",
      "139it [02:13,  1.04it/s]\u001b[A\n",
      "140it [02:14,  1.04it/s]\u001b[A\n",
      "141it [02:15,  1.04it/s]\u001b[A\n",
      "142it [02:16,  1.04it/s]\u001b[A\n",
      "143it [02:17,  1.04it/s]\u001b[A\n",
      "144it [02:18,  1.04it/s]\u001b[A\n",
      "145it [02:19,  1.04it/s]\u001b[A\n",
      "146it [02:20,  1.04it/s]\u001b[A\n",
      "147it [02:21,  1.03it/s]\u001b[A\n",
      "148it [02:22,  1.04it/s]\u001b[A\n",
      "149it [02:23,  1.04it/s]\u001b[A\n",
      "150it [02:24,  1.04it/s]\u001b[A\n",
      "151it [02:25,  1.03it/s]\u001b[A\n",
      "152it [02:26,  1.04it/s]\u001b[A\n",
      "153it [02:27,  1.04it/s]\u001b[A\n",
      "154it [02:28,  1.04it/s]\u001b[A\n",
      "155it [02:29,  1.04it/s]\u001b[A\n",
      "156it [02:30,  1.04it/s]\u001b[A\n",
      "157it [02:31,  1.03it/s]\u001b[A\n",
      "158it [02:32,  1.03it/s]\u001b[A\n",
      "159it [02:33,  1.07it/s]\u001b[A\n",
      "160it [02:34,  1.06it/s]\u001b[A\n",
      "161it [02:35,  1.05it/s]\u001b[A\n",
      "162it [02:36,  1.05it/s]\u001b[A\n",
      "163it [02:36,  1.05it/s]\u001b[A\n",
      "164it [02:37,  1.04it/s]\u001b[A\n",
      "165it [02:38,  1.04it/s]\u001b[A\n",
      "166it [02:39,  1.04it/s]\u001b[A\n",
      "167it [02:40,  1.04it/s]\u001b[A\n",
      "168it [02:41,  1.03it/s]\u001b[A\n",
      "169it [02:42,  1.04it/s]\u001b[A\n",
      "170it [02:43,  1.04it/s]\u001b[A\n",
      "171it [02:44,  1.04it/s]\u001b[A\n",
      "172it [02:45,  1.04it/s]\u001b[A\n",
      "173it [02:46,  1.04it/s]\u001b[A\n",
      "174it [02:47,  1.04it/s]\u001b[A\n",
      "175it [02:48,  1.04it/s]\u001b[A\n",
      "176it [02:49,  1.04it/s]\u001b[A\n",
      "177it [02:50,  1.04it/s]\u001b[A\n",
      "178it [02:51,  1.04it/s]\u001b[A\n",
      "179it [02:52,  1.03it/s]\u001b[A\n",
      "180it [02:53,  1.04it/s]\u001b[A\n",
      "181it [02:54,  1.03it/s]\u001b[A\n",
      "182it [02:55,  1.04it/s]\u001b[A\n",
      "183it [02:56,  1.04it/s]\u001b[A\n",
      "184it [02:57,  1.04it/s]\u001b[A\n",
      "185it [02:58,  1.04it/s]\u001b[A\n",
      "186it [02:59,  1.04it/s]\u001b[A\n",
      "187it [03:00,  1.04it/s]\u001b[A\n",
      "188it [03:01,  1.04it/s]\u001b[A\n",
      "189it [03:02,  1.03it/s]\u001b[A\n",
      "190it [03:02,  1.04it/s]\u001b[A\n",
      "191it [03:03,  1.03it/s]\u001b[A\n",
      "192it [03:04,  1.03it/s]\u001b[A\n",
      "193it [03:05,  1.04it/s]\u001b[A\n",
      "194it [03:06,  1.04it/s]\u001b[A\n",
      "195it [03:07,  1.04it/s]\u001b[A\n",
      "196it [03:08,  1.04it/s]\u001b[A\n",
      "197it [03:09,  1.04it/s]\u001b[A\n",
      "198it [03:10,  1.04it/s]\u001b[A\n",
      "199it [03:11,  1.04it/s]\u001b[A\n",
      "200it [03:12,  1.04it/s]\u001b[A\n",
      "201it [03:13,  1.04it/s]\u001b[A\n",
      "202it [03:14,  1.04it/s]\u001b[A\n",
      "203it [03:15,  1.04it/s]\u001b[A\n",
      "204it [03:16,  1.04it/s]\u001b[A\n",
      "205it [03:17,  1.04it/s]\u001b[A\n",
      "206it [03:18,  1.04it/s]\u001b[A\n",
      "207it [03:19,  1.04it/s]\u001b[A\n",
      "208it [03:20,  1.04it/s]\u001b[A\n",
      "209it [03:21,  1.04it/s]\u001b[A\n",
      "210it [03:22,  1.04it/s]\u001b[A\n",
      "211it [03:23,  1.04it/s]\u001b[A\n",
      "212it [03:24,  1.04it/s]\u001b[A\n",
      "213it [03:24,  1.09it/s]\u001b[A\n",
      "214it [03:25,  1.08it/s]\u001b[A\n",
      "215it [03:26,  1.07it/s]\u001b[A\n",
      "216it [03:27,  1.06it/s]\u001b[A\n",
      "217it [03:28,  1.05it/s]\u001b[A\n",
      "218it [03:29,  1.05it/s]\u001b[A\n",
      "219it [03:30,  1.05it/s]\u001b[A\n",
      "220it [03:31,  1.05it/s]\u001b[A\n",
      "221it [03:32,  1.04it/s]\u001b[A\n",
      "222it [03:33,  1.04it/s]\u001b[A\n",
      "223it [03:34,  1.04it/s]\u001b[A\n",
      "224it [03:35,  1.09it/s]\u001b[A\n",
      "225it [03:36,  1.06it/s]\u001b[A\n",
      "226it [03:37,  1.06it/s]\u001b[A\n",
      "227it [03:38,  1.05it/s]\u001b[A\n",
      "228it [03:39,  1.05it/s]\u001b[A\n",
      "229it [03:40,  1.05it/s]\u001b[A\n",
      "230it [03:41,  1.05it/s]\u001b[A\n",
      "231it [03:42,  1.04it/s]\u001b[A\n",
      "232it [03:43,  1.04it/s]\u001b[A\n",
      "233it [03:44,  1.04it/s]\u001b[A\n",
      "234it [03:45,  1.04it/s]\u001b[A\n",
      "235it [03:45,  1.04it/s]\u001b[A\n",
      "236it [03:46,  1.04it/s]\u001b[A\n",
      "237it [03:47,  1.04it/s]\u001b[A\n",
      "238it [03:48,  1.04it/s]\u001b[A\n",
      "239it [03:49,  1.04it/s]\u001b[A\n",
      "240it [03:50,  1.04it/s]\u001b[A\n",
      "241it [03:51,  1.04it/s]\u001b[A\n",
      "242it [03:52,  1.04it/s]\u001b[A\n",
      "243it [03:53,  1.04it/s]\u001b[A\n",
      "244it [03:54,  1.04it/s]\u001b[A\n",
      "245it [03:55,  1.04it/s]\u001b[A\n",
      "246it [03:56,  1.04it/s]\u001b[A\n",
      "247it [03:57,  1.04it/s]\u001b[A\n",
      "248it [03:58,  1.04it/s]\u001b[A\n",
      "249it [03:59,  1.07it/s]\u001b[A\n",
      "250it [04:00,  1.06it/s]\u001b[A\n",
      "251it [04:01,  1.05it/s]\u001b[A\n",
      "252it [04:02,  1.05it/s]\u001b[A\n",
      "253it [04:03,  1.05it/s]\u001b[A\n",
      "254it [04:04,  1.05it/s]\u001b[A\n",
      "255it [04:05,  1.04it/s]\u001b[A\n",
      "256it [04:06,  1.04it/s]\u001b[A\n",
      "257it [04:07,  1.04it/s]\u001b[A\n",
      "258it [04:07,  1.04it/s]\u001b[A\n",
      "259it [04:08,  1.04it/s]\u001b[A\n",
      "260it [04:09,  1.09it/s]\u001b[A\n",
      "261it [04:10,  1.07it/s]\u001b[A\n",
      "262it [04:11,  1.06it/s]\u001b[A\n",
      "263it [04:12,  1.05it/s]\u001b[A\n",
      "264it [04:13,  1.05it/s]\u001b[A\n",
      "265it [04:14,  1.05it/s]\u001b[A\n",
      "266it [04:15,  1.05it/s]\u001b[A\n",
      "267it [04:16,  1.05it/s]\u001b[A\n",
      "268it [04:17,  1.05it/s]\u001b[A\n",
      "269it [04:18,  1.04it/s]\u001b[A\n",
      "270it [04:19,  1.04it/s]\u001b[A\n",
      "271it [04:20,  1.04it/s]\u001b[A\n",
      "272it [04:21,  1.04it/s]\u001b[A\n",
      "273it [04:22,  1.04it/s]\u001b[A\n",
      "274it [04:23,  1.04it/s]\u001b[A\n",
      "275it [04:24,  1.04it/s]\u001b[A\n",
      "276it [04:25,  1.04it/s]\u001b[A\n",
      "277it [04:26,  1.04it/s]\u001b[A\n",
      "278it [04:27,  1.04it/s]\u001b[A\n",
      "279it [04:28,  1.04it/s]\u001b[A\n",
      "280it [04:28,  1.04it/s]\u001b[A\n",
      "281it [04:29,  1.03it/s]\u001b[A\n",
      "282it [04:30,  1.04it/s]\u001b[A\n",
      "283it [04:31,  1.04it/s]\u001b[A\n",
      "284it [04:32,  1.04it/s]\u001b[A\n",
      "285it [04:33,  1.04it/s]\u001b[A\n",
      "286it [04:34,  1.04it/s]\u001b[A\n",
      "287it [04:35,  1.04it/s]\u001b[A\n",
      "288it [04:36,  1.04it/s]\u001b[A\n",
      "289it [04:37,  1.04it/s]\u001b[A\n",
      "290it [04:38,  1.04it/s]\u001b[A\n",
      "291it [04:39,  1.04it/s]\u001b[A\n",
      "292it [04:40,  1.04it/s]\u001b[A\n",
      "293it [04:41,  1.04it/s]\u001b[A\n",
      "294it [04:42,  1.04it/s]\u001b[A\n",
      "295it [04:43,  1.04it/s]\u001b[A\n",
      "296it [04:44,  1.04it/s]\u001b[A\n",
      "297it [04:45,  1.04it/s]\u001b[A\n",
      "298it [04:46,  1.04it/s]\u001b[A\n",
      "299it [04:47,  1.04it/s]\u001b[A\n",
      "300it [04:48,  1.04it/s]\u001b[A\n",
      "301it [04:49,  1.04it/s]\u001b[A\n",
      "302it [04:50,  1.04it/s]\u001b[A\n",
      "303it [04:51,  1.04it/s]\u001b[A\n",
      "304it [04:52,  1.04it/s]\u001b[A\n",
      "305it [04:53,  1.04it/s]\u001b[A\n",
      "306it [04:53,  1.04it/s]\u001b[A\n",
      "307it [04:54,  1.04it/s]\u001b[A\n",
      "308it [04:55,  1.04it/s]\u001b[A\n",
      "309it [04:56,  1.04it/s]\u001b[A\n",
      "310it [04:57,  1.04it/s]\u001b[A\n",
      "311it [04:58,  1.04it/s]\u001b[A\n",
      "312it [04:59,  1.04it/s]\u001b[A\n",
      "313it [05:00,  1.04it/s]\u001b[A\n",
      "314it [05:01,  1.04it/s]\u001b[A\n",
      "315it [05:02,  1.04it/s]\u001b[A\n",
      "316it [05:03,  1.04it/s]\u001b[A\n",
      "317it [05:04,  1.04it/s]\u001b[A\n",
      "318it [05:05,  1.04it/s]\u001b[A\n",
      "319it [05:06,  1.04it/s]\u001b[A\n",
      "320it [05:07,  1.04it/s]\u001b[A\n",
      "321it [05:08,  1.03it/s]\u001b[A\n",
      "322it [05:09,  1.03it/s]\u001b[A\n",
      "323it [05:10,  1.04it/s]\u001b[A\n",
      "324it [05:11,  1.04it/s]\u001b[A\n",
      "325it [05:12,  1.04it/s]\u001b[A\n",
      "326it [05:13,  1.04it/s]\u001b[A\n",
      "327it [05:14,  1.04it/s]\u001b[A\n",
      "328it [05:15,  1.04it/s]\u001b[A\n",
      "329it [05:16,  1.04it/s]\u001b[A\n",
      "330it [05:17,  1.04it/s]\u001b[A\n",
      "331it [05:18,  1.03it/s]\u001b[A\n",
      "332it [05:18,  1.04it/s]\u001b[A\n",
      "333it [05:19,  1.04it/s]\u001b[A\n",
      "334it [05:20,  1.04it/s]\u001b[A\n",
      "335it [05:21,  1.04it/s]\u001b[A\n",
      "336it [05:22,  1.04it/s]\u001b[A\n",
      "337it [05:23,  1.04it/s]\u001b[A\n",
      "338it [05:24,  1.04it/s]\u001b[A\n",
      "339it [05:25,  1.04it/s]\u001b[A\n",
      "340it [05:26,  1.04it/s]\u001b[A\n",
      "341it [05:27,  1.03it/s]\u001b[A\n",
      "342it [05:28,  1.04it/s]\u001b[A\n",
      "343it [05:29,  1.04it/s]\u001b[A\n",
      "344it [05:30,  1.02it/s]\u001b[A\n",
      "345it [05:31,  1.02it/s]\u001b[A\n",
      "346it [05:32,  1.03it/s]\u001b[A\n",
      "347it [05:33,  1.03it/s]\u001b[A\n",
      "348it [05:34,  1.03it/s]\u001b[A\n",
      "349it [05:35,  1.04it/s]\u001b[A\n",
      "350it [05:36,  1.04it/s]\u001b[A\n",
      "351it [05:37,  1.03it/s]\u001b[A\n",
      "352it [05:38,  1.03it/s]\u001b[A\n",
      "353it [05:39,  1.03it/s]\u001b[A\n",
      "354it [05:40,  1.04it/s]\u001b[A\n",
      "355it [05:41,  1.03it/s]\u001b[A\n",
      "356it [05:42,  1.03it/s]\u001b[A\n",
      "357it [05:43,  1.04it/s]\u001b[A\n",
      "358it [05:44,  1.04it/s]\u001b[A\n",
      "359it [05:45,  1.04it/s]\u001b[A\n",
      "360it [05:46,  1.04it/s]\u001b[A\n",
      "361it [05:46,  1.03it/s]\u001b[A\n",
      "362it [05:47,  1.04it/s]\u001b[A\n",
      "363it [05:48,  1.04it/s]\u001b[A\n",
      "364it [05:49,  1.04it/s]\u001b[A\n",
      "365it [05:50,  1.04it/s]\u001b[A\n",
      "366it [05:51,  1.03it/s]\u001b[A\n",
      "367it [05:52,  1.04it/s]\u001b[A\n",
      "368it [05:53,  1.04it/s]\u001b[A\n",
      "369it [05:54,  1.04it/s]\u001b[A\n",
      "370it [05:55,  1.04it/s]\u001b[A\n",
      "371it [05:56,  1.04it/s]\u001b[A\n",
      "372it [05:57,  1.04it/s]\u001b[A\n",
      "373it [05:58,  1.04it/s]\u001b[A\n",
      "374it [05:59,  1.04it/s]\u001b[A\n",
      "375it [06:00,  1.04it/s]\u001b[A\n",
      "376it [06:01,  1.03it/s]\u001b[A\n",
      "377it [06:02,  1.04it/s]\u001b[A\n",
      "378it [06:03,  1.04it/s]\u001b[A\n",
      "379it [06:04,  1.04it/s]\u001b[A\n",
      "380it [06:05,  1.04it/s]\u001b[A\n",
      "381it [06:06,  1.03it/s]\u001b[A\n",
      "382it [06:07,  1.04it/s]\u001b[A\n",
      "383it [06:08,  1.04it/s]\u001b[A\n",
      "384it [06:09,  1.03it/s]\u001b[A\n",
      "385it [06:10,  1.04it/s]\u001b[A\n",
      "386it [06:11,  1.04it/s]\u001b[A\n",
      "387it [06:12,  1.03it/s]\u001b[A\n",
      "388it [06:13,  1.03it/s]\u001b[A\n",
      "389it [06:13,  1.04it/s]\u001b[A\n",
      "390it [06:14,  1.04it/s]\u001b[A\n",
      "391it [06:15,  1.03it/s]\u001b[A\n",
      "392it [06:16,  1.04it/s]\u001b[A\n",
      "393it [06:17,  1.04it/s]\u001b[A\n",
      "394it [06:18,  1.04it/s]\u001b[A\n",
      "395it [06:19,  1.04it/s]\u001b[A\n",
      "396it [06:20,  1.04it/s]\u001b[A\n",
      "397it [06:21,  1.03it/s]\u001b[A\n",
      "398it [06:22,  1.04it/s]\u001b[A\n",
      "399it [06:23,  1.04it/s]\u001b[A\n",
      "400it [06:24,  1.04it/s]\u001b[A\n",
      "401it [06:25,  1.03it/s]\u001b[A\n",
      "402it [06:26,  1.03it/s]\u001b[A\n",
      "403it [06:27,  1.04it/s]\u001b[A\n",
      "404it [06:28,  1.04it/s]\u001b[A\n",
      "405it [06:29,  1.04it/s]\u001b[A\n",
      "406it [06:30,  1.04it/s]\u001b[A\n",
      "407it [06:31,  1.04it/s]\u001b[A\n",
      "408it [06:32,  1.03it/s]\u001b[A\n",
      "409it [06:33,  1.03it/s]\u001b[A\n",
      "410it [06:34,  1.04it/s]\u001b[A\n",
      "411it [06:35,  1.03it/s]\u001b[A\n",
      "412it [06:36,  1.03it/s]\u001b[A\n",
      "413it [06:37,  1.04it/s]\u001b[A\n",
      "414it [06:38,  1.04it/s]\u001b[A\n",
      "415it [06:39,  1.03it/s]\u001b[A\n",
      "416it [06:40,  1.03it/s]\u001b[A\n",
      "417it [06:41,  1.04it/s]\u001b[A\n",
      "418it [06:42,  1.03it/s]\u001b[A\n",
      "419it [06:42,  1.03it/s]\u001b[A\n",
      "420it [06:43,  1.04it/s]\u001b[A\n",
      "421it [06:44,  1.03it/s]\u001b[A\n",
      "422it [06:45,  1.03it/s]\u001b[A\n",
      "423it [06:46,  1.04it/s]\u001b[A\n",
      "424it [06:47,  1.04it/s]\u001b[A\n",
      "425it [06:48,  1.04it/s]\u001b[A\n",
      "426it [06:49,  1.04it/s]\u001b[A\n",
      "427it [06:50,  1.04it/s]\u001b[A\n",
      "428it [06:51,  1.04it/s]\u001b[A\n",
      "429it [06:52,  1.03it/s]\u001b[A\n",
      "430it [06:53,  1.04it/s]\u001b[A\n",
      "431it [06:54,  1.03it/s]\u001b[A\n",
      "432it [06:55,  1.03it/s]\u001b[A\n",
      "433it [06:56,  1.03it/s]\u001b[A\n",
      "434it [06:57,  1.04it/s]\u001b[A\n",
      "435it [06:58,  1.04it/s]\u001b[A\n",
      "436it [06:59,  1.04it/s]\u001b[A\n",
      "437it [07:00,  1.04it/s]\u001b[A\n",
      "438it [07:01,  1.04it/s]\u001b[A\n",
      "439it [07:02,  1.03it/s]\u001b[A\n",
      "440it [07:03,  1.04it/s]\u001b[A\n",
