Combineinator_Library.ipynb

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            "_model_name": "LayoutModel",
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            "visibility": null,
            "align_self": null,
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            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "3bdecdac7fd14957b4d257a8698357f9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "5c05494fe6e84943aab5cd410e01bef2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/combineinator/combine-inator-acikhack2021/blob/main/Combineinator_Library.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NK50268dZY2L",
        "outputId": "faef0a2b-63b2-45a1-d776-d9a2d77b7b7c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbwSbZJDx-Sz"
      },
      "source": [
        "## CombineInator (parent class)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtC7z7KtcF-_"
      },
      "source": [
        "class CombineInator:\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.source = \"\"\n",
        "\n",
        "\n",
        "  def translate_model(self, source):\n",
        "\n",
        "    if source == \"en\":\n",
        "\n",
        "      tokenizer_trs = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-en-trk\")\n",
        "      model_trs = AutoModelForSeq2SeqLM.from_pretrained(\"Helsinki-NLP/opus-mt-en-trk\")\n",
        "      pipe_trs = \"translation_en_to_trk\"\n",
        "\n",
        "    elif source == \"tr\":\n",
        "\n",
        "      tokenizer_trs = AutoTokenizer.from_pretrained(\"Helsinki-NLP/opus-mt-tr-en\")\n",
        "      model_trs = AutoModelForSeq2SeqLM.from_pretrained(\"Helsinki-NLP/opus-mt-tr-en\")\n",
        "      pipe_trs = \"translation_tr_to_en\"\n",
        "      \n",
        "\n",
        "    return model_trs, tokenizer_trs, pipe_trs \n",
        "\n",
        "  \n",
        "  def translate(self, pipe, model, tokenizer, response):\n",
        "  \n",
        "    translator = pipeline(pipe, model=model, tokenizer=tokenizer)\n",
        "    \n",
        "    # elde edilen cümleleri hedeflnen dile çevirme:\n",
        "    trans = translator(response)[0][\"translation_text\"]\n",
        "\n",
        "    return trans\n",
        "  \n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBCX4vUdyADu"
      },
      "source": [
        "## WikiWebScraper (child)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnAfz8HmkRRJ"
      },
      "source": [
        "import requests\n",
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "from tqdm import tqdm\n",
        "from os.path import exists, basename, splitext"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-8Em1-zdjs1"
      },
      "source": [
        "class WikiWebScraper(CombineInator):\n",
        " \n",
        "  def __init__(self):\n",
        "    \n",
        "    self.__HEADERS_PARAM = {\n",
        "        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.104 Safari/537.36\"}\n",
        " \n",
        "  \n",
        "  def category_scraping_interface(self, CATEGORY_QUERY, LIMIT, SAVE_PATH, PAGE_PER_SAVE, REMOVE_NUMBERS, JUST_TITLE_ANALYSIS, TEXT_INTO_SENTENCES_PARAM):\n",
        " \n",
        "    \"\"\"\n",
        "    \n",
        "    Kategorik verilerin ayıklanma işlemleri bu fonksiyonda yönetilir.\n",
        "    \n",
        "    :param CATEGORY_QUERY: Ayıklanacak kategori sorgusu.\n",
        "    :type CATEGORY_QUERY: str\n",
        "\n",
        "    :param SAVE_PATH: Ayıklanan verinin kaydedileceği yol.\n",
        "    :type SAVE_PATH: str\n",
        "\n",
        "    :param LIMIT: Ayıklanması istenen veri limiti. Verilmediği taktirde tüm verileri çeker.\n",
        "    :type LIMIT: int\n",
        "\n",
        "    :param PAGE_PER_SAVE: Belirlenen aralıkla ayıklanan kategorik verinin kaydedilmesini sağlar.\n",
        "    :type PAGE_PER_SAVE: int\n",
        "\n",
        "    :param TEXT_INTO_SENTENCES_PARAM: Ayıklanan verilerin cümleler halinde mi, yoksa metin halinde mi kaydedileceğini belirler.\n",
        "    :type TEXT_INTO_SENTENCES_PARAM: bool\n",
        "\n",
        "    :param REMOVE_NUMBERS: Ayıklanan verilerden rakamların silinip silinmemesini belirler.\n",
        "    :type REMOVE_NUMBERS: bool\n",
        "\n",
        "    :param JUST_TITLE_ANALYSIS: Sadece sayfaların başlık bilgilerinin toplanmasını sağlar.\n",
        "    :type JUST_TITLE_ANALYSIS: bool\n",
        "    \n",
        "    \"\"\"\n",
        " \n",
        "    sub_list = []\n",
        "    page_list = []\n",
        "    text_list = []\n",
        " \n",
        "    page_list, sub_list = self.first_variable(CATEGORY_QUERY, (LIMIT - len(text_list)))\n",
        "    fv = True\n",
        "        \n",
        "    if page_list and sub_list is not None:        \n",
        "      with tqdm(total=LIMIT, desc=\"Sayfa taranıyor.\") as pbar:\n",
        "        while len(page_list) < LIMIT:\n",
        " \n",
        "          if fv is True:\n",
        "            pbar.update(len(page_list))\n",
        "            fv = False \n",
        "          \n",
        "          temp_soup = \"\"\n",
        " \n",
        "          if len(sub_list) == 0:\n",
        "            break\n",
        "        \n",
        "          temp_soup = self.sub_scraper(sub_list[0])    \n",
        " \n",
        "          if (temp_soup == False):\n",
        "            break   \n",
        " \n",
        "          del sub_list[0]   \n",
        " \n",
        "          sub_list = sub_list + self.sub_category_scraper(temp_soup)\n",
        " \n",
        "          temp_page_scraper = self.page_scraper(temp_soup, (LIMIT - len(page_list)))  \n",
        " \n",
        "          if temp_page_scraper is not None:\n",
        "            for i in temp_page_scraper:\n",
        "              if i not in page_list:\n",
        "                page_list.append(i)\n",
        "                pbar.update(1)            \n",
        " \n",
        "          if len(sub_list) == 0:\n",
        "            sub_list = sub_list + self.sub_category_scraper(temp_soup)\n",
        " \n",
        "      temp_range = 0\n",
        "      loop_counter = 0\n",
        " \n",
        "      if JUST_TITLE_ANALYSIS is False: \n",
        "        for i in range(PAGE_PER_SAVE, len(page_list)+PAGE_PER_SAVE, PAGE_PER_SAVE):\n",
        " \n",
        "          if loop_counter == (len(page_list) // PAGE_PER_SAVE):\n",
        "            PATH = SAVE_PATH + \"/\" + CATEGORY_QUERY + \"_\" + str(temp_range) + \" - \" + str(len(page_list)) + \".txt\"\n",
        "            temp_text_list = self.text_into_sentences(self.text_scraper(page_list[temp_range:i], (len(page_list) % PAGE_PER_SAVE)), REMOVE_NUMBERS,TEXT_INTO_SENTENCES_PARAM)\n",
        "          else:\n",
        "            PATH = SAVE_PATH + \"/\" + CATEGORY_QUERY + \"_\" + str(temp_range) + \" - \" + str(i) + \".txt\"\n",
        "            temp_text_list = self.text_into_sentences(self.text_scraper(page_list[temp_range:i], PAGE_PER_SAVE), REMOVE_NUMBERS, TEXT_INTO_SENTENCES_PARAM)\n",