      "441it [07:04,  1.03it/s]\u001b[A\n",
      "442it [07:05,  1.03it/s]\u001b[A\n",
      "443it [07:06,  1.03it/s]\u001b[A\n",
      "444it [07:07,  1.04it/s]\u001b[A\n",
      "445it [07:08,  1.04it/s]\u001b[A\n",
      "446it [07:09,  1.04it/s]\u001b[A\n",
      "447it [07:09,  1.04it/s]\u001b[A\n",
      "448it [07:10,  1.04it/s]\u001b[A\n",
      "449it [07:11,  1.04it/s]\u001b[A\n",
      "450it [07:12,  1.03it/s]\u001b[A\n",
      "451it [07:13,  1.03it/s]\u001b[A\n",
      "452it [07:14,  1.03it/s]\u001b[A\n",
      "453it [07:15,  1.03it/s]\u001b[A\n",
      "454it [07:16,  1.04it/s]\u001b[A\n",
      "455it [07:17,  1.04it/s]\u001b[A\n",
      "456it [07:18,  1.04it/s]\u001b[A\n",
      "457it [07:19,  1.04it/s]\u001b[A\n",
      "458it [07:20,  1.04it/s]\u001b[A\n",
      "459it [07:21,  1.04it/s]\u001b[A\n",
      "460it [07:22,  1.03it/s]\u001b[A\n",
      "461it [07:23,  1.03it/s]\u001b[A\n",
      "462it [07:24,  1.03it/s]\u001b[A\n",
      "463it [07:25,  1.03it/s]\u001b[A\n",
      "464it [07:26,  1.04it/s]\u001b[A\n",
      "465it [07:27,  1.04it/s]\u001b[A\n",
      "466it [07:28,  1.04it/s]\u001b[A\n",
      "467it [07:29,  1.04it/s]\u001b[A\n",
      "468it [07:30,  1.04it/s]\u001b[A\n",
      "469it [07:31,  1.01it/s]\u001b[A\n",
      "470it [07:32,  1.02it/s]\u001b[A\n",
      "471it [07:33,  1.02it/s]\u001b[A\n",
      "472it [07:34,  1.02it/s]\u001b[A\n",
      "473it [07:35,  1.03it/s]\u001b[A\n",
      "474it [07:36,  1.03it/s]\u001b[A\n",
      "475it [07:37,  1.04it/s]\u001b[A\n",
      "476it [07:38,  1.03it/s]\u001b[A\n",
      "477it [07:39,  1.04it/s]\u001b[A\n",
      "478it [07:39,  1.04it/s]\u001b[A\n",
      "479it [07:40,  1.04it/s]\u001b[A\n",
      "480it [07:41,  1.03it/s]\u001b[A\n",
      "481it [07:42,  1.02it/s]\u001b[A\n",
      "482it [07:43,  1.03it/s]\u001b[A\n",
      "483it [07:44,  1.03it/s]\u001b[A\n",
      "484it [07:45,  1.04it/s]\u001b[A\n",
      "485it [07:46,  1.04it/s]\u001b[A\n",
      "486it [07:47,  1.04it/s]\u001b[A\n",
      "487it [07:48,  1.04it/s]\u001b[A\n",
      "488it [07:49,  1.04it/s]\u001b[A\n",
      "489it [07:50,  1.04it/s]\u001b[A\n",
      "490it [07:51,  1.03it/s]\u001b[A\n",
      "491it [07:52,  1.03it/s]\u001b[A\n",
      "492it [07:53,  1.03it/s]\u001b[A\n",
      "493it [07:54,  1.03it/s]\u001b[A\n",
      "494it [07:55,  1.04it/s]\u001b[A\n",
      "495it [07:56,  1.04it/s]\u001b[A\n",
      "496it [07:57,  1.04it/s]\u001b[A\n",
      "497it [07:58,  1.04it/s]\u001b[A\n",
      "498it [07:59,  1.04it/s]\u001b[A\n",
      "499it [08:00,  1.04it/s]\u001b[A\n",
      "500it [08:01,  1.04it/s]\u001b[A\n",
      "501it [08:02,  1.02it/s]\u001b[A\n",
      "502it [08:03,  1.03it/s]\u001b[A\n",
      "503it [08:04,  1.03it/s]\u001b[A\n",
      "504it [08:05,  1.03it/s]\u001b[A\n",
      "505it [08:06,  1.04it/s]\u001b[A\n",
      "506it [08:07,  1.04it/s]\u001b[A\n",
      "507it [08:08,  1.04it/s]\u001b[A\n",
      "508it [08:08,  1.03it/s]\u001b[A\n",
      "509it [08:09,  1.04it/s]\u001b[A\n",
      "510it [08:10,  1.04it/s]\u001b[A\n",
      "511it [08:11,  1.02it/s]\u001b[A\n",
      "512it [08:12,  1.03it/s]\u001b[A\n",
      "513it [08:13,  1.03it/s]\u001b[A\n",
      "514it [08:14,  1.03it/s]\u001b[A\n",
      "515it [08:15,  1.04it/s]\u001b[A\n",
      "516it [08:16,  1.04it/s]\u001b[A\n",
      "517it [08:17,  1.04it/s]\u001b[A\n",
      "518it [08:18,  1.04it/s]\u001b[A\n",
      "519it [08:19,  1.04it/s]\u001b[A\n",
      "520it [08:20,  1.04it/s]\u001b[A\n",
      "521it [08:21,  1.03it/s]\u001b[A\n",
      "522it [08:22,  1.02it/s]\u001b[A\n",
      "523it [08:23,  1.03it/s]\u001b[A\n",
      "524it [08:24,  1.03it/s]\u001b[A\n",
      "525it [08:25,  1.04it/s]\u001b[A\n",
      "526it [08:26,  1.04it/s]\u001b[A\n",
      "527it [08:27,  1.04it/s]\u001b[A\n",
      "528it [08:28,  1.04it/s]\u001b[A\n",
      "529it [08:29,  1.04it/s]\u001b[A\n",
      "530it [08:30,  1.04it/s]\u001b[A\n",
      "531it [08:31,  1.03it/s]\u001b[A\n",
      "532it [08:32,  1.02it/s]\u001b[A\n",
      "533it [08:33,  1.05it/s]\u001b[A\n",
      "534it [08:34,  1.05it/s]\u001b[A\n",
      "535it [08:35,  1.04it/s]\u001b[A\n",
      "536it [08:36,  1.03it/s]\u001b[A\n",
      "537it [08:36,  1.04it/s]\u001b[A\n",
      "538it [08:37,  1.04it/s]\u001b[A\n",
      "539it [08:38,  1.04it/s]\u001b[A\n",
      "540it [08:39,  1.04it/s]\u001b[A\n",
      "541it [08:40,  1.03it/s]\u001b[A\n",
      "542it [08:41,  1.03it/s]\u001b[A\n",
      "543it [08:42,  1.02it/s]\u001b[A\n",
      "544it [08:43,  1.03it/s]\u001b[A\n",
      "545it [08:44,  1.03it/s]\u001b[A\n",
      "546it [08:45,  1.04it/s]\u001b[A\n",
      "547it [08:46,  1.10it/s]\u001b[A\n",
      "548it [08:47,  1.12it/s]\u001b[A\n",
      "549it [08:48,  1.15it/s]\u001b[A\n",
      "550it [08:49,  1.12it/s]\u001b[A\n",
      "551it [08:50,  1.08it/s]\u001b[A\n",
      "552it [08:51,  1.07it/s]\u001b[A\n",
      "553it [08:51,  1.08it/s]\u001b[A\n",
      "554it [08:52,  1.06it/s]\u001b[A\n",
      "555it [08:53,  1.05it/s]\u001b[A\n",
      "556it [08:54,  1.05it/s]\u001b[A\n",
      "557it [08:55,  1.05it/s]\u001b[A\n",
      "558it [08:56,  1.10it/s]\u001b[A\n",
      "559it [08:57,  1.08it/s]\u001b[A\n",
      "560it [08:58,  1.12it/s]\u001b[A\n",
      "561it [08:59,  1.08it/s]\u001b[A\n",
      "562it [09:00,  1.07it/s]\u001b[A\n",
      "563it [09:01,  1.06it/s]\u001b[A\n",
      "564it [09:02,  1.05it/s]\u001b[A\n",
      "565it [09:03,  1.04it/s]\u001b[A\n",
      "566it [09:04,  1.04it/s]\u001b[A\n",
      "567it [09:05,  1.04it/s]\u001b[A\n",
      "568it [09:06,  1.04it/s]\u001b[A\n",
      "569it [09:07,  1.04it/s]\u001b[A\n",
      "570it [09:08,  1.04it/s]\u001b[A\n",
      "571it [09:09,  1.03it/s]\u001b[A\n",
      "572it [09:10,  1.03it/s]\u001b[A\n",
      "573it [09:11,  1.03it/s]\u001b[A\n",
      "574it [09:11,  1.04it/s]\u001b[A\n",
      "575it [09:12,  1.03it/s]\u001b[A\n",
      "576it [09:13,  1.03it/s]\u001b[A\n",
      "577it [09:14,  1.03it/s]\u001b[A\n",
      "578it [09:15,  1.04it/s]\u001b[A\n",
      "579it [09:16,  1.04it/s]\u001b[A\n",
      "580it [09:17,  1.04it/s]\u001b[A\n",
      "581it [09:18,  1.03it/s]\u001b[A\n",
      "582it [09:19,  1.03it/s]\u001b[A\n",
      "583it [09:20,  1.03it/s]\u001b[A\n",
      "584it [09:21,  1.04it/s]\u001b[A\n",
      "585it [09:22,  1.04it/s]\u001b[A\n",
      "586it [09:23,  1.03it/s]\u001b[A\n",
      "587it [09:24,  1.03it/s]\u001b[A\n",
      "588it [09:25,  1.04it/s]\u001b[A\n",
      "589it [09:26,  1.04it/s]\u001b[A\n",
      "590it [09:27,  1.04it/s]\u001b[A\n",
      "591it [09:28,  1.03it/s]\u001b[A\n",
      "592it [09:29,  1.03it/s]\u001b[A\n",
      "593it [09:30,  1.03it/s]\u001b[A\n",
      "594it [09:31,  1.01it/s]\u001b[A\n",
      "595it [09:32,  1.02it/s]\u001b[A\n",
      "596it [09:33,  1.03it/s]\u001b[A\n",
      "597it [09:34,  1.03it/s]\u001b[A\n",
      "598it [09:35,  1.03it/s]\u001b[A\n",
      "599it [09:36,  1.09it/s]\u001b[A\n",
      "600it [09:37,  1.07it/s]\u001b[A\n",
      "601it [09:38,  1.05it/s]\u001b[A\n",
      "602it [09:38,  1.04it/s]\u001b[A\n",
      "603it [09:39,  1.04it/s]\u001b[A\n",
      "604it [09:40,  1.04it/s]\u001b[A\n",
      "605it [09:41,  1.03it/s]\u001b[A\n",
      "606it [09:42,  1.04it/s]\u001b[A\n",
      "607it [09:43,  1.04it/s]\u001b[A\n",
      "608it [09:44,  1.04it/s]\u001b[A\n",
      "609it [09:45,  1.04it/s]\u001b[A\n",
      "610it [09:46,  1.04it/s]\u001b[A\n",
      "611it [09:47,  1.03it/s]\u001b[A\n",
      "612it [09:48,  1.03it/s]\u001b[A\n",
      "613it [09:49,  1.03it/s]\u001b[A\n",
      "614it [09:50,  1.04it/s]\u001b[A\n",
      "615it [09:51,  1.04it/s]\u001b[A\n",
      "616it [09:52,  1.03it/s]\u001b[A\n",
      "617it [09:53,  1.05it/s]\u001b[A\n",
      "618it [09:54,  1.05it/s]\u001b[A\n",
      "619it [09:55,  1.05it/s]\u001b[A\n",
      "620it [09:56,  1.05it/s]\u001b[A\n",
      "621it [09:57,  1.03it/s]\u001b[A\n",
      "622it [09:58,  1.03it/s]\u001b[A\n",
      "623it [09:59,  1.04it/s]\u001b[A\n",
      "624it [10:00,  1.05it/s]\u001b[A\n",
      "625it [10:01,  1.05it/s]\u001b[A\n",
      "626it [10:02,  1.03it/s]\u001b[A\n",
      "627it [10:03,  1.04it/s]\u001b[A\n",
      "628it [10:04,  1.04it/s]\u001b[A\n",
      "629it [10:05,  1.04it/s]\u001b[A\n",
      "630it [10:05,  1.04it/s]\u001b[A\n",
      "631it [10:06,  1.08it/s]\u001b[A\n",
      "632it [10:07,  1.11it/s]\u001b[A\n",
      "633it [10:08,  1.08it/s]\u001b[A\n",
      "634it [10:09,  1.07it/s]\u001b[A\n",
      "635it [10:10,  1.06it/s]\u001b[A\n",
      "636it [10:11,  1.06it/s]\u001b[A\n",
      "637it [10:12,  1.04it/s]\u001b[A\n",
      "638it [10:13,  1.04it/s]\u001b[A\n",
      "639it [10:14,  1.04it/s]\u001b[A\n",
      "640it [10:15,  1.04it/s]\u001b[A\n",
      "641it [10:16,  1.03it/s]\u001b[A\n",
      "642it [10:17,  1.03it/s]\u001b[A\n",
      "643it [10:18,  1.03it/s]\u001b[A\n",
      "644it [10:19,  1.04it/s]\u001b[A\n",
      "645it [10:20,  1.04it/s]\u001b[A\n",
      "646it [10:21,  1.04it/s]\u001b[A\n",
      "647it [10:22,  1.03it/s]\u001b[A\n",
      "648it [10:23,  1.03it/s]\u001b[A\n",
      "649it [10:24,  1.04it/s]\u001b[A\n",
      "650it [10:25,  1.04it/s]\u001b[A\n",
      "651it [10:26,  1.03it/s]\u001b[A\n",
      "652it [10:27,  1.03it/s]\u001b[A\n",
      "653it [10:27,  1.03it/s]\u001b[A\n",
      "654it [10:28,  1.04it/s]\u001b[A\n",
      "655it [10:29,  1.04it/s]\u001b[A\n",
      "656it [10:30,  1.04it/s]\u001b[A\n",
      "657it [10:31,  1.04it/s]\u001b[A\n",
      "658it [10:32,  1.03it/s]\u001b[A\n",
      "659it [10:33,  1.03it/s]\u001b[A\n",
      "660it [10:34,  1.04it/s]\u001b[A\n",
      "661it [10:35,  1.03it/s]\u001b[A\n",
      "662it [10:36,  1.03it/s]\u001b[A\n",
      "663it [10:37,  1.04it/s]\u001b[A\n",
      "664it [10:38,  1.03it/s]\u001b[A\n",
      "665it [10:39,  1.04it/s]\u001b[A\n",
      "666it [10:40,  1.04it/s]\u001b[A\n",
      "667it [10:41,  1.04it/s]\u001b[A\n",
      "668it [10:42,  1.03it/s]\u001b[A\n",
      "669it [10:43,  1.03it/s]\u001b[A\n",
      "670it [10:44,  1.04it/s]\u001b[A\n",
      "671it [10:45,  1.03it/s]\u001b[A\n",
      "672it [10:46,  1.03it/s]\u001b[A\n",
      "673it [10:47,  1.03it/s]\u001b[A\n",
      "674it [10:48,  1.04it/s]\u001b[A\n",
      "675it [10:49,  1.04it/s]\u001b[A\n",
      "676it [10:50,  1.04it/s]\u001b[A\n",
      "677it [10:51,  1.04it/s]\u001b[A\n",
      "678it [10:52,  1.04it/s]\u001b[A\n",
      "679it [10:53,  1.03it/s]\u001b[A\n",
      "680it [10:54,  1.03it/s]\u001b[A\n",
      "681it [10:55,  1.03it/s]\u001b[A\n",
      "682it [10:56,  1.03it/s]\u001b[A\n",
      "683it [10:56,  1.03it/s]\u001b[A\n",
      "684it [10:57,  1.04it/s]\u001b[A\n",
      "685it [10:58,  1.04it/s]\u001b[A\n",
      "686it [10:59,  1.04it/s]\u001b[A\n",
      "687it [11:00,  1.04it/s]\u001b[A\n",
      "688it [11:01,  1.04it/s]\u001b[A\n",
      "689it [11:02,  1.03it/s]\u001b[A\n",
      "690it [11:03,  1.03it/s]\u001b[A\n",
      "691it [11:04,  1.02it/s]\u001b[A\n",
      "692it [11:05,  1.03it/s]\u001b[A\n",
      "693it [11:06,  1.03it/s]\u001b[A\n",
      "694it [11:07,  1.04it/s]\u001b[A\n",
      "695it [11:08,  1.03it/s]\u001b[A\n",
      "696it [11:09,  1.03it/s]\u001b[A\n",
      "697it [11:10,  1.04it/s]\u001b[A\n",
      "698it [11:11,  1.04it/s]\u001b[A\n",
      "699it [11:12,  1.04it/s]\u001b[A\n",
      "700it [11:13,  1.03it/s]\u001b[A\n",
      "701it [11:14,  1.02it/s]\u001b[A\n",
      "702it [11:15,  1.03it/s]\u001b[A\n",
      "703it [11:16,  1.03it/s]\u001b[A\n",
      "704it [11:17,  1.03it/s]\u001b[A\n",
      "705it [11:18,  1.04it/s]\u001b[A\n",
      "706it [11:19,  1.04it/s]\u001b[A\n",
      "707it [11:20,  1.04it/s]\u001b[A\n",
      "708it [11:21,  1.04it/s]\u001b[A\n",
      "709it [11:22,  1.04it/s]\u001b[A\n",
      "710it [11:23,  1.03it/s]\u001b[A\n",
      "711it [11:24,  1.02it/s]\u001b[A\n",
      "712it [11:25,  1.03it/s]\u001b[A\n",
      "713it [11:26,  1.03it/s]\u001b[A\n",
      "714it [11:26,  1.03it/s]\u001b[A\n",
      "715it [11:27,  1.04it/s]\u001b[A\n",
      "716it [11:28,  1.04it/s]\u001b[A\n",
      "717it [11:29,  1.04it/s]\u001b[A\n",
      "718it [11:30,  1.04it/s]\u001b[A\n",
      "719it [11:31,  1.01it/s]\u001b[A\n",
      "720it [11:32,  1.02it/s]\u001b[A\n",
      "721it [11:33,  1.01it/s]\u001b[A\n",
      "722it [11:34,  1.02it/s]\u001b[A\n",
      "723it [11:35,  1.03it/s]\u001b[A\n",
      "724it [11:36,  1.03it/s]\u001b[A\n",
      "725it [11:37,  1.04it/s]\u001b[A\n",
      "726it [11:38,  1.03it/s]\u001b[A\n",
      "727it [11:39,  1.04it/s]\u001b[A\n",
      "728it [11:40,  1.04it/s]\u001b[A\n",
      "729it [11:41,  1.04it/s]\u001b[A\n",
      "730it [11:42,  1.03it/s]\u001b[A\n",
      "731it [11:43,  1.02it/s]\u001b[A\n",
      "732it [11:44,  1.05it/s]\u001b[A\n",
      "733it [11:45,  1.05it/s]\u001b[A\n",
      "734it [11:46,  1.10it/s]\u001b[A\n",
      "735it [11:47,  1.08it/s]\u001b[A\n",
      "736it [11:48,  1.07it/s]\u001b[A\n",
      "737it [11:49,  1.06it/s]\u001b[A\n",
      "738it [11:50,  1.05it/s]\u001b[A\n",
      "739it [11:50,  1.05it/s]\u001b[A\n",
      "740it [11:51,  1.05it/s]\u001b[A\n",
      "741it [11:52,  1.02it/s]\u001b[A\n",
      "742it [11:53,  1.03it/s]\u001b[A\n",