        "                      \n",
        "          text_list += temp_text_list\n",
        " \n",
        "          self.save_to_csv(PATH, temp_text_list)         \n",
        "          temp_range = i\n",
        "          loop_counter += 1\n",
        " \n",
        "        print(\"\\n\\n\"+str(len(page_list)) + \" adet sayfa bulundu ve içerisinden \" + str(len(text_list)) + \" satır farklı metin ayrıştırıldı.\")\n",
        "        return text_list\n",
        " \n",
        "      else:\n",
        "        PATH = SAVE_PATH + \"/\" + CATEGORY_QUERY + \"_\" + str(len(page_list)) + \"_page_links\" + \".txt\"\n",
        "        self.save_to_csv(PATH, page_list, JUST_TITLE_ANALYSIS)\n",
        "        print(\"\\n\\n\"+str(len(page_list)) + \" adet sayfa bulundu ve sayfaların adresleri \\\"\" + PATH + \"\\\" konumunda kaydedildi.\")\n",
        "        return page_list\n",
        " \n",
        "    else:\n",
        "        print(\"Aranan kategori bulunamadı.\")\n",
        "  \n",
        " \n",
        "  def categorical_scraper(self, CATEGORY_QUERY, save_path, LIMIT=-1, page_per_save=10000, text_into_sentences_param=True, remove_numbers=False, just_title_analysis=False):\n",
        "    \n",
        "    \"\"\"\n",
        "    Wikipedia üzerinden kategorik olarak veri çekmek için kullanılır.\n",
        " \n",
        "    :param CATEGORY_QUERY: Ayıklanacak kategori sorgusu.\n",
        "    :type CATEGORY_QUERY: str\n",
        "\n",
        "    :param save_path: Ayıklanan verinin kaydedileceği yol.\n",
        "    :type save_path: str\n",
        "\n",
        "    :param LIMIT: Ayıklanması istenen veri limiti. Verilmediği taktirde tüm verileri çeker.\n",
        "    :type LIMIT: int\n",
        "\n",
        "    :param page_per_save: Belirlenen aralıkla ayıklanan kategorik verinin kaydedilmesini sağlar.\n",
        "    :type page_per_save: int\n",
        "\n",
        "    :param text_into_sentences_param: Ayıklanan verilerin cümleler halinde mi, yoksa metin halinde mi kaydedileceğini belirler.\n",
        "    :type text_into_sentences_param: bool\n",
        "\n",
        "    :param remove_numbers: Ayıklanan verilerden rakamların silinip silinmemesini belirler.\n",
        "    :type remove_numbers: bool\n",
        "\n",
        "    :param just_title_analysis: Sadece sayfaların başlık bilgilerinin toplanmasını sağlar.\n",
        "    :type just_title_analysis: bool\n",
        " \n",
        "    \"\"\"\n",
        "\n",
        "    if LIMIT == -1:\n",
        "      LIMIT = 9999999\n",
        " \n",
        "    CATEGORY_QUERY = CATEGORY_QUERY.replace(\" \",\"_\")\n",
        "    \n",
        "    return_list = self.category_scraping_interface(CATEGORY_QUERY, LIMIT, save_path, page_per_save, remove_numbers, just_title_analysis, text_into_sentences_param)\n",
        " \n",
        "    if return_list is None:\n",
        "        return []\n",
        "    else:\n",
        "        return return_list\n",
        " \n",
        "  \n",
        "  \n",
        "  def text_scraper_from_pagelist(self, page_list_path, save_path, page_per_save=10000, remove_numbers=False, text_into_sentences_param=True, RANGE=None):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Wikipedia üzerinden kategorik olarak veri çekmek için kullanılır.\n",
        " \n",
        "    :param page_list_path: Toplanan sayfaların başlık bilgilerinin çıkartılmasını sağlar\n",
        "    :type page_list_path: str\n",
        " \n",
        "    :param save_path: Ayıklanan verinin kaydedileceği yol.\n",
        "    :type save_path: str\n",
        " \n",
        "    :param page_per_save: Belirlenen aralıkla ayıklanan kategorik verinin kaydedilmesini sağlar.\n",
        "    :type page_per_save: int\n",
        " \n",
        "    :param text_into_sentences_param: Ayıklanan verilerin cümleler halinde mi, yoksa metin halinde mi kaydedileceğini belirler.\n",
        "    :type text_into_sentences_param: bool\n",
        " \n",
        "    :param remove_numbers: Ayıklanan verilerden rakamların silinip silinmemesini belirler.\n",
        "    :type remove_numbers: bool\n",
        " \n",
        "    :param RANGE: Ayıklnacak verilerin aralığını belirler. \"RANGE = [500,1000]\" şeklinde kullanılır. Verilmediği zaman tüm veri ayıklanır.\n",
        "    :type RANGE: list\n",
        " \n",
        "    \"\"\"\n",
        "  \n",
        "    page_list = []\n",
        "    text_list = []\n",
        " \n",
        "    with open(page_list_path, 'r') as f:\n",
        "      page_list = [line.strip() for line in f]\n",
        " \n",
        "    if RANGE is not None:\n",
        "      page_list = page_list[RANGE[0]:RANGE[1]]\n",
        " \n",
        "    temp_range = 0\n",
        "    loop_counter = 0\n",
        " \n",
        "    for i in range(page_per_save, len(page_list)+page_per_save, page_per_save):\n",
        "      \n",
        "      if loop_counter == (len(page_list) // page_per_save):\n",
        "        PATH = save_path + \"/\" + \"scraped_page\" + \"_\" + str(temp_range) + \" - \" + str(len(page_list)) + \".txt\"\n",
        "        temp_text_list = self.text_into_sentences(self.text_scraper(page_list[temp_range:i], (len(page_list) % page_per_save), True), remove_numbers, text_into_sentences_param)\n",
        "      else:\n",
        "        PATH = save_path + \"/\" + \"scraped_page\" + \"_\" + str(temp_range) + \" - \" + str(i) + \".txt\"\n",
        "        temp_text_list = self.text_into_sentences(self.text_scraper(page_list[temp_range:i], page_per_save, True), remove_numbers, text_into_sentences_param)\n",
        " \n",
        "      text_list += temp_text_list\n",
        " \n",
        "      save_to_csv(PATH, temp_text_list)\n",
        "      temp_range = i\n",
        "      loop_counter += 1\n",
        " \n",
        "    print(\"\\n\\\"\" + page_list_path + \"\\\" konumundaki \" + str(len(page_list)) + \" adet sayfa içerisinden \" + str(len(text_list)) + \" satır metin ayrıştırıldı.\")\n",
        "    \n",
        "    return text_list\n",
        " \n",
        "  \n",
        "  \n",
        "  def page_scraper(self, page_soup, LIMIT):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Gönderilen wikipedia SOUP objesinin içerisindeki kategorik içerik sayfaları döndürür.\n",
        "\n",
        "    :param page_soup: Wikipedia kategori sayfasının SOUP objesidir.\n",
        "    \n",
        "    :param LIMIT: Ayıklanacaj sayfa limitini belirler.\n",
        "    :type LIMIT: int\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    page_list = []\n",
        " \n",
        "    try:\n",
        "        pages = page_soup.find(\"div\", attrs={\"id\": \"mw-pages\"}).find_all(\"a\")\n",
        "        for page in pages[1:]:\n",
        "            if len(page_list) == LIMIT:\n",
        "                break\n",
        "            else:\n",
        "                page_list.append([page.text, page[\"href\"]])\n",
        "        return page_list\n",
        "   \n",
        "    except:\n",
        "      pass\n",
        "  \n",
        "  \n",
        "  def sub_category_scraper(self, sub_soup):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Gönderilen wikipedia SOUP objesinin içerisindeki alt kategorileri döndürür.\n",
        "\n",
        "    :param sub_soup: Alt kategori sayfasının SOUP objesidir.\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    sub_list = []\n",
        " \n",
        "    try:\n",
        "        sub_categories = sub_soup.find_all(\"div\", attrs={\"class\": \"CategoryTreeItem\"})\n",
        "        for sub in sub_categories[1:]:\n",
        "            sub_list.append([sub.a.text, sub.a[\"href\"]])\n",
        " \n",
        "        return sub_list\n",
        " \n",
        "    except:\n",
        "         print(\"Aranan kategori için yeterli sayfa bulunamadı.\")\n",
        " \n",
        " \n",
        "  \n",
        "  def sub_scraper(self, sub):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Fonksiyona gelen wikipedia kategori/alt kategorisinin SOUP objesini döndürür.\n",
        "\n",
        "    :param sub: Alt kategori sayfasının linkini içerir.\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    try:\n",
        "        req = requests.get(\"https://tr.wikipedia.org\" + str(sub[1]), headers=self.__HEADERS_PARAM)\n",
        "        soup = BeautifulSoup(req.content, \"lxml\")\n",
        "        return soup\n",
        " \n",
        "    except:\n",
        "        print(\"\\nAlt kategori kalmadı\")\n",
        "        return False\n",
        " \n",
        " \n",
        "  def text_scraper(self, page_list, LIMIT, IS_FROM_TXT=False):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Önceden ayıklanmış sayfa listesini içerisindeki sayfaları ayıklayarak içerisindeki metin listesini döndürür.\n",
        "\n",
        "    :param page_list: Sayfa listesini içerir.\n",
        "\n",
        "    :parama LIMIT: Ayıklanacaj sayfa limitini belirler.\n",
        "    :type LIMIT: int\n",
        "\n",
        "    :param IS_FROM_TXT: Ayıklanacak sayfanın listeleden mi olup olmadığını kontrol eder.\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    text_list = []\n",
        "    \n",
        "    with tqdm(total=LIMIT, desc=\"Sayfa Ayrıştırılıyor\") as pbar:\n",
        "      for page in page_list:\n",
        " \n",
        "          if len(text_list) == LIMIT:\n",
        "              break\n",
        "          if IS_FROM_TXT is False:\n",
        "            req = requests.get(\"https://tr.wikipedia.org\" + str(page[1]), headers=self.__HEADERS_PARAM)\n",
        "          else:\n",
        "            req = requests.get(\"https://tr.wikipedia.org\" + str(page), headers=self.__HEADERS_PARAM)\n",
        "          soup = BeautifulSoup(req.content, \"lxml\")\n",
        "          page_text = soup.find_all(\"p\")\n",
        "          temp_text = \"\"\n",
        " \n",
        "          for i in page_text[1:]:\n",
        "              temp_text = temp_text + i.text\n",
        " \n",
        "          text_list.append(temp_text)\n",
        "          pbar.update(1)\n",
        " \n",
        "    return text_list\n",
        " \n",
        " \n",
        " \n",
        "  def first_variable(self, CATEGORY_QUERY, LIMIT):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Sorguda verilen kategorinin doğruluğunu kontrol eder ve eğer sorgu doğru ise ilk değerleri ayıklar.\n",
        "\n",
        "    :param CATEGORY_QUERY: Ayıklanacak kategori sorgusu.\n",
        "    :type CATEGORY_QUERY: str\n",
        "\n",
        "    :param LIMIT: Ayıklanması istenen veri limiti. Verilmediği taktirde tüm verileri çeker.\n",
        "    :type LIMIT: int\n",
        " \n",
        "    \"\"\"\n",
        "    \n",
        "    first_req = requests.get(\"https://tr.wikipedia.org/wiki/Kategori:\" + CATEGORY_QUERY, headers=self.__HEADERS_PARAM)\n",
        "    first_soup = BeautifulSoup(first_req.content, \"lxml\")\n",
        "    page_list = self.page_scraper(first_soup, LIMIT)\n",
        "    sub_list = self.sub_category_scraper(first_soup)\n",
        " \n",
        "    return page_list, sub_list\n",
        " \n",
        "  \n",
        "  \n",
        "  def text_into_sentences(self, texts, remove_numbers, text_into_sentences_param):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Metin verilerini cümlelerine ayıklar.\n",
        "\n",
        "    :param texts: Düzlenecek metin verisi.\n",
        "\n",
        "    :param remove_numbers: Sayıların temizlenip temizlenmeyeceğini kontrol eder.\n",
        "\n",
        "    :param text_into_sentences_param: Metinlerin cümlelere çevrilip çevrilmeyeceğini kontrol eder.\n",
        " \n",
        "    \"\"\"\n",
        "  \n",
        "    flatlist = []   \n",
        "    sent_list = []\n",
        " \n",
        "    texts = self.sentence_cleaning(texts, remove_numbers)\n",
        "    \n",
        "    if text_into_sentences_param is True:\n",
        "      for line in texts:\n",
        "        temp_line = re.split(r'(?<![IVX0-9]\\S)(?<!\\w\\.\\w.)(?<![A-Z][a-z]\\.)(?<=\\.|\\?)\\s', line)\n",
        "        for i in temp_line:\n",
        "          if len(i.split(\" \")) > 3:\n",
        "            sent_list.append(i)\n",
        "    else:\n",
        "      sent_list = texts\n",
        " \n",
        "    flatlist = list(dict.fromkeys(self.flat(sent_list, flatlist)))\n",
        " \n",
        "    return flatlist\n",
        " \n",
        " \n",
        " \n",
        "  def flat(self, sl,fl): \n",
        "    \n",
        "    \"\"\"\n",
        " \n",
        "    Metinler, cümlelerine ayırıldıktan sonra listenin düzlenmesine yarar.\n",
        "\n",
        "    :param sl: Yollanan listle.\n",
        "\n",
        "    :param fl: Düzlemem liste.\n",
        "\n",
        "    \n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    for e in sl:                \n",
        "        if type(e) == list:     \n",
        "            flat(e,fl)             \n",
        "        elif len(e.split(\" \"))>3:\n",
        "          fl.append(e)  \n",
        "    return fl\n",
        " \n",
        " \n",
        " \n",
        "  def sentence_cleaning(self, sentences, remove_numbers):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Ayıklanan wikipedia verilerinin temizlenmesi bu fonksiyonda gerçekleşir.\n",
        "\n",
        "    :param sentences: Temizlenmek için gelen veri seti.\n",
        "    \n",
        "    :param remove_numbers: Sayıların temizlenip temizlenmeyeceğini kontrol eder.\n",
        " \n",
        "    \"\"\"\n",
        "    \n",
        "    return_list = []\n",
        " \n",
        "    if remove_numbers is False:\n",
        "      removing_func = '[^[a-zA-ZğüışöçĞÜIİŞÖÇ0-9.,!:;`?%&\\-\\'\" ]'\n",
        "    else:\n",
        "      removing_func = '[^[a-zA-ZğüışöçĞÜIİŞÖÇ.,!:;`?%&\\-\\'\" ]'\n",
        " \n",
        "    for input_text in sentences:\n",
        "        try:            \n",
        "            input_text = re.sub(r'(\\[.*?\\])', '', input_text)\n",
        "            input_text = re.sub(r'(\\(.*?\\))', '', input_text)\n",