      "743it [11:54,  1.03it/s]\u001b[A\n",
      "744it [11:55,  1.04it/s]\u001b[A\n",
      "745it [11:56,  1.04it/s]\u001b[A\n",
      "746it [11:57,  1.04it/s]\u001b[A\n",
      "747it [11:58,  1.04it/s]\u001b[A\n",
      "748it [11:59,  1.04it/s]\u001b[A\n",
      "749it [12:00,  1.04it/s]\u001b[A\n",
      "750it [12:01,  1.04it/s]\u001b[A\n",
      "751it [12:02,  1.02it/s]\u001b[A\n",
      "752it [12:03,  1.02it/s]\u001b[A\n",
      "753it [12:04,  1.03it/s]\u001b[A\n",
      "754it [12:05,  1.04it/s]\u001b[A\n",
      "755it [12:06,  1.04it/s]\u001b[A\n",
      "756it [12:07,  1.04it/s]\u001b[A\n",
      "757it [12:08,  1.07it/s]\u001b[A\n",
      "758it [12:09,  1.06it/s]\u001b[A\n",
      "759it [12:10,  1.05it/s]\u001b[A\n",
      "760it [12:11,  1.05it/s]\u001b[A\n",
      "761it [12:12,  1.03it/s]\u001b[A\n",
      "762it [12:13,  1.02it/s]\u001b[A\n",
      "763it [12:14,  1.03it/s]\u001b[A\n",
      "764it [12:15,  1.03it/s]\u001b[A\n",
      "765it [12:16,  1.04it/s]\u001b[A\n",
      "766it [12:17,  1.04it/s]\u001b[A\n",
      "767it [12:17,  1.04it/s]\u001b[A\n",
      "768it [12:18,  1.04it/s]\u001b[A\n",
      "769it [12:19,  1.04it/s]\u001b[A\n",
      "770it [12:20,  1.04it/s]\u001b[A\n",
      "771it [12:21,  1.03it/s]\u001b[A\n",
      "772it [12:22,  1.02it/s]\u001b[A\n",
      "773it [12:23,  1.03it/s]\u001b[A\n",
      "774it [12:24,  1.03it/s]\u001b[A\n",
      "775it [12:25,  1.03it/s]\u001b[A\n",
      "776it [12:26,  1.04it/s]\u001b[A\n",
      "777it [12:27,  1.04it/s]\u001b[A\n",
      "778it [12:28,  1.04it/s]\u001b[A\n",
      "779it [12:29,  1.04it/s]\u001b[A\n",
      "780it [12:30,  1.04it/s]\u001b[A\n",
      "781it [12:31,  1.03it/s]\u001b[A\n",
      "782it [12:32,  1.03it/s]\u001b[A\n",
      "783it [12:33,  1.02it/s]\u001b[A\n",
      "784it [12:34,  1.03it/s]\u001b[A\n",
      "785it [12:35,  1.03it/s]\u001b[A\n",
      "786it [12:36,  1.03it/s]\u001b[A\n",
      "787it [12:37,  1.04it/s]\u001b[A\n",
      "788it [12:38,  1.03it/s]\u001b[A\n",
      "789it [12:39,  1.03it/s]\u001b[A\n",
      "790it [12:40,  1.04it/s]\u001b[A\n",
      "791it [12:41,  1.02it/s]\u001b[A\n",
      "792it [12:42,  1.03it/s]\u001b[A\n",
      "793it [12:43,  1.02it/s]\u001b[A\n",
      "794it [12:44,  1.03it/s]\u001b[A\n",
      "795it [12:45,  1.04it/s]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "c_python_scores_python_only = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {\n",
    "        'normal': obs1_normal1.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.4@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal2.detach().cpu().numpy(),\n",
    "        'layer': 17, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.14@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal3.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn15.5@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal4.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn7.6@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal5.detach().cpu().numpy(),\n",
    "        'layer': 16, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn16.9@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal6.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 mlp16@-1 <= attn7.11@4'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs1_normal7.detach().cpu().numpy(),\n",
    "        'layer': 2, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs1 attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0'\n",
    "    },\n",
    "\n",
    "    \n",
    "    \n",
    "    {\n",
    "        'normal': obs2_normal1.detach().cpu().numpy(),\n",
    "        'layer': 15, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn15.0@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal2.detach().cpu().numpy(),\n",
    "        'layer': 5, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn5.7@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal3.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn7.2@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal4.detach().cpu().numpy(),\n",
    "        'layer': 19, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn19.13@3'\n",
    "    },\n",
    "    {\n",
    "        'normal': obs2_normal5.detach().cpu().numpy(),\n",
    "        'layer': 9, 'sublayer': 'ln_1_in', 'token': None, 'name': 'obs2 attn13.2@6 <= mlp11@6 <= attn9.7@3'\n",
    "    },\n",
    "], python_tokenized_prompts, pre_tokenized=True, save_every=10, save_out=\"c_python_scores_python_only_new.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "64e5ef03-7812-4642-b5fb-45d74e4d2577",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(c_python_scores_python_only, open(\"c_python_scores_python_only_new.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b10ca49d-f109-4b11-ab54-385d52ac6eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "795"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(c_python_scores_python_only[0]['scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d863c931-109b-4f13-a4d2-00a58a55cd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_c_scores = [x.mean() for x in c_python_scores_c_only[0]['scores']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "dfc21a99-62e1-40bf-ac2b-5196fc5d52d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_python_scores = [x.mean() for x in c_python_scores_python_only[0]['scores']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f2dea01b-5e39-45f9-9b62-893171ab1a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scores(scores_1, scores_2, score_idx=0, bins=25, label1=\"\", label2=\"\"):\n",
    "    mean_scores_1 = [x.mean() for x in scores_1[score_idx]['scores']]\n",
    "    mean_scores_2 = [x.mean() for x in scores_2[score_idx]['scores']]\n",
    "    plt.hist(mean_scores_1, bins=bins, alpha=0.5, label=label1)\n",
    "    plt.hist(mean_scores_2, bins=bins, alpha=0.5, label=label2)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "a1cac94c-b920-49ac-896a-4375b61bb8f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=0, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "48f5e9bf-0590-4dc2-9983-80dbb0d69ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=1, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fa77021c-cac8-47c7-a1d1-30ad1819a6ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=2, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e5baf017-4f4d-4458-8b9a-162e8f4f99eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=3, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "442dfbc0-3623-4219-846b-52754f63b4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=4, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "65b0294c-d305-414d-901a-0b6439c45a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGdCAYAAAAIbpn/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlsUlEQVR4nO3de3BU9f3/8dfmwsbVJGQoIUQTNMpFjbUEA6nRBsJVSG9ArUOpkbFTo6iAl0J+oqhtJiAqGHVaqBZTph2LLaihNJTQpFXLxRAVETXaEEUgEhESILAB9vz+YLJflwRI4Gw2n5PnY2ZH9nw+5+z7s+cMvPycs+e4LMuyBAAAYKCwUBcAAABwrggyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjRYS6gHPh8/m0e/duRUdHy+VyhbocAADQDpZl6eDBg0pMTFRYmD1zKUYGmd27dyspKSnUZQAAgHOwc+dOXXLJJbZsy8ggEx0dLenkFxETExPiagAAQHs0NjYqKSnJ/++4HYwMMi2nk2JiYggyAAAYxs7LQrjYFwAAGIsgAwAAjEWQAQAAxjLyGhkAANpiWZaOHz+uEydOhLqUbisyMlLh4eGd9nkEGQCAIzQ3N2vPnj1qamoKdSndmsvl0iWXXKKLLrqoUz6PIAMAMJ7P59OOHTsUHh6uxMRE9ejRgxumhoBlWaqvr9cXX3yh/v37d8rMDEEGAGC85uZm+Xw+JSUlyePxhLqcbq13796qra3VsWPHOiXIcLEvAMAx7LrtPc5dZ8+EsccBAICxCDIAAMBYXCMDAHC0ReuqO/XzZo0ecE7rHTlyRAsXLtS6desUEREhr9crj8ejH/3oR7r55psVHx9vc6XOQJABACDEjhw5ouzsbN14440qLy9XRMTJf55XrVqln/70p/rqq6/06KOPhrbILoogAwBAiM2bN09er1cLFiwIuFj2xz/+se68884QVtb1EWQAAAih48ePa+nSpcrPz2/zFz/333+/Dhw40PmFGYIgAyB4ygvPfxsj8s9/G0AX9tFHH6mhoUFXXnllm+3JyclKTk7u5KrMwa+WAAAIoYaGBknqtFv6Ow1BBgCAEIqLi5MkHT58OMSVmIkgAwBACA0cOFA9e/bUhx9+GOpSjESQAQAghMLDw3XXXXfpL3/5iyzLatWek5OjBx54IASVmYEgAwBAiD3yyCPyeDyaPXu2jh8/Lunkk6SfffZZbd++XQ8++GCIK+y6+NUSAMDRzvVOu53J7XZr3bp1euKJJ5SVlaXIyEh5vV5dffXV+ve//60+ffqEusQuiyADAEAXEBUVpUceeUSPPPJIqEsxCqeWAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAAihzZs3a/jw4XK5XBo0aJCGDx+ujIwMXXPNNVqyZEm7tvHuu+9q8eLFAct+8YtfKCEhQbfddpv9RXch3NkXAOBs5YWd+3kj8jvUfejQoaqoqJDL5dKcOXP8weOtt95SVlaWYmNjdcstt5xxGy1BZubMmf5lL7zwguNDjMSMDAAAXVJmZqZSU1P1t7/9LdSldGkEGQAAuqhjx47J5/MpKipKKSkpeuaZZyRJTz/9tC655BINHjxYxcXFmj9/vurq6jR8+HANHz5cO3bs8G/D5/Np9uzZysrK0qBBg7R27dqAz/jkk0900003aciQIbrmmmt099136+jRo5Kkv/71r/rOd74jl8ul1atX6wc/+IH69++ve+65p/O+hLPg1BIAAF3QihUr9OGHH2rx4sXyeDzav3+/ZsyYIUm677779Oqrr+rvf/+7oqOjZVmWHn30UVVUVLTazurVq/Xf//5XCxYs0HPPPadf/vKX+uyzzyRJXq9XY8eO1e23366HHnpIzc3NGjNmjO69914tXbpUkydP1re+9S2NGDFC27dv1+uvv669e/cqOTlZEydO1IgRIzrzK2nTOc3INDc3Kz8/XxEREaqtrW3VvmTJEqWlpSkzM1MTJkzQrl27Atoty9Ljjz+utLQ0DR06VFOnTlVDQ8M5DQAAAKeYP3++/2LfP/zhD1qzZo1Gjx6tadOmqbS0VHv27JEkbd26VZdddpmio6PPus20tDQNGjRIkvS9731Pn3/+ufbv3y9J+vOf/6zdu3f7r63p0aOHZs6cqRdffFFffvllwHamTJkiSYqPj9dVV12ld99916ZRn58OB5na2lplZWVp9+7dOnHiRKv2lStXat68eSotLdVbb72lYcOGKScnRz6fz99n0aJFWrFihd58801t3rxZPXr00K233np+IwEAwHBz5sxRRUWFNm7cqNLSUo0bN06SNGLECCUnJ6u4uFiS9OKLL2ratGnt2mbfvn39f46JiZEkNTY2SpK2bdumvn376sILL/T3ueKKK+Tz+bR9+/bTbic6Otq/jVDrcJA5dOiQli9fftovsKCgQLm5uYqPj5ckzZgxQ9u2bdOaNWskSSdOnND8+fM1ffp0eTweSdIDDzyg119/Xdu2bTvXcQAA4Fgul0u33Xabli1bpubmZm3YsEFZWVntWjc8PLzVMsuyAv57us883XZcLtcZ1+1MHQ4yqampuuKKK9ps279/v6qqqpSenu5fFhsbqwEDBqisrEzSyemw+vr6gD5XXnmlLrzwQn8fAAAQKDc3V59++qlmz56tnJycgKARFvZ//5w3NzfL6/W2a5vXXHON9uzZo8OHD/uXffrppwoPD9eVV15pX/FBZOuvlmpqaiRJCQkJAcsTEhL8bW31cblc6tOnj7/tVF6vV42NjQEvAAC6k379+ik7O1tFRUXKzc0NaOvdu7caGhpkWZYWL16sF154oV3bnDJlihITE/0302tubtbixYt1++23q0+fPnYPIShsDTJNTU2SJLfbHbDc7Xb729rT51SFhYWKjY31v5KSkuwsGwCAkGm5s6908mLfm2666bR9c3NzNWLECPXr1y9geXZ2tq677joNGzZMa9as0c0336yZM2eqtLRUpaWl+tWvfqWPP/7Yf2O9W265Re+//77cbrfWrl2rN954Q0OGDFFaWpquvvpqf7ApLS31Xwg8fPhwff3115o2bZreffddvfTSS1q4cKHt30dH2frz65ZrXk6d0vJ6vf4Lic7Up6XtVPn5+brvvvv87xsbGwkzAID26eCddjtby5192+OLL75o8xpVt9utdevWBSxbvHhxq8cWbNy4sdW6/fv3V2lpaZufN27cOP8Fxy2WLVumZcuWtavezmDrjExKSookqa6uLmB5XV2dv62tPpZl6csvv/S3ncrtdismJibgBQBAd/DBBx/otdde0/Hjx7Vq1SpNnDgx1CV1KbYGmbi4OA0ePFiVlZX+ZY2NjaqurtaoUaMkSd/+9rfVu3fvgD4fffSRDh8+7O8DAABOOnLkiO666y4NGzZM9957ry644IJQl9Sl2P6Igrlz56q4uFj19fWSpKKiIqWmpmr8+PGSTv58a86cOXr++ef918Q89dRT+v73v6/U1FS7ywEAwGjXXXeddu3apS1btuhnP/tZqMvpcjp8jUzL7YsPHDgg6eQFQ0lJSXrllVckSRMnTtTevXs1duxYRUVFKS4uTiUlJQE/DZs1a5YOHTqkzMxMRUZGqn///vrjH/9oz4gAAEC34bK6yh1tOqCxsVGxsbFqaGjgehmgKysvPP9tdPELNdE1HD16VDt27NBll12mqKioUJfTrZ1pXwTj32+efg0AcAwD/9/ccTp7HxBkAADGi4yMlKTT3o8Mnae5uVlS249GCAZb7yMDAEAohIeHq2fPntq7d6+kk/csO/VZQQg+n8+n+vp6eTweRUR0TsQgyAAAHKHl0TctYQahERYWpuTk5E4LkgQZAIAjuFwu9e3bV/Hx8Tp27Fioy+m2evToEfBL5WAjyAAAHCU8PLzTrs9A6HGxLwAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABj8fRrAK2VF4a6AgBoF2ZkAACAsQgyAADAWAQZAABgLK6RAdC12XW9zoh8e7YDoEthRgYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLGCEmS8Xq9mzZql73znO8rKytKwYcO0atWqgD5LlixRWlqaMjMzNWHCBO3atSsYpQAAAAeLCMZGf/Ob3+i1117Te++9p+joaL3zzjvKyMjQ5s2bde2112rlypWaN2+etm7dqvj4eD3++OPKycnRli1bFBbGJBEAAGifoKSGd999V+np6YqOjpYkDR48WLGxsfrXv/4lSSooKFBubq7i4+MlSTNmzNC2bdu0Zs2aYJQDAAAcKihBZtKkSXrjjTf0xRdfSJLWrl2r+vp69enTR/v371dVVZXS09P9/WNjYzVgwACVlZUFoxwAAOBQQTm1dNttt+nQoUNKTU1V37599fHHH2vSpEn6yU9+oq1bt0qSEhISAtZJSEhQTU1NMMoBAAAOFZQgs2TJEj3xxBPasmWLLr/8cr333nsqLy9XRESEmpqaJElutztgHbfb7W87ldfrldfr9b9vbGwMRtkAAMAwtp9asixLc+bM0R133KHLL79cknTttdeqpKREhYWF8ng8khQQTFret7SdqrCwULGxsf5XUlKS3WUDAAAD2R5k6uvrdeDAAV166aUByy+77DL99a9/VUpKiiSprq4uoL2urs7fdqr8/Hw1NDT4Xzt37rS7bAAAYCDbg8y3vvUtud1u7dmzJ2D5nj17dMEFFyguLk6DBw9WZWWlv62xsVHV1dUaNWpUm9t0u92KiYkJeAEAANh+jUxYWJhyc3P1wgsv6Pbbb1dcXJyqqqq0bt06LVy4UJI0d+5cTZ8+Xffff7969+6toqIipaamavz48XaXA3SaReuqz2v9WaMHOKIGAOhMQbnYd9GiRXr00Uc1cuRIeTweHTx4UPPnz9e9994rSZo4caL27t2rsWPHKioqSnFxcSopKeFmeAAAoEOCEmQ8Ho+eeOKJM/bJy8tTXl5eMD4eAAB0E0EJMgDQ5ZQX2rOdEfn2bAeALTiXAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMxZ19gS7ifB/42CLj86XnvnJ5L1tqAIDOwowMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsSJCXQAAOMmiddXntf6s0QNsqgToHpiRAQAAxiLIAAAAY3FqCegiMj5fGuoSAMA4zMgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGNxZ18A6IjywjM2Z3y+76yb2Jj8S7uqAbo9ZmQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYKWpD57LPP9NOf/lTZ2dn69re/rSFDhqi8vNzfvmTJEqWlpSkzM1MTJkzQrl27glUKAABwqKDcEO+rr77SiBEj9Pvf/14jR46UZVm65ZZb9MEHH2jEiBFauXKl5s2bp61btyo+Pl6PP/64cnJytGXLFoWFMUkEwNkyPl96+sbyXu3f0Ij88y8GMFxQUsMTTzyhoUOHauTIkZIkl8ulhQsXKicnR5JUUFCg3NxcxcfHS5JmzJihbdu2ac2aNcEoBwAAOFRQgszf/vY3ZWVlBSxLTk7WpZdeqv3796uqqkrp6en+ttjYWA0YMEBlZWXBKAcAADiU7aeWDh8+rJqaGvl8Pv3sZz9TbW2tPB6P7rjjDk2ePFk1NTWSpISEhID1EhIS/G2n8nq98nq9/veNjY12lw0AAAxke5A5cOCAJGnu3Llav3690tLStHnzZmVlZenEiRNKTEyUJLnd7oD13G63mpqa2txmYWGhHnvsMbtLBQAAhrP91FLLxbo5OTlKS0uTJA0dOlQ//vGPtWjRInk8HkkKmGFped/Sdqr8/Hw1NDT4Xzt37rS7bAAAYCDbg0zv3r3ldrt1ySWXBCzv16+fduzYoZSUFElSXV1dQHtdXZ2/7VRut1sxMTEBLwAAANuDTEREhL773e9qz549Acu//PJLJScnKy4uToMHD1ZlZaW/rbGxUdXV1Ro1apTd5QAAAAcLyq+WZs+erVdffVU7duyQdPLmeKtWrdK9994r6eT1M8XFxaqvr5ckFRUVKTU1VePHjw9GOQAAwKGCckO8cePG6bnnntOkSZPk8Xh0/PhxPfXUU/r5z38uSZo4caL27t2rsWPHKioqSnFxcSopKeFmeAAAoEOCEmQkaerUqZo6depp2/Py8pSXlxesjwcAAN0AUyAAAMBYBBkAAGAsggwAADBW0K6RAYBQ2FCz77zW/25KB54+DSDkmJEBAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAY0WEugAAaLGhZl+oSwBgGGZkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWD40EgG/gwZWAWZiRAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFj+/BuDHT48BmIYZGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwVlCDzLPPPiuXy6WKioqA5UuWLFFaWpoyMzM1YcIE7dq1K5hlAAAAhwpakNm9e7eefPLJVstXrlypefPmqbS0VG+99ZaGDRumnJwc+Xy+YJUCAAAcKmgPjbznnnuUn5+vO++8M2B5QUGBcnNzFR8fL0maMWOGfv3rX2vNmjXKyckJVjnAWS1aVx3qEoAOPbhz4/G2j9lZowfYVQ7Q5QVlRqakpESRkZEaN25cwPL9+/erqqpK6enp/mWxsbEaMGCAysrKglEKAABwMNtnZA4fPqyHHnpIa9euldfrDWirqamRJCUkJAQsT0hI8Le1xev1BmyrsbHRxooBAICpbJ+Refjhh5WXl6e+ffu2amtqapIkud3ugOVut9vf1pbCwkLFxsb6X0lJSfYWDQAAjGRrkHnnnXe0adMm5eXltdnu8XgkqdVMjdfr9be1JT8/Xw0NDf7Xzp077SsaAAAYy9ZTS6tXr9aRI0eUnZ0tSTp69KgkaebMmerZs6cWLlwoSaqrqwtYr66uTqNHjz7tdt1ud6tZHAAAAFtnZB5++GFVVVWpoqJCFRUVevnllyVJixcvVkVFhdLT0zV48GBVVlb612lsbFR1dbVGjRplZykAAKAb6PQ7+86dO1fFxcWqr6+XJBUVFSk1NVXjx4/v7FIAAIDhgnYfmZkzZ2rjxo3+Pw8aNEgvv/yyJk6cqL1792rs2LGKiopSXFycSkpKFBbG0xIAAEDHBC3ILF68+LRteXl5p70gGAAAoL2YBgEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMFbQfn4NAAiNReuqz2v9WaMH2FQJEHzMyAAAAGMRZAAAgLE4tQQAhsr4fOl5b2Nj8i9tqAQIHWZkAACAsQgyAADAWJxaAmxgxxQ/AKDjmJEBAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsSJCXQAAIHQyPl/aemF5r45vaET++RcDnANmZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsbizL7q38kL/HzM+3xfCQgAA54IZGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwVlCCzIoVKzRmzBiNHDlS6enpmjRpkmpqagL6LFmyRGlpacrMzNSECRO0a9euYJQCAAAcLChBZurUqXrggQe0fv16bdq0SdHR0Ro3bpyOHj0qSVq5cqXmzZun0tJSvfXWWxo2bJhycnLk8/mCUQ4AAHCooASZH/7whxozZszJDwgL0913361PPvlEVVVVkqSCggLl5uYqPj5ekjRjxgxt27ZNa9asCUY5AADAoYISZF555ZWA91FRUZKk5uZm7d+/X1VVVUpPT/e3x8bGasCAASorKwtGOQAAwKEiOuNDNmzYoMTERGVmZmrr1q2SpISEhIA+CQkJra6jaeH1euX1ev3vGxsbg1csAAAwRtCDjNfr1cKFC1VUVKTIyEg1NTVJktxud0A/t9vtbztVYWGhHnvssWCXCgDoAhatqw51CZo1ekCoS0A7Bf3n13fccYcmT56sSZMmSZI8Ho8kBcywtLxvaTtVfn6+Ghoa/K+dO3cGt2gAAGCEoM7IzJkzRxERESooKPAvS0lJkSTV1dUF9K2rq9Po0aPb3I7b7W41gwMAABC0ILNgwQLV1tbqz3/+s1wul7Zs2SJJGjJkiAYPHqzKykpNnjxZ0slrXqqrq7VgwYJglQMAaKcNNfs6vM7G4/93OojTMuhMQTm19Lvf/U7Lly/XjBkzVFVVpcrKSpWUlOj999+XJM2dO1fFxcWqr6+XJBUVFSk1NVXjx48PRjkAAMChbJ+ROXjwoKZPny6fz6frr78+oG3ZsmWSpIkTJ2rv3r0aO3asoqKiFBcXp5KSEoWF8cQEAADQfrYHmejoaJ04ceKs/fLy8pSXl2f3xwMAgG6EKRAAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYKygPv0a6EyL1lWfvdMpMj7v+MPxAABdBzMyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLh0aiSziXBz4CAMCMDAAAMBZBBgAAGIsgAwAAjMU1MgAAW3HNGzoTMzIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIzFr5ZgpIzPl4a6BAAOdr6/vJo1eoBNleBsmJEBAADGIsgAAABjcWoJAHDe7DjduzH5lzZUgu6GGRkAAGAsggwAADAWQQYAABiLa2Rgi478VLGtc+kZdhYDAOg2mJEBAADGIsgAAABjcWoJANAl2HXH7q7wM27uDNx5mJEBAADGIsgAAABjcWoJks5/GhQAYJ+u8HeyKae3mJEBAADGIsgAAABjEWQAAICxuEYGAOAoTvoZN86OGRkAAGAsggwAADAWp5YAAAgSTnMFX0hnZFatWqXrrrtON954o7KysvTBBx+EshwAAGCYkM3IbN68WbfeeqsqKys1cOBA/fGPf9TYsWP14YcfKjo6OlRlAQAAg7gsy7JC8cGTJk1SRESE/vKXv0iSfD6fEhMTNXfuXN19991nXLexsVGxsbFqaGhQTEyM/cWVF9q/zSDbULMv6J9h19SmXVOtAID26+jf4cG4s28w/v0O2aml9evXKz09/f8KCQvTkCFDVFZWFqqSAACAYUJyamnfvn1qaGhQQkJCwPKEhAS9/fbbrfp7vV55vV7/+4aGBkknk11QHD4anO0G0eEj3rN3Ok9HDx+yZTudUSsAIFBH/w4Pxr+xLdu082RQSIJMU1OTJMntdgcsd7vd/rZvKiws1GOPPdZqeVJSUnAKxGk8F+oCAADnrGN/h/+/IFUhSQcPHlRsbKwt2wpJkPF4PJIUMMvS8r6l7Zvy8/N13333+d/7fD59/fXX6tWrl1wuV3CLtUljY6OSkpK0c+fO4FzX08V19/FLfAeMv3uPX+I7YPwnx799+3YlJibatt2QBJlevXopNjZWdXV1Acvr6uqUkpLSqr/b7W41e9OzZ89glhg0MTEx3fIAbtHdxy/xHTD+7j1+ie+gu4//4osvVliYfZfohuxi3+zsbFVWVvrfW5alqqoqjRo1KlQlAQAAw4QsyMyZM0dr1qxRdXW1JOlPf/qTwsPDlZubG6qSAACAYUJ2Q7yhQ4equLhYU6ZM0QUXXKCwsDCtXbvWsTfDc7vdmjdvXqtTZN1Fdx+/xHfA+Lv3+CW+A8YfnPGH7IZ4AAAA54unXwMAAGMRZAAAgLEIMgAAwFgEGRutWrVK1113nW688UZlZWXpgw8+OG3fl156SYMGDdLw4cMDXkeOHOnEiu3V3Nys/Px8RUREqLa29qz933zzTWVkZCgrK0sZGRl64403gl9kEHVk/E7c/ytWrNCYMWM0cuRIpaena9KkSaqpqTnjOk46Bjo6fqcdA6+99ppycnI0evRo3XDDDRoyZIhWrFhx1vWccgycy/iddgx807PPPiuXy6WKiooz9rNl/1uwxaZNm6yLLrrI+uijjyzLsqzi4mLr4osvthobG9vsv2zZMmvZsmWdWGFw7dixw8rIyLBuvfVWS5K1Y8eOM/avra21YmJirPLycsuyLKuiosKKiYmxamtrg19sEHR0/E7b/5ZlWZGRkdbatWsty7KsEydOWLm5uVb//v2tI0eOtNnfacdAR8fvtGNg7NixVnFxsf/966+/boWFhVlbt2497TpOOgbOZfxOOwZa7Nq1y0pOTrYk+fdtW+za/8zI2GTBggUaP368Bg4cKEmaOnWqjh8/ruLi4hBX1jkOHTqk5cuXa9q0ae3qX1RU5P8/EUnKysrSwIED9eyzzwaxyuDp6Pid6Ic//KHGjBkj6eTT7O+++2598sknqqqqarO/046Bjo7faQoKCjRlyhT/++HDh8vn8+nTTz897TpOOgbOZfxOdc899yg/P/+s/eza/wQZm6xfv17p6en+92FhYRoyZIjKyspCWFXnSU1N1RVXXNHu/mVlZQHflySlp6cb+311dPxO9MorrwS8j4qKknTylFtbnHYMdHT8TjNkyBBFRJy8NdmxY8e0cOFCXXXVVRo9evRp13HSMXAu43eikpISRUZGaty4cWfta9f+J8jYYN++fWpoaFBCQkLA8oSEhDOeI1+9erWys7N1ww036Oabb9Y777wT7FK7jJqamg5/X07j9P2/YcMGJSYmKjMzs812px8DZxu/5MxjYPr06erdu7fWr1+vtWvX6qKLLjptXyceAx0Zv+SsY+Dw4cN66KGHtGjRonb1t2v/E2Rs0NTUJEmt7lbodrv9bafq06eP+vfvr3/84x968803ddNNN2nYsGFGH8Qd0dTU1KHvy2mcvv+9Xq8WLlyooqIiRUZGttnHycdAe8bv1GPg+eef1759+zRy5EhlZmZqz549p+3rxGOgI+N32jHw8MMPKy8vT3379m1Xf7v2P0HGBh6PR9LJv7y+yev1+ttOddNNN6mwsNC/E6dNm6Zrr71WTz75ZHCL7SI8Hk+Hvi+ncfr+v+OOOzR58mRNmjTptH2cfAy0Z/xOPgbCw8P16KOPyrIsPf3006ft59RjoL3jd9Ix8M4772jTpk3Ky8tr9zp27f+QPWvJSXr16qXY2FjV1dUFLK+rq1NKSkq7t3P55Zfrf//7n93ldUkpKSnn/X05jVP2/5w5cxQREaGCgoIz9nPqMdDe8bfF5GOgublZPXr08L8PCwtT//79tX379tOu46Rj4FzG3xZTj4HVq1fryJEjys7OliQdPXpUkjRz5kz17NlTL7zwQqvrCO3a/8zI2CQ7O1uVlZX+95ZlqaqqSqNGjWqzf35+fqvps127dikpKSmodXYVI0eODPi+JKmysvK035fTOHX/L1iwQLW1tVq6dKlcLpe2bNmiLVu2tNnXicdAR8bvtGMgLS2t1bI9e/YoMTHxtOs46Rg4l/E76Rh4+OGHVVVVpYqKClVUVOjll1+WJC1evFgVFRVt/hjCtv1/rr8TR6BNmzZZ0dHR1scff2xZlmUtX7484D4yt912mzV16lR//6ysLKuoqMj//p///KcVFhZmlZWVdW7hNisvL2/zPiqnjr/l/gH//ve/LcuyrP/85z9WdHS0kfeP+Kb2jt+J+/+3v/2tdfXVV1v//e9/rbffftt6++23rXnz5vnvk+H0Y6Cj43faMeByuazVq1f73y9fvtwKCwuz3njjDf8yJx8D5zJ+px0D37Rjx45W95EJ1v7n1JJNhg4dquLiYk2ZMkUXXHCBwsLCtHbtWkVHR0s6Oc127Ngxf//Zs2frueee0yuvvKITJ07I5/Np5cqVGjlyZKiGcF6am5s1ZswYHThwQJJ0yy23KCkpyf+T1FPH369fP61evVoPPvigevToIa/Xq7///e/q169fKMo/bx0dv9P2/8GDBzV9+nT5fD5df/31AW3Lli2T5Oxj4FzG77Rj4JlnnlFBQYHmz5+vEydOyOVy6fXXX9cNN9zg7+PkY+Bcxu+0Y6DFzJkztXHjRv+fBw0apJdffjlo+99lWZZl6wgAAAA6CdfIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGCs/w9X+M/37HkC5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=5, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "84b5c29b-d270-4fc1-b91e-7cd65e5b4056",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=6, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a783b50a-11ef-4870-a378-0c6435dd6073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=7, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9ed05400-8706-4aa2-8501-ef2c3b59cc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=8, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bc36fa4a-47c9-417e-b3be-0df1a95d5ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=9, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6ef8e235-bd9e-45e6-8f6e-8973f3a08504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAkWUlEQVR4nO3de1SUdeLH8c8gNDjJ7ZiIJngpzcougpjGtly8lVK7oamny2K1p9gyNbsom6Z5cvHSpkttm2Zb1tbaTTKM1VXTPZ3yElKZlVHHS6aSmMiYlyHg+f3hcX5NWjE4830YeL/O4dQ8t/k8UszH7/N9HhyWZVkCAAAwJMzuAAAAoGWhfAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwKtzuAD9VX1+vvXv3KioqSg6Hw+44AACgASzL0uHDh9WxY0eFhf3y2EaTKx979+5VYmKi3TEAAEAj7N69W506dfrFbZpc+YiKipJ0Inx0dLTNaQAAQEO43W4lJiZ6P8d/SZMrHycvtURHR1M+AAAIMQ2ZMsGEUwAAYBTlAwAAGEX5AAAARjW5OR8AAPycuro6/fDDD3bHaLFatWql8PDwM34UBuUDABASvv/+e33zzTeyLMvuKC2ay+VShw4ddNZZZzX6GJQPAECTV1dXp2+++UYul0vt2rXjIZQ2sCxLNTU1qqys1I4dO9S9e/dffZjYz6F8AACavB9++EGWZaldu3Zq3bq13XFarNatWysiIkK7du1STU2NIiMjG3UcJpwCAEIGIx72a+xoh88xApADAACgwSgfAADAKOZ8AABC1rxV5Ubf795BPRq137FjxzR37lytWrVK4eHh8ng8crlc+v3vf6+RI0cqPj4+wEmbNsoHAABBdOzYMWVlZemqq67S2rVrFR5+4qO3qKhIo0aN0oEDBzR9+nR7QxpG+QAAIIimTZsmj8ej2bNn+0yYvf766/WnP/3JxmT2oXwAABAktbW1WrhwofLz8097p859992nQ4cOmQ9mM8oHALQUawvMvE9mvpn3CQHbtm1TdXW1LrzwwtOuT0pKUlJSkuFU9uNuFwAAgqS6ulqS1KZNG5uTNC2UDwAAgiQuLk6SdOTIEZuTNC2UDwAAguSCCy5QbGysPv/8c7ujNCmUDwAAgqRVq1a666679Morr5z2t/FmZ2fr/vvvtyGZvSgfAAAE0cMPPyyXy6VJkyaptrZW0onfEPvEE0/os88+0wMPPGBzQvO42wUAELIa+8RRk5xOp1atWqU5c+YoPT1dERER8ng8uvjii/W///1P7du3tzuicZQPAACCLDIyUg8//LAefvhhu6M0CVx2AQAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAUz/kAgFCztsDuBMAZYeQDAIAg2bRpkzIyMuRwONSzZ09lZGSoX79+uuSSS7RgwYIGHeOjjz7S/PnzfZb98Y9/VEJCgsaMGRP40AYw8gEACF2mR4Ey8/3avG/fvlq3bp0cDocmT57sLQvvvfee0tPTFRMTo9GjR//iMU6WjwkTJniXLVq0KGSLh8TIBwAAxqWlpalXr15644037I5iC8oHAAA2+OGHH1RfX6/IyEh169ZNf/vb3yRJjz/+uDp16qTevXtr8eLFmjVrlioqKpSRkaGMjAzt2LHDe4z6+npNmjRJ6enp6tmzp1auXOnzHl9++aWuueYapaSk6JJLLtHYsWN1/PhxSdLrr7+uyy+/XA6HQ8uXL9d1112n7t2765577gn6uXPZBQAAw1599VV9/vnnmj9/vlwul6qqqjR+/HhJ0sSJE/Xmm2/q7bffVlRUlCzL0vTp07Vu3bpTjrN8+XK9//77mj17tp588kndcccd2rVrlyTJ4/FoyJAhuv322/XQQw+ppqZGgwcP1rhx47Rw4UKNGDFC55xzjjIzM/XZZ5/prbfe0v79+5WUlKScnBxlZmYG7fwZ+QAAwIBZs2Z5J5z+85//VElJiQYNGqRbb71VK1as0L59+yRJW7ZsUdeuXRUVFfWrx0xOTlbPnj0lSb/97W/19ddfq6qqSpL08ssva+/evd65ImeddZYmTJigZ599Vt9++63PcW688UZJUnx8vC666CJ99NFHATrr02PkAwAAA3484fTHMjMzlZSUpMWLF2vy5Ml69tlndeuttzbomB06dPD+e3R0tCTJ7XYrLi5OW7duVYcOHXT22Wd7tzn//PNVX1+vzz77TO3btz/tcaKiouR2u/09Pb8w8gEAgI0cDofGjBmj5557TjU1NVq/fr3S09MbtG+rVq1OWWZZls8/f+49f+44DofjF/cNBMoHAAA2y83N1VdffaVJkyYpOzvbpxyEhf3/R3VNTY08Hk+DjnnJJZdo3759OnLkiHfZV199pVatWunCCy8MXPhGoHwAAGCzzp07KysrS4WFhcrNzfVZ165dO1VXV8uyLM2fP1+LFi1q0DFvvPFGdezY0fuAspqaGs2fP1+33367zyUXO1A+AAAIkpNPOJVOTDi95pprfnbb3NxcZWZmqnPnzj7Ls7Ky1KdPH11xxRUqKSnRyJEjNWHCBK1YsUIrVqzQgw8+qC+++ML7sLLRo0frk08+kdPp1MqVK/Xuu+8qJSVFycnJuvjii71lZMWKFd7JqBkZGTp48KBuvfVWffTRR3r++ec1d+7cgP95nOSwgn1hx09ut1sxMTGqrq72Tp4BAPxIU//dLn4+BbQhjh8/rh07dqhr166KjIwM+PGbglmzZikxMVE33XST3VF+0c99L/z5/GbkAwAAm3z66adatmyZamtrVVRUpJycHLsjGcGttgAA2OTYsWO66667lJCQoIkTJ6p169Z2RzKC8gEAgE369OmjPXv22B3DOC67AAAAoygfAADAKMoHACBkNLEbNFukQHwPKB8AgCbv5OO/a2pqbE6Co0ePSpIiIiIafQwmnAIAmrzw8HC5XC5VVlYqIiLC55HjMMOyLB09elT79+9XbGzsaX+vTENRPgAATZ7D4VCHDh20Y8cO7dq1y+44LVpsbKwSEhLO6BiUDwBASDjrrLPUvXt3Lr3YKCIi4oxGPE6ifAAAQkZYWFizfbx6S8JFMwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAUTzgFADutLbA7AWAcIx8AAMAoygcAADDK7/Lh8Xh077336vLLL1d6erquuOIKFRUV+WyzYMECJScnKy0tTcOGDdOePXsCFhgAAIQ2v+d8PProo1q2bJk+/vhjRUVF6cMPP1S/fv20adMmXXbZZVq6dKmmTZumLVu2KD4+XjNmzFB2drY2b96ssDAGWgAAaOn8bgMfffSRUlNTFRUVJUnq3bu3YmJi9M4770iSZs6cqdzcXMXHx0uSxo8fr61bt6qkpCSAsQEAQKjyu3wMHz5c7777rr755htJ0sqVK1VZWan27durqqpKZWVlSk1N9W4fExOjHj16aPXq1YFLDQAAQpbfl13GjBmj77//Xr169VKHDh30xRdfaPjw4brhhhu0ZcsWSVJCQoLPPgkJCdq+fftpj+fxeOTxeLyv3W63v5EAAEAI8bt8LFiwQHPmzNHmzZt13nnn6eOPP9batWsVHh6uo0ePSpKcTqfPPk6n07vupwoKCvTII480IjoA2GveqvJG7XfvoB4BTgKEFr8uu1iWpcmTJ+vOO+/UeeedJ0m67LLLVFxcrIKCArlcLknyGck4+frkup/Kz89XdXW192v37t2NOQ8AABAi/CoflZWVOnTokLp06eKzvGvXrnr99dfVrVs3SVJFRYXP+oqKCu+6n3I6nYqOjvb5AgAAzZdf5eOcc86R0+nUvn37fJbv27dPrVu3VlxcnHr37q3S0lLvOrfbrfLycg0cODAwiQEAQEjzq3yEhYUpNzdXixYtUlVVlSSprKxMq1at0siRIyVJU6ZM0eLFi1VZWSlJKiwsVK9evTR06NAARwcAAKHI7wmn8+bN0/Tp0zVgwAC5XC4dPnxYs2bN0rhx4yRJOTk52r9/v4YMGaLIyEjFxcWpuLiYB4wBAABJjSgfLpdLc+bM+cVt8vLylJeX1+hQAACg+fK7fABAc9PYW2YBNA7XQgAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBR4XYHAAA0M2sL/N8nMz/wOdBkMfIBAACMonwAAACjKB8AAMAo5nwAaFbmrSq3OwKAX8HIBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwivIBAACMonwAAACjKB8AAMAoygcAADCK8gEAAIyifAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwivIBAACMonwAAACjKB8AAMAoygcAADCK8gEAAIyifAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwivIBAACMonwAAACjKB8AAMAoygcAADCK8gEAAIyifAAAAKMaVT527dqlUaNGKSsrS5deeqlSUlK0du1a7/oFCxYoOTlZaWlpGjZsmPbs2ROwwAAAILSF+7vDgQMHlJmZqWeeeUYDBgyQZVkaPXq0Pv30U2VmZmrp0qWaNm2atmzZovj4eM2YMUPZ2dnavHmzwsIYaAEAnMbaAv/3ycwPfA4Y4XcbmDNnjvr27asBAwZIkhwOh+bOnavs7GxJ0syZM5Wbm6v4+HhJ0vjx47V161aVlJQEMDYAAAhVfpePN954Q+np6T7LkpKS1KVLF1VVVamsrEypqanedTExMerRo4dWr1595mkBAEDI8+uyy5EjR7R9+3bV19frpptu0s6dO+VyuXTnnXdqxIgR2r59uyQpISHBZ7+EhATvup/yeDzyeDze1263299zAAAAIcSv8nHo0CFJ0pQpU7RmzRolJydr06ZNSk9PV11dnTp27ChJcjqdPvs5nU4dPXr0tMcsKCjQI4880ojoAAAgFPl12eXkhNHs7GwlJydLkvr27avrr79e8+bNk8vlkiSfkYyTr0+u+6n8/HxVV1d7v3bv3u33SQAAgNDhV/lo166dnE6nOnXq5LO8c+fO2rFjh7p16yZJqqio8FlfUVHhXfdTTqdT0dHRPl8AAKD58qt8hIeHq3///tq3b5/P8m+//VZJSUmKi4tT7969VVpa6l3ndrtVXl6ugQMHBiYxAAAIaX7f7TJp0iS9+eab2rFjh6QTDxwrKirSuHHjJJ2YD7J48WJVVlZKkgoLC9WrVy8NHTo0gLEBAECo8vshY1dffbWefPJJDR8+XC6XS7W1tfrrX/+qW265RZKUk5Oj/fv3a8iQIYqMjFRcXJyKi4t5wBgAAJAkOSzLsuwO8WNut1sxMTGqrq5m/gcAv81bVW53hF9176Ae//+iMU/2xAk84bRJ8efz2++RDwDA6fX7emHDNlzbNrhBgCaOayEAAMAoygcAADCK8gEAAIxizgfQwjR2QqbPJEkAOAOMfAAAAKMoHwAAwCjKBwAAMIo5H0ATwDwMAC0JIx8AAMAoygcAADCK8gEAAIyifAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwivIBAACMonwAAACjKB8AAMAoygcAADAq3O4AAJq3eavKG7XfvYN6BDgJgKaCkQ8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGMWttgBwGv2+Xmh3BKDZYuQDAAAYRfkAAABGUT4AAIBRlA8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAU5QMAABhF+QAAAEaF2x0AQOPNW1VudwQA8BsjHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAwilttATRJ3EYMNF+MfAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoxpdPp544gk5HA6tW7fOZ/mCBQuUnJystLQ0DRs2THv27DnTjAAAoBlpVPnYu3evHnvssVOWL126VNOmTdOKFSv03nvv6YorrlB2drbq6+vPOCgAAGgeGlU+7rnnHuXn55+yfObMmcrNzVV8fLwkafz48dq6datKSkrOLCUAAGg2/C4fxcXFioiI0NVXX+2zvKqqSmVlZUpNTfUui4mJUY8ePbR69eozTwoAAJoFv55weuTIET300ENauXKlPB6Pz7rt27dLkhISEnyWJyQkeNedjsfj8TmW2+32JxIAAAgxfo18TJ06VXl5eerQocMp644ePSpJcjqdPsudTqd33ekUFBQoJibG+5WYmOhPJAAAEGIaXD4+/PBDbdy4UXl5eadd73K5JOmUERGPx+Nddzr5+fmqrq72fu3evbuhkQAAQAhq8GWX5cuX69ixY8rKypIkHT9+XJI0YcIExcbGau7cuZKkiooKn/0qKio0aNCgnz2u0+k8ZbQEAAA0Xw0e+Zg6darKysq0bt06rVu3TkuWLJEkzZ8/X+vWrVNqaqp69+6t0tJS7z5ut1vl5eUaOHBg4JMDAICQFNAnnE6ZMkWLFy9WZWWlJKmwsFC9evXS0KFDA/k2AAAghPl1t8tJEyZM0IYNG7z/3rNnTy1ZskQ5OTnav3+/hgwZosjISMXFxam4uFhhYTzFHQAAnNCo8jF//vyfXZeXl/ezk1IBAAAaVT4AnN68VeV2RwBajrUF/u+TeerTuWEe10MAAIBRlA8AAGAU5QMAABjFnA8Y15h5EfcO6hGEJAAAOzDyAQAAjKJ8AAAAoygfAADAKMoHAAAwivIBAACMonwAAACjKB8AAMAoygcAADCK8gEAAIyifAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAo8LtDgA0RfNWldsdAQCaLUY+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgVLjdAQDAH/2+Xuj3PhuS7ghCEgCNxcgHAAAwivIBAACMonwAAACjmPOBZm3eqnK7IzQb/FkCCBRGPgAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFLfaotG49RIA0BiMfAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKG61RUjgtl4AaD4Y+QAAAEZRPgAAgFGUDwAAYBRzPgA0e/2+Xmh3BAA/wsgHAAAwivIBAACMonwAAACjmPMBnqEBADCKkQ8AAGCU3+Xj1Vdf1eDBgzVgwAClpqZq+PDh2r59u882CxYsUHJystLS0jRs2DDt2bMnYIEBAEBo8/uyy80336zly5dr8ODBqq+v12233aarr75aW7ZsUWRkpJYuXapp06Zpy5Ytio+P14wZM5Sdna3NmzcrLIyBlmDi8gkAIBT43QZ+97vfafDgwSd2DgvT2LFj9eWXX6qsrEySNHPmTOXm5io+Pl6SNH78eG3dulUlJSUBjA0AAEKV3+Xjtdde83kdGRkpSaqpqVFVVZXKysqUmprqXR8TE6MePXpo9erVZxgVAAA0B2d8t8v69evVsWNHpaWlacuWLZKkhIQEn20SEhJOmRdyksfjkcfj8b52u91nGgkAADRhZzQJw+PxaO7cuSosLFRERISOHj0qSXI6nT7bOZ1O77qfKigoUExMjPcrMTHxTCIBAIAm7ozKx5133qkRI0Zo+PDhkiSXyyVJPiMZJ1+fXPdT+fn5qq6u9n7t3r37TCIBAIAmrtGXXSZPnqzw8HDNnDnTu6xbt26SpIqKCp9tKyoqNGjQoNMex+l0njJSAgAAmq9GjXzMnj1bO3fu1MKFC+VwOLR582Zt3rxZcXFx6t27t0pLS73but1ulZeXa+DAgQELDQAAQpffIx9PP/20XnzxRT3zzDPe22uXL1+uLl26KCUlRVOmTNHdd9+t++67T+3atVNhYaF69eqloUOHBjw8AISi9du/a9R+/bu1DXASwB5+lY/Dhw/r7rvvVn19va688kqfdc8995wkKScnR/v379eQIUMUGRmpuLg4FRcX84AxAAAgyc/yERUVpbq6ul/dLi8vT3l5eY0OBQAAmi+GIwAAgFGUDwAAYBTlAwAAGEX5AAAARlE+AACAUZQPAABgFOUDAAAYRfkAAABGUT4AAIBRlA8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGBVudwAAoa/f1wsbtd+GpDsCnARAKGDkAwAAGEX5AAAARlE+AACAUcz5AADgl6wtaNx+mfmBzdGMMPIBAACMonwAAACjKB8AAMAo5nwA8NHYZ3Y09fcC0HQw8gEAAIyifAAAAKMoHwAAwCjmfAAAWo7GPrMDAcXIBwAAMIryAQAAjOKySxM0b1W53REAAAgaRj4AAIBRlA8AAGAU5QMAABhF+QAAAEZRPgAAgFGUDwAAYBTlAwAAGMVzPgAgRKzf/l2j9uvfrW2AkwBnhpEPAABgFOUDAAAYxWUXAACaisb81t3M/MDnCDJGPgAAgFGUDwAAYBTlAwAAGMWcD6AZ6/f1QrsjAC1XY+ZvtBCMfAAAAKMoHwAAwCjKBwAAMIo5Hw00b1V5o/a7d1CPACcBACC0MfIBAACMonwAAACjuOwSZI29XAN7NObW1A1JdwQhyam4bRbAaYXgI9kZ+QAAAEZRPgAAgFGUDwAAYBRzPoAzxFwMNHXrt3/n9z79u7UNQhLgBEY+AACAUZQPAABgFOUDAAAY1eLmfPDcjZaBeRgA0HQx8gEAAIwKWvkoKipSnz59dNVVVyk9PV2ffvppsN4KAACEkKBcdtm0aZP+8Ic/qLS0VBdccIFeeOEFDRkyRJ9//rmioqKC8ZYAACBEBKV8zJ49W0OHDtUFF1wgSbr55pv14IMPavHixRo7dmww3hIAEECNeTaIxPNB0DBBueyyZs0apaam/v+bhIUpJSVFq1evDsbbAQCAEBLwkY/vvvtO1dXVSkhI8FmekJCgDz744JTtPR6PPB6P93V1dbUkye12BzqaJOn4ke+Dclw0LUeOeX59IwAB5z5y3O4IaIggfMae/Ny2LOtXtw14+Th69Kgkyel0+ix3Op3edT9WUFCgRx555JTliYmJgY4GAAAkSTOCduTDhw8rJibmF7cJePlwuVyS5DOacfL1yXU/lp+fr4kTJ3pf19fX6+DBg2rbtq0cDkdAs7ndbiUmJmr37t2Kjo4O6LGbspZ43pxzyzhnqWWeN+fMOTdFlmXp8OHD6tix469uG/Dy0bZtW8XExKiiosJneUVFhbp163bK9k6n85RRktjY2EDH8hEdHR0S38hAa4nnzTm3HC3xvDnnliGUzvnXRjxOCsqE06ysLJWWlnpfW5alsrIyDRw4MBhvBwAAQkhQysfkyZNVUlKi8vITjzJ/6aWX1KpVK+Xm5gbj7QAAQAgJynM++vbtq8WLF+vGG29U69atFRYWppUrV9r+gDGn06lp06adcpmnuWuJ5805txwt8bw555ahOZ+zw2rIPTEAAAABwi+WAwAARlE+AACAUZQPAABgVIstH1u2bNGQIUPUv39/paWlKScnR7t27bI7lhFvvPGGMjMzlZGRofPPP1/XXnutampq7I4VdPX19erbt6+6dOlid5SgOnjwoKZPn67f/OY3ysjI0OWXX65HH31UtbW1dkcLuKKiIvXp00dXXXWV0tPT9emnn9odKaheffVVDR48WAMGDFBqaqqGDx+u7du32x3LmCeeeEIOh0Pr1q2zO4oRu3bt0qhRo5SVlaVLL71UKSkpWrt2rd2xAsNqgerr663ExETrvvvu8y679957rT59+tiYyowlS5ZYKSkpVlVVlWVZlrVnzx4rOjraOnz4sL3BDCgsLLRiY2Otzp072x0lqF588UXroosusg4dOmRZ1onvcfv27a2pU6fanCywNm7caLVp08batm2bZVmWtXjxYuvcc8+13G63zcmCJyIiwlq5cqVlWZZVV1dn5ebmWt27d7eOHTtmc7Lg27Nnj5WUlGRJstauXWt3nKCrrKy0unbtaq1evdqyrBOfWyNHjrSeeOIJm5MFRossHwcOHLAkWSUlJd5lb7/9tiXJOnjwoI3Jgqu2ttbq0KGD9Z///Mdn+XvvvWfV1tbalMqMb775xrrsssusBx98sNmXj5KSEuvZZ5/1WXb33Xdb3bt3tylRcOTk5FgjR470vq6rq7Pat2/fbH44n86IESN8Xn/wwQeWJOu9996zKZE5OTk51j/+8Y8WUz4eeOABa9SoUT7Ldu3aZe3YscOeQAHWIi+7tG3bVhkZGXrllVdUW1ur2tpaLVmyRGeffbbOPvtsu+MFzfvvv6+Kigr99re/9Vl+5ZVXqlWrVjalMmPcuHEqKChQ69at7Y4SdNdcc41uu+02n2WRkZHN7tLamjVrlJqa6n0dFhamlJQUrV692sZUwfXaa6/5vI6MjJSkZve9/ani4mJFRETo6quvtjuKMW+88YbS09N9liUlJTWby8YtsnxI0ltvvaXvvvtOnTp1UqdOnVRUVKSnn35aZ511lt3RguaTTz5RbGysVq1apYEDB+rKK6/ULbfcop07d9odLaiKi4sVHh6ua665xu4otlm/fr1uuOEGu2MEzHfffafq6molJCT4LE9ISGhRcyDWr1+vjh07Ki0tze4oQXPkyBE99NBDmjdvnt1RjDly5Ii2b9+u+vp63XTTTUpLS9OgQYP0+uuv2x0tYILyhNOmrq6uTsOGDVOXLl20e/duSdILL7yg888/3+ZkwVVVVSW3260nn3xSy5Ytk8vl0gMPPKD+/ftr27ZtDf6FQKHk+++/15///Gf997//tTuKbd555x19/fXXKikpsTtKwBw9elSSTnnyo9Pp9K5r7jwej+bOnavCwkJFRETYHSdopk6dqry8PHXo0KHZ/0XppEOHDkmSpkyZojVr1ig5OVmbNm1Senq66urqNGrUKHsDBkCzGvmYPn26HA7HL36Vlpbqrbfe0rvvvquCggJFREQoIiJCgwcPVmZmZkj+ramh5x0WFqa6ujpNnjxZZ599thwOh2bMmKEDBw7o3//+t92n4ZeGnvOPf3CFuoae84/t2bNHeXl5WrZsWbMqly6XS9KJD+Af83g83nXN3Z133qkRI0Zo+PDhdkcJmg8//FAbN25UXl6e3VGMCgs78dGcnZ2t5ORkSSd+bcn111/fbEaAmtXIx/333/+r/5Gec845eueddxQeHq5zzz3XuzwxMVG1tbVavny5xo0bF+yoAdXQ8962bZskqVOnTt7lLpdL55xzjnbs2BHUjIHW0HO+/fbbtXnzZu+18p07d6qiosJ7m/GiRYtMxA2Ihp7zSQcPHtR1112np556yvsDrLlo27atYmJiVFFR4bO8oqJC3bp1symVOZMnT1Z4eLhmzpxpd5SgWr58uY4dO6asrCxJ0vHjxyVJEyZMUGxsrBYtWtQsR6zbtWsnp9Pp87Nakjp37qw1a9bYlCqwmlX5aNOmjdq0afOr25177rmqra3VgQMHvD+sKysrVVtbG5ITEht63ldddZUkad++fbrgggskST/88IMOHjyopKSkoGYMtIae88cff+zzevr06Xr++edD8jkBDT1nSTp8+LCuvfZaPfzwwxo4cKAkaeHChbrjjjuCGdGorKwsn5Eey7JUVlamhx56yMZUwTd79mzt3LlTL7/8shwOhzZv3ixJSklJsTlZ4E2dOlVTp071vt65c6e6du2q+fPnKyMjw75gQRYeHq7+/ftr3759Psu//fbbkPtZ/XOa1WWXhho2bJjat2+vOXPmeJfNmjVL0dHRzXo2defOnTV69GgVFhaqrq5OkvTUU08pJiZGo0ePtjkdAuX48eO67rrr1K9fP5177rkqLS1VaWmpFixYYHe0gJo8ebJKSkpUXl4uSXrppZfUqlUr5ebm2pwseJ5++mm9+OKLGj9+vMrKylRaWqri4mJ98skndkdDgE2aNElvvvmmd1R6165dKioqCrmR+Z/TYn+r7SeffKIHH3xQhw4dUl1dndq0aaO//OUv6tevn93RgurIkSOaOHGiNmzYoJiYGLVp00aPPfaYLrroIrujBVVFRYVGjx7tvezSr18/jRkzRmPGjLE7WsD9/e9/19ixY0+7rrn9715UVKSZM2eqdevWCgsL01NPPaWLL77Y7lhBcfjwYcXGxqq+vv6Udc8991yz/G/5xyZMmKANGzZo48aNuuyyy9SzZ08tWbLE7lhB9a9//UuPP/64XC6Xamtrdccdd5xyG32oarHlAwAA2KNFXnYBAAD2oXwAAACjKB8AAMAoygcAADCK8gEAAIyifAAAAKMoHwAAwCjKBwAAMIryAQAAjKJ8AAAAoygfAADAKMoHAAAw6v8AmOZi5ozKAWwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=10, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d4dc198b-6732-4215-9b97-92ea1316e64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(c_python_scores_c_only, c_python_scores_python_only, score_idx=11, label1=\"C\", label2=\"Python\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1120cd9e-f8ea-4bf7-adee-6a3e691414b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A\n",
      "1it [00:00,  1.01it/s]\u001b[A\n",
      "2it [00:01,  1.01it/s]\u001b[A\n",
      "3it [00:02,  1.01it/s]\u001b[A\n",
      "4it [00:03,  1.01it/s]\u001b[A\n",
      "5it [00:04,  1.01it/s]\u001b[A\n",
      "6it [00:05,  1.01it/s]\u001b[A\n",
      "7it [00:06,  1.01it/s]\u001b[A\n",
      "8it [00:07,  1.00it/s]\u001b[A\n",
      "9it [00:08,  1.01it/s]\u001b[A\n",
      "10it [00:09,  1.01it/s]\u001b[A\n",
      "11it [00:10,  1.01it/s]\u001b[A\n",
      "12it [00:11,  1.01it/s]\u001b[A\n",
      "13it [00:12,  1.01it/s]\u001b[A\n",
      "14it [00:13,  1.01it/s]\u001b[A\n",
      "15it [00:14,  1.01it/s]\u001b[A\n",
      "16it [00:15,  1.01it/s]\u001b[A\n",
      "17it [00:16,  1.01it/s]\u001b[A\n",
      "18it [00:17,  1.01it/s]\u001b[A\n",
      "19it [00:18,  1.01it/s]\u001b[A\n",
      "20it [00:19,  1.01s/it]\u001b[A\n",
      "21it [00:20,  1.00s/it]\u001b[A\n",
      "22it [00:21,  1.00it/s]\u001b[A\n",
      "23it [00:22,  1.00it/s]\u001b[A\n",
      "24it [00:23,  1.01it/s]\u001b[A\n",
      "25it [00:24,  1.01it/s]\u001b[A\n",
      "26it [00:25,  1.01it/s]\u001b[A\n",
      "27it [00:26,  1.01it/s]\u001b[A\n",
      "28it [00:27,  1.01it/s]\u001b[A\n",
      "29it [00:28,  1.01it/s]\u001b[A\n",
      "30it [00:29,  1.00it/s]\u001b[A\n",
      "31it [00:30,  1.01it/s]\u001b[A\n",
      "32it [00:31,  1.01it/s]\u001b[A\n",
      "33it [00:32,  1.01it/s]\u001b[A\n",
      "34it [00:33,  1.01it/s]\u001b[A\n",
      "35it [00:34,  1.01it/s]\u001b[A\n",
      "36it [00:35,  1.01it/s]\u001b[A\n",
      "37it [00:36,  1.01it/s]\u001b[A\n",
      "38it [00:37,  1.01it/s]\u001b[A\n",
      "39it [00:38,  1.01it/s]\u001b[A\n",
      "40it [00:39,  1.01it/s]\u001b[A\n",
      "41it [00:40,  1.00it/s]\u001b[A\n",
      "42it [00:41,  1.01it/s]\u001b[A\n",
      "43it [00:42,  1.01it/s]\u001b[A\n",
      "44it [00:43,  1.01it/s]\u001b[A\n",
      "45it [00:44,  1.01it/s]\u001b[A\n",
      "46it [00:45,  1.01it/s]\u001b[A\n",
      "47it [00:46,  1.01it/s]\u001b[A\n",
      "48it [00:47,  1.01it/s]\u001b[A\n",
      "49it [00:48,  1.01it/s]\u001b[A\n",
      "50it [00:49,  1.01it/s]\u001b[A\n",
      "51it [00:50,  1.01it/s]\u001b[A\n",
      "52it [00:51,  1.01it/s]\u001b[A\n",
      "53it [00:52,  1.01it/s]\u001b[A\n",
      "54it [00:53,  1.01it/s]\u001b[A\n",
      "55it [00:54,  1.01it/s]\u001b[A\n",
      "56it [00:55,  1.01it/s]\u001b[A\n",
      "57it [00:56,  1.01it/s]\u001b[A\n",
      "58it [00:57,  1.01it/s]\u001b[A\n",
      "59it [00:58,  1.01it/s]\u001b[A\n",
      "60it [00:59,  1.01it/s]\u001b[A\n",
      "61it [01:00,  1.00it/s]\u001b[A\n",
      "62it [01:01,  1.01it/s]\u001b[A\n",
      "63it [01:02,  1.01it/s]\u001b[A\n",
      "64it [01:03,  1.01it/s]\u001b[A\n",
      "65it [01:04,  1.01it/s]\u001b[A\n",
      "66it [01:05,  1.01it/s]\u001b[A\n",
      "67it [01:06,  1.01it/s]\u001b[A\n",
      "68it [01:07,  1.01it/s]\u001b[A\n",
      "69it [01:08,  1.01it/s]\u001b[A\n",
      "70it [01:09,  1.01it/s]\u001b[A\n",
      "71it [01:10,  1.01it/s]\u001b[A\n",
      "72it [01:11,  1.01it/s]\u001b[A\n",
      "73it [01:12,  1.01it/s]\u001b[A\n",
      "74it [01:13,  1.01it/s]\u001b[A\n",
      "75it [01:14,  1.01it/s]\u001b[A\n",
      "76it [01:15,  1.01it/s]\u001b[A\n",
      "77it [01:16,  1.01it/s]\u001b[A\n",
      "78it [01:17,  1.01it/s]\u001b[A\n",
      "79it [01:18,  1.01it/s]\u001b[A\n",
      "80it [01:19,  1.01it/s]\u001b[A\n",
      "81it [01:20,  1.01it/s]\u001b[A\n",
      "82it [01:21,  1.00it/s]\u001b[A\n",
      "83it [01:22,  1.01it/s]\u001b[A\n",
      "84it [01:23,  1.01it/s]\u001b[A\n",
      "85it [01:24,  1.01it/s]\u001b[A\n",
      "86it [01:25,  1.01it/s]\u001b[A\n",
      "87it [01:26,  1.01it/s]\u001b[A\n",
      "88it [01:27,  1.01it/s]\u001b[A\n",
      "89it [01:28,  1.01it/s]\u001b[A\n",
      "90it [01:29,  1.01it/s]\u001b[A\n",
      "91it [01:30,  1.01it/s]\u001b[A\n",
      "92it [01:31,  1.00it/s]\u001b[A\n",
      "93it [01:32,  1.01it/s]\u001b[A\n",
      "94it [01:33,  1.01it/s]\u001b[A\n",
      "95it [01:34,  1.01it/s]\u001b[A\n",
      "96it [01:35,  1.01it/s]\u001b[A\n",
      "97it [01:36,  1.01it/s]\u001b[A\n",
      "98it [01:37,  1.01it/s]\u001b[A\n",
      "99it [01:38,  1.01it/s]\u001b[A\n",
      "100it [01:39,  1.01it/s]\u001b[A\n",
      "101it [01:40,  1.01it/s]\u001b[A\n",
      "102it [01:41,  1.00it/s]\u001b[A\n",
      "103it [01:42,  1.01it/s]\u001b[A\n",
      "104it [01:42,  1.01it/s]\u001b[A\n",
      "105it [01:43,  1.01it/s]\u001b[A\n",
      "106it [01:44,  1.01it/s]\u001b[A\n",
      "107it [01:45,  1.01it/s]\u001b[A\n",
      "108it [01:46,  1.01it/s]\u001b[A\n",
      "109it [01:47,  1.01it/s]\u001b[A\n",
      "110it [01:48,  1.01it/s]\u001b[A\n",
      "111it [01:49,  1.01it/s]\u001b[A\n",
      "112it [01:50,  1.01it/s]\u001b[A\n",
      "113it [01:51,  1.01it/s]\u001b[A\n",
      "114it [01:52,  1.01it/s]\u001b[A\n",
      "115it [01:53,  1.01it/s]\u001b[A\n",
      "116it [01:54,  1.01it/s]\u001b[A\n",
      "117it [01:55,  1.01it/s]\u001b[A\n",
      "118it [01:56,  1.01it/s]\u001b[A\n",
      "119it [01:57,  1.01it/s]\u001b[A\n",
      "120it [01:58,  1.01it/s]\u001b[A\n",
      "121it [01:59,  1.01it/s]\u001b[A\n",
      "122it [02:00,  1.01it/s]\u001b[A\n",
      "123it [02:01,  1.00it/s]\u001b[A\n",
      "124it [02:02,  1.01it/s]\u001b[A\n",
      "125it [02:03,  1.01it/s]\u001b[A\n",
      "126it [02:04,  1.01it/s]\u001b[A\n",
      "127it [02:05,  1.01it/s]\u001b[A\n",
      "128it [02:06,  1.01it/s]\u001b[A\n",
      "129it [02:07,  1.01it/s]\u001b[A\n",
      "130it [02:08,  1.01it/s]\u001b[A\n",
      "131it [02:09,  1.01it/s]\u001b[A\n",
      "132it [02:10,  1.01it/s]\u001b[A\n",
      "133it [02:11,  1.00it/s]\u001b[A\n",
      "134it [02:12,  1.01it/s]\u001b[A\n",
      "135it [02:13,  1.01it/s]\u001b[A\n",
      "136it [02:14,  1.01it/s]\u001b[A\n",
      "137it [02:15,  1.01it/s]\u001b[A\n",
      "138it [02:16,  1.01it/s]\u001b[A\n",
      "139it [02:17,  1.01it/s]\u001b[A\n",
      "140it [02:18,  1.01it/s]\u001b[A\n",
      "141it [02:19,  1.00it/s]\u001b[A\n",
      "142it [02:20,  1.00s/it]\u001b[A\n",
      "143it [02:21,  1.00s/it]\u001b[A\n",
      "144it [02:22,  1.00it/s]\u001b[A\n",
      "145it [02:23,  1.01it/s]\u001b[A\n",
      "146it [02:24,  1.01it/s]\u001b[A\n",
      "147it [02:25,  1.01it/s]\u001b[A\n",
      "148it [02:26,  1.01it/s]\u001b[A\n",
      "149it [02:27,  1.01it/s]\u001b[A\n",
      "150it [02:28,  1.01it/s]\u001b[A\n",
      "151it [02:29,  1.01it/s]\u001b[A\n",
      "152it [02:30,  1.00it/s]\u001b[A\n",
      "153it [02:31,  1.01it/s]\u001b[A\n",
      "154it [02:32,  1.01it/s]\u001b[A\n",
      "155it [02:33,  1.01it/s]\u001b[A\n",
      "156it [02:34,  1.01it/s]\u001b[A\n",
      "157it [02:35,  1.01it/s]\u001b[A\n",
      "158it [02:36,  1.01it/s]\u001b[A\n",
      "159it [02:37,  1.04it/s]\u001b[A\n",
      "160it [02:38,  1.03it/s]\u001b[A\n",
      "161it [02:39,  1.03it/s]\u001b[A\n",
      "162it [02:40,  1.01it/s]\u001b[A\n",
      "163it [02:41,  1.01it/s]\u001b[A\n",
      "164it [02:42,  1.01it/s]\u001b[A\n",
      "165it [02:43,  1.01it/s]\u001b[A\n",
      "166it [02:44,  1.01it/s]\u001b[A\n",
      "167it [02:45,  1.01it/s]\u001b[A\n",
      "168it [02:46,  1.01it/s]\u001b[A\n",