        "            input_text = re.sub(r'(\\{.*?\\})', '', input_text)\n",
        "            input_text = re.sub(removing_func, '', input_text)\n",
        "            input_text = re.sub(\"(=+(\\s|.)*)\", \"\", input_text)\n",
        "            input_text = re.sub(\"(\\s{2,})\", \"\", input_text)\n",
        "            input_text = input_text.replace(\"''\", \"\")\n",
        "            input_text = input_text.replace(\"\\n\", \"\")            \n",
        "            return_list.append(input_text)\n",
        "        except:\n",
        "            pass\n",
        "    \n",
        "    return return_list\n",
        " \n",
        " \n",
        "  def save_to_csv(self, PATH, data, is_just_title_analysis=False):\n",
        " \n",
        "    \"\"\"\n",
        " \n",
        "    Verilerin 'csv' formatında kaydedilmesini bu fonksiyonda gerçekleşir.\n",
        "\n",
        "    :param PATH: Kaydedilecek yol.\n",
        "\n",
        "    :param data: Kaydedilecek veri.\n",
        "\n",
        "    :param is_just_title_analysis: Sadece analiz yapılıp yapılmadığını kontrol eder.\n",
        " \n",
        "    \"\"\"\n",
        "    \n",
        "    if is_just_title_analysis is False:\n",
        "      with open(PATH, \"w\") as output:\n",
        "        for i in data:\n",
        "          output.write(i+\"\\n\")\n",
        " \n",
        "    else:\n",
        "      temp_data = []\n",
        "      for i in data:\n",
        "        temp_data.append(i[1])\n",
        "      with open(PATH, \"w\") as output:\n",
        "        for i in temp_data:\n",
        "          output.write(i+\"\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qISeoqQvy4RT"
      },
      "source": [
        "### Örnek kullanım"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gD0TPP_iicbu"
      },
      "source": [
        "library = WikiWebScraper()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyoJ5M9Tivai"
      },
      "source": [
        "PATH = \"/content/\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcNJPr3lie5T",
        "outputId": "f260226b-8f0f-4b14-eb71-bbf04e905bd7"
      },
      "source": [
        "library.categorical_scraper(\"savaş\", PATH, 20, text_into_sentences_param=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sayfa taranıyor.: 100%|██████████| 20/20 [00:00<00:00, 52.99it/s]\n",
            "Sayfa Ayrıştırılıyor: 100%|██████████| 20/20 [00:04<00:00,  4.68it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "20 adet sayfa bulundu ve içerisinden 20 satır farklı metin ayrıştırıldı.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['HavaCepheKaraDeniz Uzay ZırhlıBiyolojikSüvarilerKimyasalPiyadeNükleer GerillaGayrinizamiKuşatmaTopyekn savaşCepheYakıp yıkmaAsker operasyon planıEkonomikAmbargoOperasyonelPsikolojikAsker eğitimOrduTümenTaburGrupAtHelikopterSavaş filiTahkimatTankDonanımUçakSavaş veya harp; ülkeler, hükmetler, bloklar ya da bir ülke içerisindeki toplumlar, isyancılar veya milisler gibi büyük gruplar arasında gerçekleşen silahlı mücadeledir.Savaşlar genellikle dini, mill, siyasi ve ekonomik amaçlara ulaşmak için gerçekleştirilir. Kullanılan silahlara, amaçlara, taraflara ve gerçekleştiği yerlere göre farklı şekillerde adlandırılır: nükleer savaş, soğuk savaş, iç savaş, dini savaş , dünya savaşı, gerilla savaşı gibi.Günümüzde savaşlar Birleşmiş Milletler tarafından bazı temellere ve kurallara dayandırılmıştır. Geçmişte yapılan savaşların aksine günümüzdeki savaşlarda özellikle sivillerin öldürülmesini engellemek, ülke ya da kitleleri yok etmektense onları güçsüz bırakmak güdülmektedir. Ancak buna rağmen günümüzde de trajediler yaşanmaya devam etmektedir. Örneğin, 1990\\'lı yıllarda Kuzey Afrika\\'da çıkan iç savaşlarda ve kabile savaşlarında 1 milyonun üzerinde insan ölmüştür. 2003 yılında başlayan Irak Savaşı\\'nda çok sayıda asker ve sivil ölmüştür.Uluslararası hukukta silahlı direniş hareketleri başlatmış olduğu savaşın kabul edilebilirliği ve yasallığı üzerine, en az 1899\\'dan günümüze kadar, savaş hukukunun bir dizi uluslararası antlaşma düzeyde belirlenmesi yoluyla ilk büyük girişim gerçekleştiğinden bu yana savaşın direniş hareketi olarak kapsanması konusunda anlaşmazlık yaşanmaktadır. Yakın tarihte, 12 Ağustos 1949 tarihli Cenevre Sözleşmeleri\\'ne 1977 yılında eklenen I. Protokol olarak anılan \"Uluslararası Silahlı Çatışmaların Kurbanlarının Korunması ile İlgili Protokol\"; 1. ve 4. maddede silahlı çatışmalara atıfta bulunarak \"... insanların sömürgecilik hakimiyeti, yabancı işgal kuvveti ve ırkçı rejimlere karşı\" savaşın yapılabileceğini öngörür. Ancak tüm bu ayrımlar politik birer yargıdır, iktidarların uygulamalarından öte uluslararası hukuk da bu konuda net bir ayrım yapmamakta ve en ciddi insan hakları ihlallerine karşı bile örgütlü bir direniş hakkı tanımlamamaktadır. Zira bu durum soykırım ve devlet terörü gibi suçlara karşı savaş hakkını engellemekte, insanlığa karşı suçlar ve büyük çapta savaş suçlarına karşı mücadeleyi zayıflatmaktadır.Savaşlar farklı açılardan ayrı biçimlerde türlerine ayrılmıştır.Savaş ilanı, bir devlet ya da milletin diğerine karşı savaşı öngören irade beyanını gösteren resm bir eylemdir. Bu ilan, iki veya daha fazla devlet arasında savaş hali yaratmak için ulusal bir hükmetin yetkili organı veya yetkili kişisi tarafından irade beyanını sunan bir konuşma eylemi veya bir belgenin imzalanması sonucu tamamlanabilir.Savaşan taraf, uluslararası hukukta, iki veya daha fazlaegemen devletin, bir savaşa girme durumunu göstermek için kullanılan bir terimdir. Savaşlar, 51. maddesi uyarıncaönce yaptığı gibi Birleşmiş Milletler Antlaşması\\'nın veya Birleşmiş Milletler Güvenlik Konseyi kararının himayesindekendiliğinden savunma hakkını ihlal eden bir çatışmaya karşı bir veya birden çok tarafla çatışarak gerçekleşir.İç silahlı çatışma durumunda, bu çatışmanın tarafı olan devlet dışı aktörlerin hukuki açıdan değerlendirilmesi, o silahlı çatışmaya uygulanacak uluslararası hukuk kurallarını da belirlemektedir. Zira tarafların ve aynı zamanda çatışmanın niteliğine göre iç silahlı çatışmanın gerçekleşmesi olasılığı olduğu gibi, iç silahlı çatışmanın uluslararası nitelik kazanması olasılığı da bulunmaktadır. Başkaldırı, iç silahlı çatışmaların başlangıç aşaması olarak değerlendirilir. Başkaldıran gruplara asi ya da savaşan taraf statüleri tanınabilir. Savaşan statüsünün tanınması, iç silahlı çatışmaların uluslararasılaşması sonucunu doğuran aşama olarak değerlendirilebilir. Başkaldırı, tek başına uluslararası silahlı çatışmalara uygulanan insancıl hukuk kurallarının uygulanması için yeterli bir durum değildir. Hatta yalnız iç silahlı çatışmalara ilişkin düzenlemelerin uygulanması bile, bu grupların gerçekleştirdikleri eylemin belirli bir eşiği geçmesine bağlıdır.Kendilerini, bir iç silahlı çatışmanın taraflarına nazaran daha özel konumda gören ulusal bağımsızlık hareketleri, uluslararası hukuk bakımından da farklı değerlendirilirler. Bu tür gruplarla savaşan taraf ve asiler arasındaki fark, belirli bir bölgede ülkesel kontrol sağlamasalar ya da statüleri tanınmasa da, uluslararası taleplerde bulunma