      "169it [02:47,  1.01it/s]\u001b[A\n",
      "170it [02:48,  1.01it/s]\u001b[A\n",
      "171it [02:49,  1.01it/s]\u001b[A\n",
      "172it [02:50,  1.01it/s]\u001b[A\n",
      "173it [02:51,  1.01it/s]\u001b[A\n",
      "174it [02:52,  1.01it/s]\u001b[A\n",
      "175it [02:53,  1.01it/s]\u001b[A\n",
      "176it [02:54,  1.01it/s]\u001b[A\n",
      "177it [02:55,  1.01it/s]\u001b[A\n",
      "178it [02:56,  1.01it/s]\u001b[A\n",
      "179it [02:57,  1.01it/s]\u001b[A\n",
      "180it [02:58,  1.01it/s]\u001b[A\n",
      "181it [02:59,  1.01it/s]\u001b[A\n",
      "182it [03:00,  1.01it/s]\u001b[A\n",
      "183it [03:01,  1.00it/s]\u001b[A\n",
      "184it [03:02,  1.01it/s]\u001b[A\n",
      "185it [03:03,  1.01it/s]\u001b[A\n",
      "186it [03:04,  1.01it/s]\u001b[A\n",
      "187it [03:05,  1.01it/s]\u001b[A\n",
      "188it [03:06,  1.01it/s]\u001b[A\n",
      "189it [03:07,  1.01it/s]\u001b[A\n",
      "190it [03:08,  1.01it/s]\u001b[A\n",
      "191it [03:09,  1.01it/s]\u001b[A\n",
      "192it [03:10,  1.01it/s]\u001b[A\n",
      "193it [03:11,  1.00it/s]\u001b[A\n",
      "194it [03:12,  1.01it/s]\u001b[A\n",
      "195it [03:13,  1.01it/s]\u001b[A\n",
      "196it [03:13,  1.01it/s]\u001b[A\n",
      "197it [03:14,  1.01it/s]\u001b[A\n",
      "198it [03:15,  1.01it/s]\u001b[A\n",
      "199it [03:16,  1.01it/s]\u001b[A\n",
      "200it [03:17,  1.01it/s]\u001b[A\n",
      "201it [03:18,  1.01it/s]\u001b[A\n",
      "202it [03:19,  1.01it/s]\u001b[A\n",
      "203it [03:20,  1.00it/s]\u001b[A\n",
      "204it [03:21,  1.01it/s]\u001b[A\n",
      "205it [03:22,  1.01it/s]\u001b[A\n",
      "206it [03:23,  1.01it/s]\u001b[A\n",
      "207it [03:24,  1.01it/s]\u001b[A\n",
      "208it [03:25,  1.01it/s]\u001b[A\n",
      "209it [03:26,  1.01it/s]\u001b[A\n",
      "210it [03:27,  1.01it/s]\u001b[A\n",
      "211it [03:28,  1.01it/s]\u001b[A\n",
      "212it [03:29,  1.01it/s]\u001b[A\n",
      "213it [03:30,  1.06it/s]\u001b[A\n",
      "214it [03:31,  1.04it/s]\u001b[A\n",
      "215it [03:32,  1.03it/s]\u001b[A\n",
      "216it [03:33,  1.03it/s]\u001b[A\n",
      "217it [03:34,  1.02it/s]\u001b[A\n",
      "218it [03:35,  1.02it/s]\u001b[A\n",
      "219it [03:36,  1.02it/s]\u001b[A\n",
      "220it [03:37,  1.02it/s]\u001b[A\n",
      "221it [03:38,  1.02it/s]\u001b[A\n",
      "222it [03:39,  1.01it/s]\u001b[A\n",
      "223it [03:40,  1.01it/s]\u001b[A\n",
      "224it [03:41,  1.05it/s]\u001b[A\n",
      "225it [03:42,  1.04it/s]\u001b[A\n",
      "226it [03:43,  1.03it/s]\u001b[A\n",
      "227it [03:44,  1.03it/s]\u001b[A\n",
      "228it [03:45,  1.02it/s]\u001b[A\n",
      "229it [03:46,  1.02it/s]\u001b[A\n",
      "230it [03:47,  1.02it/s]\u001b[A\n",
      "231it [03:48,  1.02it/s]\u001b[A\n",
      "232it [03:49,  1.01it/s]\u001b[A\n",
      "233it [03:50,  1.01it/s]\u001b[A\n",
      "234it [03:51,  1.01it/s]\u001b[A\n",
      "235it [03:52,  1.01it/s]\u001b[A\n",
      "236it [03:53,  1.01it/s]\u001b[A\n",
      "237it [03:54,  1.01it/s]\u001b[A\n",
      "238it [03:55,  1.01it/s]\u001b[A\n",
      "239it [03:56,  1.01it/s]\u001b[A\n",
      "240it [03:57,  1.01it/s]\u001b[A\n",
      "241it [03:58,  1.01it/s]\u001b[A\n",
      "242it [03:59,  1.01it/s]\u001b[A\n",
      "243it [04:00,  1.01it/s]\u001b[A\n",
      "244it [04:01,  1.01it/s]\u001b[A\n",
      "245it [04:02,  1.00it/s]\u001b[A\n",
      "246it [04:03,  1.01it/s]\u001b[A\n",
      "247it [04:04,  1.01it/s]\u001b[A\n",
      "248it [04:05,  1.01it/s]\u001b[A\n",
      "249it [04:06,  1.03it/s]\u001b[A\n",
      "250it [04:07,  1.03it/s]\u001b[A\n",
      "251it [04:08,  1.02it/s]\u001b[A\n",
      "252it [04:09,  1.02it/s]\u001b[A\n",
      "253it [04:10,  1.02it/s]\u001b[A\n",
      "254it [04:11,  1.02it/s]\u001b[A\n",
      "255it [04:12,  1.01it/s]\u001b[A\n",
      "256it [04:13,  1.01it/s]\u001b[A\n",
      "257it [04:14,  1.01it/s]\u001b[A\n",
      "258it [04:14,  1.01it/s]\u001b[A\n",
      "259it [04:15,  1.01it/s]\u001b[A\n",
      "260it [04:16,  1.06it/s]\u001b[A\n",
      "261it [04:17,  1.04it/s]\u001b[A\n",
      "262it [04:18,  1.03it/s]\u001b[A\n",
      "263it [04:19,  1.02it/s]\u001b[A\n",
      "264it [04:20,  1.00it/s]\u001b[A\n",
      "265it [04:21,  1.01it/s]\u001b[A\n",
      "266it [04:22,  1.01it/s]\u001b[A\n",
      "267it [04:23,  1.01it/s]\u001b[A\n",
      "268it [04:24,  1.01it/s]\u001b[A\n",
      "269it [04:25,  1.01it/s]\u001b[A\n",
      "270it [04:26,  1.01it/s]\u001b[A\n",
      "271it [04:27,  1.01it/s]\u001b[A\n",
      "272it [04:28,  1.01it/s]\u001b[A\n",
      "273it [04:29,  1.01it/s]\u001b[A\n",
      "274it [04:30,  1.00it/s]\u001b[A\n",
      "275it [04:31,  1.01it/s]\u001b[A\n",
      "276it [04:32,  1.01it/s]\u001b[A\n",
      "277it [04:33,  1.01it/s]\u001b[A\n",
      "278it [04:34,  1.01it/s]\u001b[A\n",
      "279it [04:35,  1.01it/s]\u001b[A\n",
      "280it [04:36,  1.01it/s]\u001b[A\n",
      "281it [04:37,  1.01it/s]\u001b[A\n",
      "282it [04:38,  1.01it/s]\u001b[A\n",
      "283it [04:39,  1.01it/s]\u001b[A\n",
      "284it [04:40,  1.00it/s]\u001b[A\n",
      "285it [04:41,  1.01it/s]\u001b[A\n",
      "286it [04:42,  1.01it/s]\u001b[A\n",
      "287it [04:43,  1.01it/s]\u001b[A\n",
      "288it [04:44,  1.01it/s]\u001b[A\n",
      "289it [04:45,  1.01it/s]\u001b[A\n",
      "290it [04:46,  1.01it/s]\u001b[A\n",
      "291it [04:47,  1.01it/s]\u001b[A\n",
      "292it [04:48,  1.01it/s]\u001b[A\n",
      "293it [04:49,  1.01it/s]\u001b[A\n",
      "294it [04:50,  1.00it/s]\u001b[A\n",
      "295it [04:51,  1.01it/s]\u001b[A\n",
      "296it [04:52,  1.01it/s]\u001b[A\n",
      "297it [04:53,  1.01it/s]\u001b[A\n",
      "298it [04:54,  1.01it/s]\u001b[A\n",
      "299it [04:55,  1.01it/s]\u001b[A\n",
      "300it [04:56,  1.01it/s]\u001b[A\n",
      "301it [04:57,  1.01it/s]\u001b[A\n",
      "302it [04:58,  1.01it/s]\u001b[A\n",
      "303it [04:59,  1.01it/s]\u001b[A\n",
      "304it [05:00,  1.01it/s]\u001b[A\n",
      "305it [05:01,  1.00it/s]\u001b[A\n",
      "306it [05:02,  1.01it/s]\u001b[A\n",
      "307it [05:03,  1.01it/s]\u001b[A\n",
      "308it [05:04,  1.01it/s]\u001b[A\n",
      "309it [05:05,  1.01it/s]\u001b[A\n",
      "310it [05:06,  1.01it/s]\u001b[A\n",
      "311it [05:07,  1.01it/s]\u001b[A\n",
      "312it [05:08,  1.01it/s]\u001b[A\n",
      "313it [05:09,  1.01it/s]\u001b[A\n",
      "314it [05:10,  1.01it/s]\u001b[A\n",
      "315it [05:11,  1.00it/s]\u001b[A\n",
      "316it [05:12,  1.01it/s]\u001b[A\n",
      "317it [05:13,  1.01it/s]\u001b[A\n",
      "318it [05:14,  1.01it/s]\u001b[A\n",
      "319it [05:15,  1.01it/s]\u001b[A\n",
      "320it [05:16,  1.01it/s]\u001b[A\n",
      "321it [05:17,  1.01it/s]\u001b[A\n",
      "322it [05:18,  1.01it/s]\u001b[A\n",
      "323it [05:19,  1.01it/s]\u001b[A\n",
      "324it [05:20,  1.01it/s]\u001b[A\n",
      "325it [05:21,  1.00it/s]\u001b[A\n",
      "326it [05:22,  1.00it/s]\u001b[A\n",
      "327it [05:23,  1.01it/s]\u001b[A\n",
      "328it [05:24,  1.01it/s]\u001b[A\n",
      "329it [05:25,  1.01it/s]\u001b[A\n",
      "330it [05:26,  1.01it/s]\u001b[A\n",
      "331it [05:27,  1.01it/s]\u001b[A\n",
      "332it [05:28,  1.01it/s]\u001b[A\n",
      "333it [05:29,  1.01it/s]\u001b[A\n",
      "334it [05:30,  1.01it/s]\u001b[A\n",
      "335it [05:31,  1.01it/s]\u001b[A\n",
      "336it [05:32,  1.01it/s]\u001b[A\n",
      "337it [05:33,  1.01it/s]\u001b[A\n",
      "338it [05:34,  1.01it/s]\u001b[A\n",
      "339it [05:35,  1.01it/s]\u001b[A\n",
      "340it [05:36,  1.01it/s]\u001b[A\n",
      "341it [05:37,  1.01it/s]\u001b[A\n",
      "342it [05:38,  1.01it/s]\u001b[A\n",
      "343it [05:39,  1.01it/s]\u001b[A\n",
      "344it [05:39,  1.01it/s]\u001b[A\n",
      "345it [05:40,  1.01it/s]\u001b[A\n",
      "346it [05:41,  1.00it/s]\u001b[A\n",
      "347it [05:42,  1.01it/s]\u001b[A\n",
      "348it [05:43,  1.01it/s]\u001b[A\n",
      "349it [05:44,  1.01it/s]\u001b[A\n",
      "350it [05:45,  1.01it/s]\u001b[A\n",
      "351it [05:46,  1.01it/s]\u001b[A\n",
      "352it [05:47,  1.01it/s]\u001b[A\n",
      "353it [05:48,  1.01it/s]\u001b[A\n",
      "354it [05:49,  1.01it/s]\u001b[A\n",
      "355it [05:50,  1.01it/s]\u001b[A\n",
      "356it [05:51,  1.00it/s]\u001b[A\n",
      "357it [05:52,  1.01it/s]\u001b[A\n",
      "358it [05:53,  1.01it/s]\u001b[A\n",
      "359it [05:54,  1.01it/s]\u001b[A\n",
      "360it [05:55,  1.01it/s]\u001b[A\n",
      "361it [05:56,  1.01it/s]\u001b[A\n",
      "362it [05:57,  1.01it/s]\u001b[A\n",
      "363it [05:58,  1.01it/s]\u001b[A\n",
      "364it [05:59,  1.01it/s]\u001b[A\n",
      "365it [06:00,  1.01it/s]\u001b[A\n",
      "366it [06:01,  1.00it/s]\u001b[A\n",
      "367it [06:02,  1.01it/s]\u001b[A\n",
      "368it [06:03,  1.01it/s]\u001b[A\n",
      "369it [06:04,  1.01it/s]\u001b[A\n",
      "370it [06:05,  1.01it/s]\u001b[A\n",
      "371it [06:06,  1.01it/s]\u001b[A\n",
      "372it [06:07,  1.01it/s]\u001b[A\n",
      "373it [06:08,  1.01it/s]\u001b[A\n",
      "374it [06:09,  1.01it/s]\u001b[A\n",
      "375it [06:10,  1.01it/s]\u001b[A\n",
      "376it [06:11,  1.01it/s]\u001b[A\n",
      "377it [06:12,  1.00it/s]\u001b[A\n",
      "378it [06:13,  1.01it/s]\u001b[A\n",
      "379it [06:14,  1.01it/s]\u001b[A\n",
      "380it [06:15,  1.01it/s]\u001b[A\n",
      "381it [06:16,  1.01it/s]\u001b[A\n",
      "382it [06:17,  1.01it/s]\u001b[A\n",
      "383it [06:18,  1.01it/s]\u001b[A\n",
      "384it [06:19,  1.01it/s]\u001b[A\n",
      "385it [06:20,  1.01s/it]\u001b[A\n",
      "386it [06:21,  1.00s/it]\u001b[A\n",
      "387it [06:22,  1.00it/s]\u001b[A\n",
      "388it [06:23,  1.01it/s]\u001b[A\n",
      "389it [06:24,  1.01it/s]\u001b[A\n",
      "390it [06:25,  1.01it/s]\u001b[A\n",
      "391it [06:26,  1.01it/s]\u001b[A\n",
      "392it [06:27,  1.01it/s]\u001b[A\n",
      "393it [06:28,  1.01it/s]\u001b[A\n",
      "394it [06:29,  1.01it/s]\u001b[A\n",
      "395it [06:30,  1.01it/s]\u001b[A\n",
      "396it [06:31,  1.00it/s]\u001b[A\n",
      "397it [06:32,  1.01it/s]\u001b[A\n",
      "398it [06:33,  1.01it/s]\u001b[A\n",
      "399it [06:34,  1.01it/s]\u001b[A\n",
      "400it [06:35,  1.01it/s]\u001b[A\n",
      "401it [06:36,  1.01it/s]\u001b[A\n",
      "402it [06:37,  1.01it/s]\u001b[A\n",
      "403it [06:38,  1.01it/s]\u001b[A\n",
      "404it [06:39,  1.01it/s]\u001b[A\n",
      "405it [06:40,  1.01it/s]\u001b[A\n",
      "406it [06:41,  1.00it/s]\u001b[A\n",
      "407it [06:42,  1.01it/s]\u001b[A\n",
      "408it [06:43,  1.01it/s]\u001b[A\n",
      "409it [06:44,  1.01it/s]\u001b[A\n",
      "410it [06:45,  1.01it/s]\u001b[A\n",
      "411it [06:46,  1.01it/s]\u001b[A\n",
      "412it [06:47,  1.01it/s]\u001b[A\n",
      "413it [06:48,  1.01it/s]\u001b[A\n",
      "414it [06:49,  1.01it/s]\u001b[A\n",
      "415it [06:50,  1.01it/s]\u001b[A\n",
      "416it [06:51,  1.00it/s]\u001b[A\n",
      "417it [06:52,  1.01it/s]\u001b[A\n",
      "418it [06:53,  1.01it/s]\u001b[A\n",
      "419it [06:54,  1.01it/s]\u001b[A\n",
      "420it [06:55,  1.01it/s]\u001b[A\n",
      "421it [06:56,  1.01it/s]\u001b[A\n",
      "422it [06:57,  1.01it/s]\u001b[A\n",
      "423it [06:58,  1.01it/s]\u001b[A\n",
      "424it [06:59,  1.01it/s]\u001b[A\n",
      "425it [07:00,  1.01it/s]\u001b[A\n",
      "426it [07:01,  1.00it/s]\u001b[A\n",
      "427it [07:02,  1.01it/s]\u001b[A\n",
      "428it [07:03,  1.01it/s]\u001b[A\n",
      "429it [07:04,  1.01it/s]\u001b[A\n",
      "430it [07:05,  1.01it/s]\u001b[A\n",
      "431it [07:06,  1.01it/s]\u001b[A\n",
      "432it [07:07,  1.01it/s]\u001b[A\n",
      "433it [07:08,  1.01it/s]\u001b[A\n",
      "434it [07:09,  1.01it/s]\u001b[A\n",
      "435it [07:10,  1.01it/s]\u001b[A\n",
      "436it [07:11,  1.01it/s]\u001b[A\n",
      "437it [07:12,  1.00it/s]\u001b[A\n",
      "438it [07:13,  1.01it/s]\u001b[A\n",
      "439it [07:14,  1.01it/s]\u001b[A\n",
      "440it [07:15,  1.01it/s]\u001b[A\n",
      "441it [07:16,  1.01it/s]\u001b[A\n",
      "442it [07:17,  1.01it/s]\u001b[A\n",
      "443it [07:18,  1.01it/s]\u001b[A\n",
      "444it [07:19,  1.01it/s]\u001b[A\n",
      "445it [07:20,  1.01it/s]\u001b[A\n",
      "446it [07:21,  1.01it/s]\u001b[A\n",
      "447it [07:22,  1.00it/s]\u001b[A\n",
      "448it [07:23,  1.00it/s]\u001b[A\n",
      "449it [07:24,  1.01it/s]\u001b[A\n",
      "450it [07:24,  1.01it/s]\u001b[A\n",
      "451it [07:25,  1.01it/s]\u001b[A\n",
      "452it [07:26,  1.01it/s]\u001b[A\n",
      "453it [07:27,  1.01it/s]\u001b[A\n",
      "454it [07:28,  1.01it/s]\u001b[A\n",
      "455it [07:29,  1.01it/s]\u001b[A\n",
      "456it [07:30,  1.01it/s]\u001b[A\n",
      "457it [07:31,  1.00it/s]\u001b[A\n",
      "458it [07:32,  1.01it/s]\u001b[A\n",
      "459it [07:33,  1.01it/s]\u001b[A\n",
      "460it [07:34,  1.01it/s]\u001b[A\n",
      "461it [07:35,  1.01it/s]\u001b[A\n",
      "462it [07:36,  1.01it/s]\u001b[A\n",
      "463it [07:37,  1.01it/s]\u001b[A\n",
      "464it [07:38,  1.01it/s]\u001b[A\n",
      "465it [07:39,  1.01it/s]\u001b[A\n",
      "466it [07:40,  1.01it/s]\u001b[A\n",
      "467it [07:41,  1.01it/s]\u001b[A\n",
      "468it [07:42,  1.00it/s]\u001b[A\n",
      "469it [07:43,  1.01it/s]\u001b[A\n",
      "470it [07:44,  1.01it/s]\u001b[A\n",
      "471it [07:45,  1.01it/s]\u001b[A\n",
      "472it [07:46,  1.01it/s]\u001b[A\n",
      "473it [07:47,  1.01it/s]\u001b[A\n",
      "474it [07:48,  1.01it/s]\u001b[A\n",
      "475it [07:49,  1.01it/s]\u001b[A\n",
      "476it [07:50,  1.01it/s]\u001b[A\n",
      "477it [07:51,  1.01it/s]\u001b[A\n",
      "478it [07:52,  1.00it/s]\u001b[A\n",
      "479it [07:53,  1.01it/s]\u001b[A\n",
      "480it [07:54,  1.01it/s]\u001b[A\n",
      "481it [07:55,  1.01it/s]\u001b[A\n",
      "482it [07:56,  1.01it/s]\u001b[A\n",
      "483it [07:57,  1.01it/s]\u001b[A\n",
      "484it [07:58,  1.01it/s]\u001b[A\n",
      "485it [07:59,  1.01it/s]\u001b[A\n",
      "486it [08:00,  1.01it/s]\u001b[A\n",
      "487it [08:01,  1.01it/s]\u001b[A\n",
      "488it [08:02,  1.00it/s]\u001b[A\n",
      "489it [08:03,  1.01it/s]\u001b[A\n",
      "490it [08:04,  1.01it/s]\u001b[A\n",
      "491it [08:05,  1.01it/s]\u001b[A\n",
      "492it [08:06,  1.01it/s]\u001b[A\n",
      "493it [08:07,  1.01it/s]\u001b[A\n",
      "494it [08:08,  1.01it/s]\u001b[A\n",
      "495it [08:09,  1.01it/s]\u001b[A\n",
      "496it [08:10,  1.01it/s]\u001b[A\n",
      "497it [08:11,  1.01it/s]\u001b[A\n",
      "498it [08:12,  1.01it/s]\u001b[A\n",
      "499it [08:13,  1.00it/s]\u001b[A\n",
      "500it [08:14,  1.01it/s]\u001b[A\n",
      "501it [08:15,  1.01it/s]\u001b[A\n",
      "502it [08:16,  1.01it/s]\u001b[A\n",
      "503it [08:17,  1.01it/s]\u001b[A\n",
      "504it [08:18,  1.01it/s]\u001b[A\n",
      "505it [08:19,  1.01it/s]\u001b[A\n",
      "506it [08:20,  1.01it/s]\u001b[A\n",
      "507it [08:21,  1.01s/it]\u001b[A\n",
      "508it [08:22,  1.00s/it]\u001b[A\n",
      "509it [08:23,  1.00it/s]\u001b[A\n",
      "510it [08:24,  1.01it/s]\u001b[A\n",
      "511it [08:25,  1.01it/s]\u001b[A\n",
      "512it [08:26,  1.01it/s]\u001b[A\n",
      "513it [08:27,  1.01it/s]\u001b[A\n",
      "514it [08:28,  1.01it/s]\u001b[A\n",
      "515it [08:29,  1.01it/s]\u001b[A\n",
      "516it [08:30,  1.01it/s]\u001b[A\n",
      "517it [08:31,  1.00it/s]\u001b[A\n",
      "518it [08:32,  1.01it/s]\u001b[A\n",
      "519it [08:33,  1.01it/s]\u001b[A\n",
      "520it [08:34,  1.01it/s]\u001b[A\n",
      "521it [08:35,  1.01it/s]\u001b[A\n",
      "522it [08:36,  1.01it/s]\u001b[A\n",
      "523it [08:37,  1.01it/s]\u001b[A\n",
      "524it [08:38,  1.01it/s]\u001b[A\n",
      "525it [08:39,  1.01it/s]\u001b[A\n",
      "526it [08:40,  1.01it/s]\u001b[A\n",
      "527it [08:41,  1.00it/s]\u001b[A\n",
      "528it [08:42,  1.01it/s]\u001b[A\n",
      "529it [08:43,  1.01it/s]\u001b[A\n",
      "530it [08:44,  1.01it/s]\u001b[A\n",
      "531it [08:45,  1.01it/s]\u001b[A\n",
      "532it [08:46,  1.01it/s]\u001b[A\n",
      "533it [08:47,  1.03it/s]\u001b[A\n",
      "534it [08:48,  1.03it/s]\u001b[A\n",
      "535it [08:49,  1.02it/s]\u001b[A\n",
      "536it [08:50,  1.02it/s]\u001b[A\n",
      "537it [08:51,  1.02it/s]\u001b[A\n",
      "538it [08:52,  1.01it/s]\u001b[A\n",
      "539it [08:53,  1.01it/s]\u001b[A\n",
      "540it [08:54,  1.01it/s]\u001b[A\n",
      "541it [08:55,  1.01it/s]\u001b[A\n",
      "542it [08:56,  1.01it/s]\u001b[A\n",
      "543it [08:57,  1.01it/s]\u001b[A\n",
      "544it [08:58,  1.01it/s]\u001b[A\n",
      "545it [08:59,  1.01it/s]\u001b[A\n",
      "546it [09:00,  1.01it/s]\u001b[A\n",
      "547it [09:00,  1.07it/s]\u001b[A\n",
      "548it [09:01,  1.07it/s]\u001b[A\n",
      "549it [09:02,  1.11it/s]\u001b[A\n",
      "550it [09:03,  1.08it/s]\u001b[A\n",
      "551it [09:04,  1.06it/s]\u001b[A\n",
      "552it [09:05,  1.04it/s]\u001b[A\n",
      "553it [09:06,  1.06it/s]\u001b[A\n",
      "554it [09:07,  1.04it/s]\u001b[A\n",
      "555it [09:08,  1.03it/s]\u001b[A\n",
      "556it [09:09,  1.03it/s]\u001b[A\n",
      "557it [09:10,  1.02it/s]\u001b[A\n",
      "558it [09:11,  1.07it/s]\u001b[A\n",
      "559it [09:12,  1.04it/s]\u001b[A\n",
      "560it [09:13,  1.07it/s]\u001b[A\n",
      "561it [09:14,  1.05it/s]\u001b[A\n",
      "562it [09:15,  1.04it/s]\u001b[A\n",
      "563it [09:16,  1.03it/s]\u001b[A\n",
      "564it [09:17,  1.03it/s]\u001b[A\n",
      "565it [09:18,  1.02it/s]\u001b[A\n",
      "566it [09:19,  1.02it/s]\u001b[A\n",
      "567it [09:20,  1.02it/s]\u001b[A\n",
      "568it [09:21,  1.02it/s]\u001b[A\n",
      "569it [09:22,  1.00it/s]\u001b[A\n",
      "570it [09:23,  1.01it/s]\u001b[A\n",
      "571it [09:24,  1.01it/s]\u001b[A\n",
      "572it [09:25,  1.01it/s]\u001b[A\n",
      "573it [09:26,  1.01it/s]\u001b[A\n",
      "574it [09:27,  1.01it/s]\u001b[A\n",
      "575it [09:27,  1.01it/s]\u001b[A\n",
      "576it [09:28,  1.01it/s]\u001b[A\n",
      "577it [09:29,  1.01it/s]\u001b[A\n",
      "578it [09:30,  1.01it/s]\u001b[A\n",
      "579it [09:31,  1.00it/s]\u001b[A\n",
      "580it [09:32,  1.01it/s]\u001b[A\n",
      "581it [09:33,  1.01it/s]\u001b[A\n",
      "582it [09:34,  1.01it/s]\u001b[A\n",
      "583it [09:35,  1.01it/s]\u001b[A\n",
      "584it [09:36,  1.01it/s]\u001b[A\n",
      "585it [09:37,  1.01it/s]\u001b[A\n",
      "586it [09:38,  1.01it/s]\u001b[A\n",
      "587it [09:39,  1.01it/s]\u001b[A\n",
      "588it [09:40,  1.01it/s]\u001b[A\n",
      "589it [09:41,  1.01it/s]\u001b[A\n",
      "590it [09:42,  1.00it/s]\u001b[A\n",
      "591it [09:43,  1.01it/s]\u001b[A\n",
      "592it [09:44,  1.01it/s]\u001b[A\n",
      "593it [09:45,  1.01it/s]\u001b[A\n",
      "594it [09:46,  1.01it/s]\u001b[A\n",
      "595it [09:47,  1.01it/s]\u001b[A\n",
      "596it [09:48,  1.01it/s]\u001b[A\n",
      "597it [09:49,  1.01it/s]\u001b[A\n",
      "598it [09:50,  1.01it/s]\u001b[A\n",
      "599it [09:51,  1.06it/s]\u001b[A\n",
      "600it [09:52,  1.04it/s]\u001b[A\n",
      "601it [09:53,  1.03it/s]\u001b[A\n",
      "602it [09:54,  1.02it/s]\u001b[A\n",
      "603it [09:55,  1.02it/s]\u001b[A\n",
      "604it [09:56,  1.02it/s]\u001b[A\n",
      "605it [09:57,  1.02it/s]\u001b[A\n",
      "606it [09:58,  1.02it/s]\u001b[A\n",
      "607it [09:59,  1.01it/s]\u001b[A\n",
      "608it [10:00,  1.01it/s]\u001b[A\n",
      "609it [10:01,  1.01it/s]\u001b[A\n",
      "610it [10:02,  1.01it/s]\u001b[A\n",
      "611it [10:03,  1.01it/s]\u001b[A\n",
      "612it [10:04,  1.01it/s]\u001b[A\n",
      "613it [10:05,  1.01it/s]\u001b[A\n",
      "614it [10:06,  1.01it/s]\u001b[A\n",
      "615it [10:07,  1.01it/s]\u001b[A\n",
      "616it [10:08,  1.01it/s]\u001b[A\n",
      "617it [10:09,  1.03it/s]\u001b[A\n",
      "618it [10:10,  1.02it/s]\u001b[A\n",
      "619it [10:11,  1.02it/s]\u001b[A\n",
      "620it [10:12,  1.02it/s]\u001b[A\n",
      "621it [10:13,  1.01it/s]\u001b[A\n",
      "622it [10:14,  1.01it/s]\u001b[A\n",
      "623it [10:15,  1.01it/s]\u001b[A\n",
      "624it [10:16,  1.02it/s]\u001b[A\n",
      "625it [10:17,  1.02it/s]\u001b[A\n",
      "626it [10:18,  1.02it/s]\u001b[A\n",
      "627it [10:19,  1.01it/s]\u001b[A\n",
      "628it [10:20,  1.01it/s]\u001b[A\n",
      "629it [10:21,  1.00it/s]\u001b[A\n",
      "630it [10:22,  1.00s/it]\u001b[A\n",
      "631it [10:23,  1.05it/s]\u001b[A\n",
      "632it [10:23,  1.08it/s]\u001b[A\n",
      "633it [10:24,  1.06it/s]\u001b[A\n",
      "634it [10:25,  1.04it/s]\u001b[A\n",
      "635it [10:26,  1.03it/s]\u001b[A\n",
      "636it [10:27,  1.03it/s]\u001b[A\n",
      "637it [10:28,  1.02it/s]\u001b[A\n",
      "638it [10:29,  1.02it/s]\u001b[A\n",
      "639it [10:30,  1.02it/s]\u001b[A\n",
      "640it [10:31,  1.01it/s]\u001b[A\n",
      "641it [10:32,  1.01it/s]\u001b[A\n",
      "642it [10:33,  1.01it/s]\u001b[A\n",
      "643it [10:34,  1.01it/s]\u001b[A\n",
      "644it [10:35,  1.01it/s]\u001b[A\n",
      "645it [10:36,  1.01it/s]\u001b[A\n",
      "646it [10:37,  1.01it/s]\u001b[A\n",
      "647it [10:38,  1.01it/s]\u001b[A\n",
      "648it [10:39,  1.01it/s]\u001b[A\n",
      "649it [10:40,  1.01it/s]\u001b[A\n",
      "650it [10:41,  1.00it/s]\u001b[A\n",
      "651it [10:42,  1.01it/s]\u001b[A\n",
      "652it [10:43,  1.01it/s]\u001b[A\n",
      "653it [10:44,  1.01it/s]\u001b[A\n",
      "654it [10:45,  1.01it/s]\u001b[A\n",
      "655it [10:46,  1.01it/s]\u001b[A\n",
      "656it [10:47,  1.01it/s]\u001b[A\n",
      "657it [10:48,  1.01it/s]\u001b[A\n",
      "658it [10:49,  1.01it/s]\u001b[A\n",
      "659it [10:50,  1.01it/s]\u001b[A\n",
      "660it [10:51,  1.01it/s]\u001b[A\n",
      "661it [10:52,  1.00it/s]\u001b[A\n",
      "662it [10:53,  1.01it/s]\u001b[A\n",
      "663it [10:54,  1.01it/s]\u001b[A\n",
      "664it [10:55,  1.01it/s]\u001b[A\n",
      "665it [10:56,  1.01it/s]\u001b[A\n",
      "666it [10:57,  1.01it/s]\u001b[A\n",
      "667it [10:58,  1.01it/s]\u001b[A\n",
      "668it [10:59,  1.01it/s]\u001b[A\n",
      "669it [11:00,  1.01it/s]\u001b[A\n",
      "670it [11:01,  1.01it/s]\u001b[A\n",
      "671it [11:02,  1.00it/s]\u001b[A\n",
      "672it [11:03,  1.01it/s]\u001b[A\n",
      "673it [11:04,  1.01it/s]\u001b[A\n",
      "674it [11:05,  1.01it/s]\u001b[A\n",
      "675it [11:06,  1.01it/s]\u001b[A\n",
      "676it [11:07,  1.01it/s]\u001b[A\n",
      "677it [11:08,  1.01it/s]\u001b[A\n",
      "678it [11:09,  1.01it/s]\u001b[A\n",
      "679it [11:10,  1.01it/s]\u001b[A\n",
      "680it [11:11,  1.01it/s]\u001b[A\n",
      "681it [11:12,  1.00it/s]\u001b[A\n",
      "682it [11:13,  1.01it/s]\u001b[A\n",
      "683it [11:14,  1.01it/s]\u001b[A\n",
      "684it [11:15,  1.01it/s]\u001b[A\n",
      "685it [11:16,  1.01it/s]\u001b[A\n",
      "686it [11:17,  1.01it/s]\u001b[A\n",
      "687it [11:18,  1.01it/s]\u001b[A\n",
      "688it [11:19,  1.01it/s]\u001b[A\n",
      "689it [11:20,  1.01it/s]\u001b[A\n",
      "690it [11:21,  1.01it/s]\u001b[A\n",
      "691it [11:22,  1.00it/s]\u001b[A\n",
      "692it [11:23,  1.01it/s]\u001b[A\n",
      "693it [11:24,  1.01it/s]\u001b[A\n",
      "694it [11:25,  1.01it/s]\u001b[A\n",
      "695it [11:26,  1.01it/s]\u001b[A\n",
      "696it [11:27,  1.01it/s]\u001b[A\n",
      "697it [11:28,  1.01it/s]\u001b[A\n",
      "698it [11:29,  1.01it/s]\u001b[A\n",
      "699it [11:30,  1.01it/s]\u001b[A\n",
      "700it [11:31,  1.01it/s]\u001b[A\n",
      "701it [11:32,  1.01it/s]\u001b[A\n",
      "702it [11:33,  1.00it/s]\u001b[A\n",
      "703it [11:34,  1.01it/s]\u001b[A\n",
      "704it [11:35,  1.01it/s]\u001b[A\n",
      "705it [11:36,  1.01it/s]\u001b[A\n",
      "706it [11:37,  1.01it/s]\u001b[A\n",
      "707it [11:38,  1.01it/s]\u001b[A\n",
      "708it [11:39,  1.01it/s]\u001b[A\n",
      "709it [11:40,  1.01it/s]\u001b[A\n",
      "710it [11:41,  1.01it/s]\u001b[A\n",
      "711it [11:42,  1.01it/s]\u001b[A\n",
      "712it [11:43,  1.00it/s]\u001b[A\n",
      "713it [11:44,  1.01it/s]\u001b[A\n",
      "714it [11:45,  1.01it/s]\u001b[A\n",
      "715it [11:46,  1.01it/s]\u001b[A\n",
      "716it [11:47,  1.01it/s]\u001b[A\n",
      "717it [11:48,  1.01it/s]\u001b[A\n",
      "718it [11:49,  1.01it/s]\u001b[A\n",
      "719it [11:50,  1.01it/s]\u001b[A\n",
      "720it [11:51,  1.01it/s]\u001b[A\n",
      "721it [11:52,  1.01it/s]\u001b[A\n",
      "722it [11:53,  1.00it/s]\u001b[A\n",
      "723it [11:54,  1.00s/it]\u001b[A\n",
      "724it [11:55,  1.00it/s]\u001b[A\n",
      "725it [11:56,  1.01it/s]\u001b[A\n",
      "726it [11:57,  1.01it/s]\u001b[A\n",
      "727it [11:58,  1.01it/s]\u001b[A\n",
      "728it [11:59,  1.01it/s]\u001b[A\n",
      "729it [12:00,  1.01it/s]\u001b[A\n",
      "730it [12:01,  1.01it/s]\u001b[A\n",
      "731it [12:01,  1.01it/s]\u001b[A\n",
      "732it [12:02,  1.03it/s]\u001b[A\n",
      "733it [12:03,  1.02it/s]\u001b[A\n",
      "734it [12:04,  1.07it/s]\u001b[A\n",
      "735it [12:05,  1.05it/s]\u001b[A\n",
      "736it [12:06,  1.04it/s]\u001b[A\n",
      "737it [12:07,  1.03it/s]\u001b[A\n",
      "738it [12:08,  1.03it/s]\u001b[A\n",
      "739it [12:09,  1.02it/s]\u001b[A\n",
      "740it [12:10,  1.02it/s]\u001b[A\n",
      "741it [12:11,  1.02it/s]\u001b[A\n",
      "742it [12:12,  1.02it/s]\u001b[A\n",
      "743it [12:13,  1.01it/s]\u001b[A\n",
      "744it [12:14,  1.01it/s]\u001b[A\n",
      "745it [12:15,  1.01it/s]\u001b[A\n",
      "746it [12:16,  1.01it/s]\u001b[A\n",
      "747it [12:17,  1.01it/s]\u001b[A\n",
      "748it [12:18,  1.01it/s]\u001b[A\n",
      "749it [12:19,  1.01it/s]\u001b[A\n",
      "750it [12:20,  1.01it/s]\u001b[A\n",
      "751it [12:21,  1.00s/it]\u001b[A\n",
      "752it [12:22,  1.00s/it]\u001b[A\n",
      "753it [12:23,  1.00it/s]\u001b[A\n",
      "754it [12:24,  1.01it/s]\u001b[A\n",
      "755it [12:25,  1.01it/s]\u001b[A\n",
      "756it [12:26,  1.01it/s]\u001b[A\n",
      "757it [12:27,  1.04it/s]\u001b[A\n",
      "758it [12:28,  1.03it/s]\u001b[A\n",
      "759it [12:29,  1.03it/s]\u001b[A\n",
      "760it [12:30,  1.02it/s]\u001b[A\n",
      "761it [12:31,  1.02it/s]\u001b[A\n",
      "762it [12:32,  1.01it/s]\u001b[A\n",
      "763it [12:33,  1.01it/s]\u001b[A\n",
      "764it [12:34,  1.01it/s]\u001b[A\n",
      "765it [12:35,  1.01it/s]\u001b[A\n",
      "766it [12:36,  1.01it/s]\u001b[A\n",
      "767it [12:37,  1.01it/s]\u001b[A\n",
      "768it [12:38,  1.01it/s]\u001b[A\n",
      "769it [12:39,  1.01it/s]\u001b[A\n",
      "770it [12:40,  1.01it/s]\u001b[A\n",
      "771it [12:41,  1.01it/s]\u001b[A\n",
      "772it [12:42,  1.00it/s]\u001b[A\n",
      "773it [12:43,  1.01it/s]\u001b[A\n",