ve yükümlülükler üstlenme açısından ortaya çıkar. Yani Cenevre Sözleşmelerinin ilgili Ek I. Protokol 1. maddesinin uygulanabilmesi için ulusal bağımsızlık hareketlerinin resmen tanınması gerekir. Bununla beraber tanıma olmaksızın bu tür hareketlerin, olağan bir iç silahlı çatışma olarak adlandırılması durumunda nasıl bir hukuki sonuç getireceği açık değildir. Dolayısıyla uluslararası düzeyde tanıma işlemleri, her ne kadar resmbir ön koşul olmasa bile, uygulamada fiilen bir gereklilik olarak kabul edilir.Savaş suçu, asker veya sivil, kişi veya kişilerin, savaş kanunları ihlli için uluslararası ceza hukuku çerçevesinde cezalandırılabileceği suçtur. Bunlar özellikle, sivil halkın öldürülmesi, kötü muameleye tabi tutulması veya zorla çalıştırılması, savaş esirlerinin öldürülmesi ya da kötü muameleye tabi tutulması, rehinelerin öldürülmesi, kamu ve özel kişilerin mallarının yağmalanması, gereksiz yere şehirlerin yakılıp yıkılması gibi eylemleri kapsamaktadır.Devletler arası çatışmalarda savaş kanunlarının her ihlli bir savaş suçu sayılmaktadır, ama devlet içi çatışmalarda yer alan ihlller savaş suçu sayılmayabilir.Devletler ekonomik pozisyonlarını savaş zamanında canlı tutmak ve kriz yaşamamak için savaş ekonomisi kapsamında, üretim, paylaşım ve tasarruf üzerinde çeşitli düzenlemelere gitmekte ve döneme özel önlemler almaktadırlar.İnsanlık tarihi boyunca çatışma kategorisine girmiş birçok olay gerçekleşmiştir. Bunlar iki kabile arasındaki küçük çaplı dövüşmelerden, düzenli ordular arasında geçen ve yeryüzündeki insan nüfusunun çoğunluğunu etkileyen dünya savaşlarına kadar sıralanırlar.Tarihteki en ölümcül savaş, toplam kümülatif ölüm sayısı bakımından, 1939\\'dan 1945\\'e kadar, 60-85 milyon ölüm ile II. Dünya Savaşı olmuştur. Ardından da 60 milyon civarındaki ölüm sayısı ile Moğol istilaları gelmektedir.Ateşkes, savaşın ve çatışmaların geçici bir süreliğine durdurulmasına denir. Savaşan, çarpışan iki düşmanın, yaralılarını, ölülerini kaldırmak ya da barış sağlayıcı görüşmelerde vb. bulunmak üzere anlaşarak, çarpışmaları durdurmasıdır. Eş anlamlısı bırakışma, mütareke olarak da bilinir. Süresi bitince çarpışmalar tekrar başlayacağı gibi her an bu anlaşmanın bozulabilmesi de mümkündür.Barış genel anlamda düşmanlığın zıddı olan politik duruma verilen isimdir.Devletler arası diplomasilerde birbirlerine göre durumlarının düşmanlık sıfatıyla tanımlanmadığı durumlarda bu terim kullanılır.Bu durumun oluştuğu taraflar müttefik devletler olarak adlandırılır.Sembolü güvercin kuşu olarak bilinir. Çeşitli resim yarışması konularına çok dahil olmuş bir konu olmakla birlikte geniş kapsamlı olduğu da söylenir.Özel askerşirketler, silahlı veya silahsız mücadelelerde, muharebe destek, güvenlik veya güvenlik danışmanlık hizmetleri veren kr amaçlı kurumlardır. Bu şirketlerin bir kısmı \"özel sektör silah sanayi\" içinde de yer almaktadırlar.',\n",
              " \"Savaş arabası; geçmişte savaşlarda kullanılan, çoğunlukla iki tekerli ve üstü açık at arabasıdır. Genellikle bir veya iki askerin ayakta seyahat ederek savaştığı, hafif ve hızlı araçlardır.Savaş arabaları MÖ 3000 yıllarında Mezopotamya'da ortaya çıktı. Sümer kentleri Ur ve Tutub'daki bazı savaş anıtlarında yekpare tekerli, tahta gövdeli, deri kaplamalı araçlar görülür. Benzer araçlar önceleri muhtemelen kraliyet cenaze törenlerinde kullanılırken zamanla savaş ve av amacıyla kullanılmaya başlandı. En erken örneklerinde öküzlerin boyunduruğuna iliştirilmiş sabit dingiller görülür. Araba kısmı ise ön ve yan panellerle üç taraftan kapatılmış şekildedir. Mezopotamya savaş arabalarında bir sürücü ve bir mızraklı asker bulunuyordu, ancak muhtemelen bu araçların üzerinden savaşılmamıştır.İlk savaş arabaları dört tekerli iken zamanla manevra kabiliyeti daha yüksek olan iki tekerliler kullanılmaya başlandı. Parmaklıklı hafif tekerlerin icadı ve öküz yerine ikili ve dörtlü eşeklerin kullanılması ile arabaların sürati önemli oranda arttı. MÖ 2000 yıllarında savaş arabalarında atların kullanılmaya başlanması ile antik dünyada daha önce benzeri görülmemiş şekilde mobilize ordular ortaya çıktı. Mısır'da Hiksosların, Anadolu'da Hititlerin, Kuzey Hindistan'da Aryanların ve Yunanistan'da Mikenlerin kazandığı zaferlerde savaş arabaları önemli rol oynadı. MÖ 1435 yılında Antik Mısırlılar savaş arabalarını kullanmaya başladı ve aynı yüzyılın sonunda dört parmaklıklı tekerlere sahip hafif savaş arabaları tüm Levant bölgesinde yaygınlaşarak Girit'e ve Güney Avrupa'ya ulaştı.\",\n",
              " 'Ön Türklerden gelme ve Selçuklu Türklerine kadar uzanan eski Türk savaş taktiğidir. Ordunun öncü birlikleri İlerlemek te olan düşman kuvvetlerine, sağ ve sol kanatlardan gece-gündüz sürekli olmak koşulu ile saldırılar da bulunur. Sürekliliğini kaybetmeden bu taktiksel saldırı küçük boyutlarda ancak düşman ordusunu yıpratmaktadır. Saldırıyı gerçekleştiren Esas Türk ordusu Çöl yönüne doğru çekilmektedir. Burada esas hedef Düşman kuvvetlerini susuzlukla baş başa bırakmaktır. Düşman kuvvetinin geçtiği yol boyunca bulunan su kuyuları tahrip edilir. Düşman kuvvet ordusu susuzluk ve yorgunlukla hareket kabiliyetini yitirir. Bu taktikle hedefe ulaşılır.Konuya En Önemli Örnek;',\n",
              " 'Müttefik zaferiII. Dünya Savaşı, 1939\\'dan 1945\\'e kadar süren küresel savaş. Savaşa dönemin büyük güçleri ve dünya ülkelerinin büyük çoğunluğu katıldı ve iki karşıt asker ittifak kuruldu: Müttefikler ve Mihver. 30\\'dan fazla ülkeden gelen 100 milyondan fazla personelin doğrudan katıldığı bu topyekn savaşta, savaşın büyük tarafları tüm ekonomik, endüstriyel ve bilimsel kapasitelerini savaş için seferber ettiler. 70 ila 85 milyon ölümle sonuçlanan II. Dünya Savaşı, insanlık tarihindeki en ölümcül savaştı ve savaş boyunca asker personelden daha çok sivil kayıp verildi. Milyonlarca insan soykırımdan , planlanmış açlık ölümlerinden, katliamlardan ve hastalıklardan öldü. Tanklar, zırhlı araçlar, savaş uçakları, stratejik bombardımanlar, uçak gemileri, radar ve sonar, nükleer silahların geliştirilmesi ve roketler gibi birçok savaş teknolojisi savaşta önemli rol oynadı.II. Dünya Savaşı\\'nın 1 Eylül 1939\\'da Almanya\\'nın Polonya\\'yı istila etmesi ve ardından 3 Eylül\\'de Birleşik Krallık ve Fransa\\'nın Almanya\\'ya savaş ilan etmesiyle başladığı kabul edilir. 1939\\'un sonlarından 1941\\'in başlarına kadar; Almanya bir dizi harekt ve antlaşma ile Kıta Avrupası\\'nın çoğunu istila etti ve kontrolü altına aldı. Ayrıca İtalya, Japonya vediğer ülkeler ile Mihver ittifakını kurdu. Ağustos 1939\\'daki Alman-Sovyet Saldırmazlık Paktı ile Almanya ve Sovyetler Birliği, Polonya\\'yı paylaştı ve istila etti. Bu saldırmazlık anlaşmasından yararlanan Sovyetler Birliği Finlandiya ve Romanya\\'nın bir kısmını, Baltık devletlerinin ise tamamını ilhak etti. Kuzey Afrika ve Doğu Afrika\\'da harektların başlamasının ve 1940\\'ın ortalarında Fransa\\'nın 2 ay içerisinde çöküşünün ardından savaş çoğunlukla Avrupalı Mihver devletleri ve Britanya İmparatorluğu arasında Balkanlar\\'daki harektlarla, Atlantik Savaşı\\'yla, Britanya\\'da hava savaşıyla ve Blitz\\'le devam etti. 22 Haziran 1941\\'de başlarında Almanya, Avrupalı Mihver devletleri ile Sovyetler Birliği\\'ni istila ederek tarihin en büyük kara cephesi olan Doğu Cephesi\\'ni açtı ve Mihver\\'i, özellikle de Alman Wehrmacht\\'ını bir yıpratma savaşına soktu. Almanya\\'nın Sovyetler Birliği seferine Japon İmparatorluğu dahil olmadı. Asya ve Pasifik üzerinde hükmetmeyi amaçlayan Japonya, 1937\\'den beri Çin Cumhuriyeti ile savaş içerisindeydi. Aralık 1941\\'de Japonya, Güneydoğu Asya ve Orta Pasifik\\'te bulunan Amerikan ve İngiliz topraklarının ve kolonilerinin neredeyse tamamına Pearl Harbor Saldırısı gibi eşzamanlı saldırılar düzenledi. ABD\\'nin ve Birleşik Krallık\\'ın Japonya\\'ya savaş ilanından sonra Avrupalı Mihver devletleri, müttefikleri ile dayanışma içinde ABD\\'ye savaş ilan etti. Japonya kısa süre içinde Batı Pasifik\\'in çoğunu ele geçirdi 1942\\'de Midway Muharebesi\\'ni kaybetmelerinin ardından ilerlemeleri durduruldu. Daha sonra Almanya ile İtalya Kuzey Afrika\\'da müttefik güçlerine ve Stalingrad\\'da Sovyetler Birliği\\'ne karşı ağır yenilgilere uğradı. 1943\\'teki aksiliklerMihver\\'i bütün cephelerde stratejik geri çekilmeler yapmaya zorladı. 1944\\'te Batılı Müttefikler Alman işgali altındaki Fransa\\'yı geri aldı ve Sovyetler Birliği Mihver güçlerini topraklarından tamamen atıp Almanya\\'yı istila etmeye başladı. 1944 ve 1945 boyunca, Japonya\\'nın Çin\\'deki ilerlemeleri tersine dönerken, Müttefikler Japon Donanması\\'na önemli darbeler vurdu ve stratejik Batı Pasifik adalarını ele geçirdi.Avrupa\\'daki savaş, Alman işgali altındaki bölgelerin özgürleştirilmesi, Sovyetler Birliği\\'nin ve Batılı Müttefiklerin Almanya\\'yı istilası, Berlin Muharebesi, Adolf Hitler\\'in intiharı ve 8 Mayıs 1945\\'te Almanya\\'nın koşulsuz teslimiyetiyle sonuçlandı. 26 Temmuz 1945\\'te Müttefiklerin Potsdam Deklarasyonu\\'ndan ve Japonya\\'nın bu şartlara göre teslim olmayı reddetmesinin ardından, ABD ilk atom bombalarını 6 Ağustos 1945\\'te Hiroşima\\'ya ve 9 Ağustos\\'ta Nagazaki\\'ye attı. Japon adalarının potansiyel bir istilası, ek atom bombası saldırıları olasılığı ve 9 Ağustos\\'ta Japonya\\'ya karşı Sovyetlerin savaşa girmesi ve Mançurya\\'yı istilasıyla karşı karşıya kalan Japonya, 15 Ağustos 1945\\'te teslim olmaya niyetini açıkladı ve Asya\\'da Müttefikler zafer elde etti. Savaşın ardından, Almanya ve Japonya işgal edildi ve Alman ve Japon liderlere karşı savaş suçları mahkemeleri açıldı. Çoğunlukla Yunanistan ve Yugoslavya\\'da işlenmiş belgeli savaş suçlarına rağmen, İtalyan liderler ve generaller diplomatik faaliyetler sayesinde genellikle cezalar ile karşı karşıya kalmadılar.II. Dünya Savaşı, dünyanın siyasi gruplaşmasını ve sosyal yapısını değiştirdi. Birleşmiş Milletler , uluslararası işbirliğini geliştirmek ve gelecekteki potansiyel çatışmaları önlemek için kuruldu ve muzaffer büyük güçlerGüvenlik Konseyi\\'nin daimi üyeleri oldu. Sovyetler Birliği ve ABD rakip süper güçler olarak ortaya çıktı ve bu neredeyse yarım yüzyıl süren Soğuk Savaş için zemin hazırladı. Avrupa\\'nın savaştan gördüğü büyük zararın ardından, büyük güçlerinin etkisi azaldı ve bu Asya ile Afrika\\'nın dekolonizasyonunu tetikledi. Endüstrileri zarar gören çoğu ülke ekonomik iyileşme ve genişlemeye yöneldi. Siyasi bütünleşme, özellikle Avrupa\\'da, gelecekteki düşmanlıkları önleme, savaş öncesi düşmanlıkları sona erdirme ve ortak bir kimlik oluşturma çabası ile başladı.Avrupadaki savaşın genellikle 1 Eylül 1939da Polonya Seferi ile ve iki gün sonra Birleşik Krallık ve Fransa\\'nın Almanya\\'ya savaş ilanıyla başlandığı kabul edilir. Pasifik\\'te savaşın başlama tarihi olarak kabul edilen tarihlerin arasında ise, İkinci Çin-Japon Savaşı\\'nın başladığı 7 Temmuz 1937 ve Japonya\\'nın Mançurya\\'yı istila ettiği 19 Eylül 1931 yer alır.Diğer bazı görüşler ise, Çin-Japon Savaşı ile Avrupa ve kolonilerindeki savaşın eşzamanlı olarak gerçekleştiğini ve iki savaşın 1941\\'de birleştiğini savunan İngiliz tarihçi A.J.P. Taylor\\'ın görüşlerini takip eder. II. Dünya Savaşı için kullanılan diğer başlangıç tarihlerinin arasında II. İtalya-Habeşistan Savaşı\\'nın başladığı 3 Ekim 1935 bulunur. Bunun yanında, Hem I. hem de II. Dünya Savaşı\\'nın aynı \"Avrupa İç Savaşı\" veya \"İkinci Otuz Yıl Savaşı\" nın bir parçası olduğu görüşü de vardır. İngiliz tarihçi Antony Beevor, II. Dünya Savaşı\\'nın başlangıcını Mayıs\\'tan Eylül 1939\\'e kadar Sovyetler Birliği ile Moğolistan ve Japonya güçleri arasında savaşılan Halhin Gol Muharebesi olarak belirler.Savaşın bitiş tarihi hakkında da evrensel bir fikir birliği yoktur. O zamanlarda savaşın, 2 Eylül 1945\\'te Japonya\\'nın resmen teslim olarak Asya\\'daki savaşı resmen bitirmesi ile değil, 14 Ağustos 1945\\'deki mütareke ile sonlandığı kabul görüyordu. Japonya ve Müttefikler arasında bir barış antlaşması, 1951\\'de imzalandı. 1990\\'da Almanya\\'nın geleceğini ilgilendiren bir antlaşma, Doğu ve Batı Almanya\\'nın yeniden birleşmesine zemin hazırladı ve bu, II. Dünya Savaşı\\'nın sebep olduğu savaş sonrası sorunların çoğunu çözdü. Japonya ile Sovyetler Birliği arasında hiçbir zaman resmi bir barış antlaşması imzalanmadı.I. Dünya Savaşı, İttifak Devletleri\\'ninyenilgisi ve 1917\\'de Bolşeviklerin Rusya\\'da iktidarı ele geçirerek Sovyetler Birliği\\'ni kurması ile Avrupa siyasi haritasını kökten değiştirdi. Bu sırada, Fransa, Belçika, İtalya, Romanya ve Yunanistan gibi muzaffer İtilaf Devletleri savaşın bir sonucu olarak yeni topraklar kazandılar ve Avusturya-Macaristan, Osmanlı ve Rus İmparatorluklarının çöküşünden yeni ulus devletler meydana geldi.Milletler Cemiyeti, gelecekteki potansiyel bir dünya savaşını önlemek için 1919\\'da Paris Barış Konferansı sırasında kuruldu. Organizasyonun temel amaçları, kolektif güvenlik yoluyla silahlı çatışmayı önlemek, silahsızlanmak ve barışçıl müzakereler ve tahkim yoluyla uluslararası anlaşmazlıkları çözmekti.I. Dünya Savaşı\\'ndan sonra ortaya çıkan güçlü pasifist görüşlere rağmen, aynı dönemde birçok Avrupa ülkesinde irredantizm ve intikamcı milliyetçilik gelişmeye başladı. Bu görüşler özellikle, Versay Barış Antlaşması tarafından dayatılan önemli toprak, sömürge ve ekonomi kayıpları nedeniyle Almanya\\'da belirgindi. Antlaşma nedeniyle Almanya, Avrupa\\'daki topraklarının %13\\'ünü ve denizaşırı topraklarının tümünü kaybetti, diğer devletleri ilhak etmesi yasaklandı, tazminat ödemek zorunda bırakıldı ve ülkenin silahlı kuvvetlerinin büyüklüğüne ve ekipmanlarına sınırlar getirildi.1918-1919 Alman Devrimi ile Alman İmparatorluğu yıkıldı ve daha sonra Weimar Cumhuriyeti olarak bilinecek olan demokratik bir hükümet kuruldu. İki savaş arası dönem boyunca, yeni kurulan cumhuriyetin destekçileri ile hem sağ hem de sol görüşlü sert muhalifler arasında çekişme görüldü. Bir İtilaf müttefiki olan İtalya, savaş sonrası bazı toprak kazanımları elde etti; ancak İtalyan milliyetçileri, Birleşik Krallık ve Fransa\\'nın İtalyanların savaşa girişini sağlamak için verdikleri sözlerin barış antlaşmasında yerine getirilmemesi yüzünden öfkeliydiler. 