      "774it [12:44,  1.01it/s]\u001b[A\n",
      "775it [12:45,  1.01it/s]\u001b[A\n",
      "776it [12:46,  1.01it/s]\u001b[A\n",
      "777it [12:47,  1.01it/s]\u001b[A\n",
      "778it [12:48,  1.01it/s]\u001b[A\n",
      "779it [12:49,  1.01it/s]\u001b[A\n",
      "780it [12:50,  1.01it/s]\u001b[A\n",
      "781it [12:51,  1.01it/s]\u001b[A\n",
      "782it [12:52,  1.00it/s]\u001b[A\n",
      "783it [12:53,  1.01it/s]\u001b[A\n",
      "784it [12:54,  1.01it/s]\u001b[A\n",
      "785it [12:55,  1.01it/s]\u001b[A\n",
      "786it [12:56,  1.01it/s]\u001b[A\n",
      "787it [12:57,  1.01it/s]\u001b[A\n",
      "788it [12:58,  1.01it/s]\u001b[A\n",
      "789it [12:59,  1.01it/s]\u001b[A\n",
      "790it [13:00,  1.01it/s]\u001b[A\n",
      "791it [13:01,  1.01it/s]\u001b[A\n",
      "792it [13:02,  1.01it/s]\u001b[A\n",
      "793it [13:03,  1.00it/s]\u001b[A\n",
      "794it [13:04,  1.01it/s]\u001b[A\n",
      "795it [13:05,  1.01it/s]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "c_python_scores = get_normal_list_scores_for_prompt_list(model,[\n",
    "    {\n",
    "        'normal': c_normal_1.detach().cpu().numpy(),\n",
    "        'layer': 3, 'sublayer': 'ln_1_in', 'token': None, 'name': 'c_normal_1 mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= mlp7@-1 <= attn3.9@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': c_normal_2.detach().cpu().numpy(),\n",
    "        'layer': 7, 'sublayer': 'ln_1_in', 'token': None, 'name': 'c_normal_2 mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn7.6@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': c_normal_3.detach().cpu().numpy(),\n",
    "        'layer': 6, 'sublayer': 'ln_1_in', 'token': None, 'name': 'mlp16@-1 <= mlp11@-1 <= mlp9@-1 <= attn6.13@8'\n",
    "    },\n",
    "    {\n",
    "        'normal': c_normal_4.detach().cpu().numpy(),\n",
    "        'layer': 2, 'sublayer': 'ln_1_in', 'token': None, 'name': 'c_normal_4 attn17.4@3 <= mlp13@3 <= mlp6@3 <= mlp4@3 <= attn2.12@0 '\n",
    "    },\n",
    "], python_tokenized_prompts, pre_tokenized=True, save_every=10, save_out=\"c_python_scores_python_only.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c6823cf5-ecf2-4136-add9-ca01d34ee797",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(c_python_scores, open(\"c_python_scores_python_only.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5686be7-abbb-4b85-8e43-48d478059c50",
   "metadata": {},
   "source": [
    "## Logistic regression feature vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2e9cbcca-f858-4a13-8645-31fd964b13b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_dataset = load_dataset(\"andstor/the_pile_github\", 'c', streaming=True)\n",
    "python_dataset = load_dataset(\"andstor/the_pile_github\", 'python', streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "74a6596d-fffc-4941-ba7d-0d3be2e403ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 111/220 [00:16<00:14,  7.60it/s]"
     ]
    }
   ],
   "source": [
    "python_rows = []\n",
    "c_rows = []\n",
    "\n",
    "rows_num = 110\n",
    "pbar = tqdm.tqdm(total=2*rows_num)\n",
    "for row in python_dataset['train']:\n",
    "    if len(python_rows) < rows_num:\n",
    "        python_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(python_rows) >= rows_num: break\n",
    "for row in c_dataset['train']:\n",
    "    if len(c_rows) < rows_num:\n",
    "        c_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(c_rows) >= rows_num: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f9040bc9-5b6e-4a45-b012-50d027da3b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_tokenized_prompts = []\n",
    "for prompt in c_rows:\n",
    "    c_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "c_tokenized_prompts = slice_tokenized_prompts_into_context_windows(c_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "86e29cd3-20ff-4f60-a631-9aa0b981d460",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 220/220 [00:30<00:00,  7.60it/s]"
     ]
    }
   ],
   "source": [
    "python_tokenized_prompts = []\n",
    "for prompt in python_rows:\n",
    "    python_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "python_tokenized_prompts = slice_tokenized_prompts_into_context_windows(python_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fa11e15f-718d-4511-9737-41393e8cbf60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "  0%|          | 0/791 [03:31<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
      "  0%|          | 0/300 [04:29<?, ?it/s]\n",
      "\n",
      "  0%|          | 0/300 [04:29<?, ?it/s]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "c_embeddings = embeddings_from_prompt_list2(model, c_tokenized_prompts[:300], 16, 'ln_1_in', pre_tokenized=True, mean_embedding=True)\n",
    "python_embeddings = embeddings_from_prompt_list2(model, python_tokenized_prompts[:300], 16, 'ln_1_in', pre_tokenized=True, mean_embedding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7c962e8d-67b3-46a4-bbd6-73b206263369",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model\n",
    "logistic_model = sklearn.linear_model.LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "66d67534-2ec1-4d7e-a2f4-6579930cc035",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 25\n",
    "xs = np.concatenate([c_embeddings[:train_len], python_embeddings[:train_len]])\n",
    "ys = np.concatenate([np.zeros(train_len)+1, np.zeros(train_len)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "563747c5-2d9e-47dc-a7a8-48bf492cf650",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model.fit(xs, ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1573dfb6-0976-49a3-b1cd-63149e3e386b",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_scores = np.stack(c_embeddings[train_len:]) @ logistic_model.coef_[0]\n",
    "python_scores = np.stack(python_embeddings[train_len:]) @ logistic_model.coef_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a5e78fdb-040e-4dad-ae2d-63cb56cd0272",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6a7a7aaa-5ccf-45f6-9b2f-bec55a55c0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9970512396694214"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.roc_auc_score(\n",
    "    np.concatenate([np.zeros(len(c_scores)), np.zeros(len(python_scores))+1]),\n",
    "    np.concatenate([c_scores, python_scores])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "6b09c2b8-8ca6-4f8c-982e-95251b6a97bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import sklearn.metrics\n",
    "\n",
    "auc_score = sklearn.metrics.roc_auc_score(\n",
    "    np.concatenate([np.zeros(len(c_scores))+1, np.zeros(len(python_scores))]),\n",
    "    np.concatenate([c_scores, python_scores])\n",
    ")\n",
    "\n",
    "bins=17\n",
    "plt.hist(c_scores, bins=bins, alpha=0.5, label=\"C\")\n",
    "plt.hist(python_scores, bins=bins, alpha=0.5, label=\"Python\")\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "empty_patch = mpatches.Patch(color='none', label=f'AUC score: {auc_score:.4f}')\n",
    "handles.append(empty_patch)\n",
    "labels.append(f'AUC score: {auc_score:.4f}')\n",
    "\n",
    "plt.legend(handles, labels, loc='upper right')\n",
    "plt.xlabel(\"Logistic regression feature vector score\")\n",
    "plt.ylabel(\"Density (number of examples)\")\n",
    "plt.title(\"Logistic regression feature vector scores for C and Python code\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f18f66e8-cf9c-418b-a3ee-849b9700d2da",
   "metadata": {},
   "source": [
    "## ObsProp feature vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "f00e50fa-f3ed-407d-989f-fda268271c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "c_scores_obsprop = np.stack(c_embeddings[train_len:]) @ obs1_normal5.detach().cpu().numpy()\n",
    "python_scores_obsprop = np.stack(python_embeddings[train_len:]) @ obs1_normal5.detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2852271e-f475-46bb-aeb2-c31910549ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973602084987864"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.roc_auc_score(\n",
    "    np.concatenate([np.zeros(len(c_scores_obsprop))+1, np.zeros(len(python_scores_obsprop))]),\n",
    "    np.concatenate([c_scores_obsprop, python_scores_obsprop])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "ffb9c81d-99b3-404e-83aa-b47e3e6b280a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 220/220 [1:10:19<00:00, 19.18s/it]"
     ]
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import sklearn.metrics\n",
    "\n",
    "auc_score = sklearn.metrics.roc_auc_score(\n",
    "    np.concatenate([np.zeros(len(c_scores_obsprop))+1, np.zeros(len(python_scores_obsprop))]),\n",
    "    np.concatenate([c_scores_obsprop, python_scores_obsprop])\n",
    ")\n",
    "\n",
    "bins=17\n",
    "plt.hist(c_scores_obsprop, bins=bins, alpha=0.5, label=\"C\")\n",
    "plt.hist(python_scores_obsprop, bins=bins, alpha=0.5, label=\"Python\")\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "empty_patch = mpatches.Patch(color='none', label=f'AUC score: {auc_score:.4f}')\n",
    "handles.append(empty_patch)\n",
    "labels.append(f'AUC score: {auc_score:.4f}')\n",
    "\n",
    "plt.legend(handles, labels, loc='upper left')\n",
    "plt.xlabel(\"attn16.9 feature vector score\")\n",
    "plt.ylabel(\"Density (number of examples)\")\n",
    "plt.title(\"Observable propagation feature vector scores for C and Python code\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235ccd0d-d25a-4f24-964c-962f096c4f3c",
   "metadata": {},
   "source": [
    "## Mean difference feature vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "481faa94-0cf9-41bf-86a7-433b14d974c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "c_dataset = load_dataset(\"andstor/the_pile_github\", 'c', streaming=True)\n",
    "python_dataset = load_dataset(\"andstor/the_pile_github\", 'python', streaming=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "28d967dd-94a6-4995-8cc8-6dd93a7b3e53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 111/220 [00:14<00:12,  8.69it/s]"
     ]
    }
   ],
   "source": [
    "python_rows = []\n",
    "c_rows = []\n",
    "\n",
    "rows_num = 110\n",
    "pbar = tqdm.tqdm(total=2*rows_num)\n",
    "for row in python_dataset['train']:\n",
    "    if len(python_rows) < rows_num:\n",
    "        python_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(python_rows) >= rows_num: break\n",
    "for row in c_dataset['train']:\n",
    "    if len(c_rows) < rows_num:\n",
    "        c_rows.append(row)\n",
    "        pbar.update(1)\n",
    "    if len(c_rows) >= rows_num: break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d75913b8-4143-43ff-80d0-1c758001e5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (2706 > 2048). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "c_tokenized_prompts = []\n",
    "for prompt in c_rows:\n",
    "    c_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "c_tokenized_prompts = slice_tokenized_prompts_into_context_windows(c_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "32bae6e5-fd5a-47f3-bce0-c8550039b3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "python_tokenized_prompts = []\n",
    "for prompt in python_rows:\n",
    "    python_tokenized_prompts.append(tokenizer(prompt['text'], return_tensors=\"pt\"))\n",
    "python_tokenized_prompts = slice_tokenized_prompts_into_context_windows(python_tokenized_prompts, 128, max_tokens=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a5ec9fce-8df4-480a-98c7-9d85bc94c547",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/300 [03:45<?, ?it/s]\u001b[A\n",
      "\n",
      "  0%|          | 0/300 [03:45<?, ?it/s]\u001b[A\n"
     ]
    }
   ],
   "source": [
    "c_embeddings = embeddings_from_prompt_list2(model, c_tokenized_prompts[:300], 16, 'ln_1_in', pre_tokenized=True, mean_embedding=True)\n",
    "python_embeddings = embeddings_from_prompt_list2(model, python_tokenized_prompts[:300], 16, 'ln_1_in', pre_tokenized=True, mean_embedding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "273cb60a-1cff-4555-84a9-249313637df7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_len = 50\n",
    "mean_c = np.mean(c_embeddings[:train_len], axis=0)\n",
    "mean_python = np.mean(python_embeddings[:train_len], axis=0)\n",
    "mean_diff = mean_c - mean_python\n",
    "\n",
    "meanvec_c_scores = c_embeddings[train_len:] @ mean_diff\n",
    "meanvec_python_scores = python_embeddings[train_len:] @ mean_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "ef3f808e-0225-478a-8457-98b3a22fa091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9052"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.metrics\n",
    "sklearn.metrics.roc_auc_score(\n",
    "    np.concatenate([np.zeros(len(meanvec_c_scores))+1, np.zeros(len(meanvec_python_scores))]),\n",
    "    np.concatenate([meanvec_c_scores, meanvec_python_scores])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "df2ed3ce-7158-4cca-b5d7-ca1fa09fb403",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=17\n",
    "plt.hist(meanvec_c_scores, bins=bins, alpha=0.5, label=\"C\")\n",
    "plt.hist(meanvec_python_scores, bins=bins, alpha=0.5, label=\"Python\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}