1922\\'den 1925\\'e kadar, Benito Mussolini önderliğindeki faşist hareket; temsili demokrasiyi ortadan kaldıran, sosyalist, sol ve liberal güçlere baskı uygulayan ve İtalya\\'yı bir büyük güç yapıp bir \"Yeni Roma İmparatorluğu\" yaratma sözü veren yayılmacı bir dış politika izleyen milliyetçi, totaliter ve sınıf işbirlikçisi bir ideoloji ile İtalya\\'da iktidarı ele geçirdi.Adolf Hitler, 1923\\'te Alman hükümetini devirmeye yönelik başarısız bir darbe girişiminden sonra 1933\\'te Almanya Şansölyesi oldu. Hitler ırk temelli ve radikal yeni bir dünya düzenini benimseyerek demokrasiyi kaldırdı ve kısa süre sonra büyük bir tekrar silahlanma gayreti başlattı. Bu sırada Fransa, ittifakını güvence altına almak için, İtalya\\'nın sömürge olarak istediği Etiyopya\\'yı istila etmesine göz yumdu. Durum, 1935\\'in başlarında Saar Bölgesi\\'nin Almanya ile yasal olarak yeniden birleşmesi ve Hitler\\'in tekrar silahlanma programını hızlandırıp zorunlu askerliği başlatarak Versay Antlaşması\\'nı ihlal etmesi ile daha da kötüleşti.Birleşik Krallık, Fransa ve İtalya, Almanya\\'yı kontrol altına almak için Nisan 1935\\'te asker küreselleşme yolunda önemli bir adım olan Stresa Antlaşması\\'nı imzaladı; ancak aynı yılın Haziran ayında Birleşik Krallık ve Almanya arasında önceki kısıtlamaları hafifleten bağımsız bir denizcilik antlaşması imzalandı. Almanya\\'nın Doğu Avrupa\\'da geniş toprakları istila hedeflerinden endişelenmeye başlayan Sovyetler Birliği, Fransa ile bir karşılıklı yardım antlaşması hazırladı. Ancak, Fransız-Sovyet paktının yürürlüğe girmeden önce Milletler Cemiyeti bürokrasisinden geçmesi gerekiyordu ve bu da antlaşmayı etkisiz kılıyordu. Avrupa ve Asya\\'daki olaylarla yakından ilgilenen ABD, aynı yılın Ağustos ayında Yansızlık Yasaları\\'nı onadı.Mart 1936\\'da Hitler, Renanya\\'yı yeniden silahlandırarak Versay ve Locarno Antlaşmaları\\'nı ihlal etti ve yatıştırma politikası nedeniyle çok az uluslararası muhalefetle karşılaştı. Ekim 1936\\'da, Almanya ve İtalya Roma-Berlin Mihveri\\'ni kurdu. Bir ay sonra, Almanya ve Japonya, İtalya\\'nın ertesi yıl katılacağı Anti-Komintern Paktı\\'nı imzaladı.Çin\\'deki Kuomintang partisi, 1920\\'lerin ortalarında bölgesel savaş ağalarına karşı bir birleşme harektı başlattı ve Çin\\'i birleştirdi; ancak kısa süre sonra ülkede, eski müttefikleri olan Çin Komünist Partisi ve yeni ortaya çıkmış bölgesel savaş ağalarına karşı bir iç savaş başladı. 1931\\'de, hükümetinin ülkenin Asya\\'yı yönetmeyi hakkı olarak gördüğü için Çin\\'de uzun süredir nüfuz elde etmek için fırsat arayan ve giderek militaristleşen Japon İmparatorluğu, Mançurya\\'yı istila etmek ve Mançukuo kukla devletini kurmak için bir bahane olarak bir sahte bayrak operasyonu olan Mukden Olayı\\'nı gerçekleştirdi.Çin, Japonya\\'nın Mançurya\\'yı istilasını durdurması için Milletler Cemiyeti\\'ne başvurdu. Japonya, Mançurya\\'ya olan istilasından dolayı kınandıktan sonra, Milletler Cemiyeti\\'nden çekildi. İki ülke daha sonra Şanghay, Rehe ve Hebei\\'de 1933\\'teki Tanggu Ateşkesi imzalanana kadar birkaç muharebede çatıştı. Çinli gönüllüler daha sonra Mançurya, Chahar ve Suiyuan\\'da Japon saldırganlığına karşı direnişi sürdürdü. 1936\\'daki Xi\\'an Olayı\\'ndan sonra, Kuomintang ve komünist kuvvetler Japonya\\'ya karşı beraber savaşmak için ateşkes konusunda anlaştılar.II. İtalya-Habeşistan Savaşı, Ekim 1935\\'te başlayan ve Mayıs 1936\\'da son bulan bir sömürge savaşıydı. Savaş, İtalya Krallığı\\'nın silahlı kuvvetleri tarafından İtalyan Somalisi ve Eritresi\\'nden başlatılan asker harekt ile Etiyopya İmparatorluğu\\'nu istilası ile başladı. Etiyopya\\'nın asker işgali ve yeni oluşturulan İtalyan Doğu Afrikası\\'na ilhakı ile sonuçlanan savaş, ayrıca Milletler Cemiyeti\\'nin barışı koruma gücü olarak zayıflığını da ortaya çıkardı. Hem İtalya hem de Etiyopya cemiyete üye ülkelerdi; ancak cemiyet İtalya cemiyet sözleşmesinin 10. maddesini açıkça ihlal ederken çok az şey yaptı. Birleşik Krallık ve Fransa, İtalya\\'ya işgal nedeniyle yaptırım uygulanmasını desteklediler; ancak yaptırımlar tam olarak uygulanmadı ve İtalyan istilasını sona erdirmedi. Daha sonra İtalya, Almanya\\'nın Avusturya\\'yı topraklarına katma hedefine olan itirazlarını sonlandırdı.İspanya\\'da iç savaş başladığında Hitler ve Mussolini, General Francisco Franco önderliğindeki Milliyetçi isyancılara askeri destek verdi. İtalya, Milliyetçilere Nazilerin verdiğinden daha çok destek verdi; Mussolini İspanya\\'ya toplamda 70.000\\'den fazla asker ve 6.000 havacılık personelinin yanı sıra yaklaşık 720 uçak gönderdi. Sovyetler Birliği ise, İspanya Cumhuriyeti\\'nin mevcut hükümetini destekledi. Uluslararası Tugaylar olarak bilinen 30.000\\'den fazla yabancı gönüllü de ayrıca Milliyetçilere karşı savaştı. Hem Almanya hem de Sovyetler Birliği, bu veklet savaşını en gelişmiş silahlarını ve taktiklerini çatışmada test etmek için bir fırsat olarak kullandı. Milliyetçiler Nisan 1939\\'da savaşı kazandı ve artık diktatör olan Franco, savaş boyunca resmi olarak tarafsız kaldı ama çoğunlukla Mihver\\'e daha yakın davrandı. Franco\\'nun Almanya ile olan en büyük işbirliği, Doğu Cephesi\\'nde savaşmak için gönüllülerin gönderilmesiydi.Temmuz 1937\\'de Japonya, tüm Çin\\'i istila etmeye yönelik bir asker harekt ile sonuçlanan Marco Polo Köprüsü Olayı\\'nın sonrasında eski Çin imparatorluk başkenti Pekin\\'i ele geçirdi. Kısa süre içerisinde Sovyetler, Çin ile bir saldırmazlık paktı imzaladı ve Çin\\'e malzeme desteği vermeye başladı; bu Çin\\'in Almanya ile olan işbirliğini etkin bir şekilde sona erdirdi. Eylül\\'den Kasım\\'a kadar, Japon kuvvetleri Taiyuan\\'a saldırdı, Niangzi Geçidi civarında Ulusal Devrimci Ordu ile çatıştı ve Pingxing Geçidi\\'nde komünist kuvvetler ile savaştı. Generalissimo Çan Kay Şek Şanghay\\'ı savunmak için en iyi ordusunu konuşlandırdı; ancak 3 aylık çatışmaların sonucunda şehir düştü. Japonlar, Çin kuvvetlerini geri püskürtmeye devam etti ve Aralık 1937\\'de başkent Nankin\\'i ele geçirdi. Nankin\\'in düşüşünden sonra, on binlerce veya yüz binlerce Çinli sivil ve silahsızlandırılmış asker Japonlar tarafından katledildi.Mart 1938\\'de Milliyetçi Çin kuvvetleri Tai\\'erzhuang\\'da ilk önemli zaferlerini elde etti, ama daha sonra Mayıs\\'ta Xuzhou Japon kuvvetleri tarafından ele geçirildi. Haziran 1938\\'de Çin kuvvetleri, Sarı Irmak\\'ta yapay bir sel başlatarak Japonların ilerlemesini durdurdu; bu hamle Çinlilere Vuhan\\'da savunmalarını hazırlamaları için zaman kazandırdı; ancak şehir buna rağmen Ekim ayında Japonlara düştü. Japon asker zaferleri, Japonya\\'nın umduğunun aksine Çin direnişinin sonunu getirmedi; aksine Çin hükümeti iç bölgelerdeki Çongçing\\'e taşındı ve savaşmayı sürdürdü.1930\\'ların ortalarından sonuna kadar, Mançukuo\\'daki Japon kuvvetleri zaman zaman Sovyetler Birliği ve Moğolistan ile sınır çatışmaları yaşadı. Japonya\\'nın kuzeye doğru genişlemesine dayanan Hokushin-ron doktrinine o zamanda İmparatorluk Ordusu tarafından sıcak bakılıyordu. 1939\\'da Hasan Gölü\\'nde Japonların yenilmesi, devam eden İkinci Çin-Japon Savaşı ve Japon müttefiki Nazi Almanyası\\'nın Sovyetler ile tarafsızlık peşinde koşması nedeniyle bu politikayı sürdürmek zor olacaktı. Japonya ve Sovyetler Birliği sonunda Nisan 1941\\'de bir tarafsızlık paktı imzaladı; ve Japonya, Deniz Kuvvetleri tarafından desteklenen ve kuzeyin aksine güneye odaklanan Nanshin-ron doktrinini benimsedi ve bu sonunda Japonya\\'nın ABD ve Batılı Müttefikler ile savaşına yol açtı.Avrupa\\'da, İtalya ve Almanya giderek daha da saldırganlaşıyorlardı. Mart 1938\\'de Almanya, Avusturya\\'yı ilhak etti ve bu, diğer Avrupa ülkelerinden çok az tepki aldı. Bundan cesaretlenen Hitler, Çekoslovakya\\'ya, etnik Alman nüfusunun yaşadığı bir bölge olan Südetler yüzünden baskı yapmaya başladı. Daha sonra Birleşik Krallık ve Fransa, İngiliz Başbakanı Neville Chamberlain\\'in yatıştırma politikasını izledi ve bu bölgeyi, Nazilerin başka bir toprak talebi olmaması karşılığında Çekoslovak hükümetinin isteklerine karşı yapılan Münih Antlaşması ile Almanya\\'ya teslim etti. Kısa süre sonra Almanya ve İtalya, Çekoslovakya\\'yı Macaristan\\'a daha fazla toprak vermeye zorladı ve Polonya, Çekoslovakya\\'nın Zaolzie bölgesini ilhak etti.Almanya\\'nın beyan ettiği tüm talepleri Münih Antlaşması ile karşılanmış olsa da, Hitler şahsi olarak İngiliz müdahalesi yüzünden tek bir operasyonda Çekoslovakya\\'nın tamamını ele geçiremediği için öfkeliydi. Sonraki konuşmalarında Hitler, İngiliz ve Yahudi \"savaş tacirlerine\" tepki gösterdi ve Ocak 1939\\'da İngiliz donanmasının üstünlüğünü ortadan kaldırmak için Alman donanmasının gizlice büyütülmesini emretti. Mart 1939\\'da Almanya, Çekoslovakya\\'nın geri kalanını istila etti ve ardından Bohemya ve Moravya Protektorası ile Alman yanlısı bir bağımlı devlet olan Slovak Cumhuriyeti\\'ni oluşturarak ülkeyi böldü. Hitler ayrıca 20 Mart 1939\\'da Litvanya\\'ya önceden Memel Bölgesi olarak bilinen Klaipda Bölgesi\\'nin Almanya\\'ya ilhakını zorlayan bir ültimatom verdi.Paniğe kapılan Birleşik Krallık ve Fransa, Hitler\\'in Danzig Serbest Şehri üzerinde yeni iddialarda bulunması üzerine Polonya\\'nın bağımsızlığını desteklediklerini garanti etti; Nisan 1939\\'da İtalya Arnavutluk\\'u istila ettiğinde aynı garanti Romanya ve Yunanistan\\'a da verildi. Fransa ve Birleşik Krallık\\'ın Polonya ile kurduğu ittifaklardan kısa süre sonra, Almanya ve İtalya Çelik Pakt ile kendi ittifaklarını resmileştirdi. Hitler, Birleşik Krallık ve Polonya\\'yı, Almanya\\'yı \"kuşatmaya\" çalışmakla suçladı ve Birleşik Krallık\\'la imzaladığı deniz antlaşması ile Polonya\\'yla imzaladığı saldırganlık paktını iptal etti.Alman askerleri Polonya sınırına doğru hareket etmeye devam ederken, durum Ağustos sonunda genel bir kriz hline geldi. 23 Ağustos\\'ta Fransa, Birleşik Krallık ve Sovyetler Birliği arasında potansiyel bir askeri ittifakla ilgili üçlü müzakereler durduğunda, Sovyetler Birliği Almanya ile bir saldırmazlık paktı imzaladı. Bu paktın, Alman ve Sovyet \"nüfuz alanlarını\"tanımlayan ve Polonya bağımsızlığının sürdürülüp sürdürülmeyeceği sorusunu gündeme getiren gizli bir protokolü vardı. Pakt, Sovyetlerin Polonya\\'ya karşı gerçekleştirilen bir asker harekta karşı çıkma olasılığını geçersiz kıldı ve I. Dünya Savaşı\\'nda olduğu gibi, Almanya\\'nın iki cepheli bir savaşla yüzleşmek zorunda kalmayacağına dair bir güvence verdi. Bundan hemen sonra Hitler, saldırının 26 Ağustos\\'ta başlamasını emretti; ama Birleşik Krallık\\'ın Polonya ile bir karşılıklı yardım antlaşması imzaladığını ve İtalya\\'nın tarafsızlığını koruyacağını duyunca saldırıyı ertelemeye karar verdi.Birleşik Krallık\\'ın savaştan kaçınabilmek için yaptığı doğrudan müzakere taleplerine yanıt olarak Almanya, ilişkileri kötüleştirmek için bir bahane olmaları için Polonya\\'dan bazı taleplerde bulundu. 29 Ağustos\\'ta Hitler, tam yetkili bir Polonyalı diplomatın Danzig\\'in Almanya tarafından ilhakını müzakere etmek üzere derhal Berlin\\'e gelmesini ve Danzig Koridoru\\'nda Alman azınlığın ayrılık için oy vereceği bir referanduma izin verilmesini istedi. Polonya Alman taleplerine uymayı reddetti ve 30-31 Ağustos gecesi İngiliz büyükelçisi Nevile Henderson ile bir görüşme sırasında Ribbentrop, Almanya\\'nın toprak iddialarını reddedilmiş olarak gördüğünü söyledi.1 Eylül 1939\\'da Almanya, istilaya bahane olarak gösterilen birkaç sahte bayrak operasyonu düzenlemesinin ardından Polonya\\'yı istila etti. Polonya\\'ya karşı yapılan ilk Alman saldırısı, Westerplatte\\'deki Polonya savunmasına karşı gerçekleşti. Birleşik Krallık, Almanya\\'ya istilayı durdurması için bir ültimatom verdi ve ültimatomun görmezden gelinmesinin ardından, 3 Eylül\\'de Fransa ve Birleşik Krallık, ardından ise Avustralya, Yeni Zelanda, Güney Afrika ve Kanada, Almanya\\'ya savaş ilan etti. İttifak, Fransızların Saarland\\'a yaptığı küçük bir harekt dışında Polonya\\'ya doğrudan asker destek sağlamadı. Batılı Müttefikler ayrıca Almanya\\'ya karşı bir deniz ablukası başlattı; bu ülke ekonomisine ve savaş gayretine zarar verdi. Almanya buna, Müttefik ticari ve savaş gemilerine karşı bir denizaltı savaşı emri vererek tepki gösterdi ve bu daha sonra Atlantik Savaşı hline geldi.8 Eylül\\'de Alman askerleri Varşova\\'nın banliyölerine ulaştı. Polonya karşı saldırısı, Alman ilerlemesini birkaç gün boyunca durdurdu; ancak kısa süre içerisinde Wehrmacht karşı saldırıya üstün geldi ve Polonya askerlerini kuşattı. Polonya ordusundan geriye kalanlar, kuşatılmış Varşova\\'ya doğru ilerledi. 17 Eylül 1939\\'da Japonya ile ateşkes imzaladıktan sonra Sovyetler Birliği, Polonya devletinin varlığını sona erdirdiği bahanesiyle doğu Polonya\\'yı istila etti. 27 Eylül\\'de Varşova garnizonu Almanlara teslim oldu ve Polonya Ordusu\\'nun son büyük birimi 6 Ekim\\'de teslim oldu. Asker yenilgiye rağmen, Polonya asla teslim olmadı; bunun yerine sürgündeki Polonya Hükmeti\\'ni kurdu ve işgal altındaki Polonya\\'da gizli bir devlet aygıtı olarak varlığını devam ettirdi. Polonyalı asker personellerin önemli bir kısmı Romanya\\'ya ve Baltık ülkelerine tahliye edildi; bunların birçoğu daha sonra savaşın diğer cephelerinde Mihver Devletleri\\'ne karşı savaştı.Almanya batı Polonya\\'yı ilhak edip ülkenin orta kısmında bir işgal bölgesi kurarken, Sovyetler Birliği ülkenin doğusunu ilhak etti; Polonya topraklarından bazı diğer küçük parçalar Litvanya ve Slovakya tarafından ilhak edildi. 6 Ekim\\'de Hitler, Polonya\\'nın geleceğinin yalnızca Almanya ve Sovyetler Birliği tarafından belirleneceği şartıyla Birleşik Krallık ve Fransa\\'ya bir barış teklifinde bulundu. Teklif reddedildi, ve bunun üzerine Hitler Fransa\\'ya karşı bir saldırı emri verdi, ancak saldırı kötü hava koşulları nedeniyle 1940 baharına kadar ertelendi.Sovyetler Birliği, Baltık devletlerini , içlerinde Sovyet birliklerinin konuşlandırılmasını öngören \"karşılıklı yardım paktları\" imzalamaya zorladı. Kısa süre sonra, önemli miktarlardaki Sovyet asker birimleri ülkelere doğru harekete geçti. Finlandiya benzer bir antlaşma imzalamayı ve topraklarının bir kısmını Sovyetler Birliği\\'ne bırakmayı reddetti. Sovyetler Birliği Kasım 1939\\'da Finlandiya\\'yı istila etti, ve bunun üzerine Sovyetler Birliği, Milletler Cemiyeti\\'nden atıldı. Ezici sayısal üstünlüğüne rağmen, Sovyet asker başarısı asgari seviyedeydi ve Fin-Sovyet savaşı, Mart 1940\\'te Sovyetlerin Finlandiya\\'dan küçük miktarlarda toprak ilhak etmesi ile sona erdi.Haziran 1940\\'ta, Sovyetler Birliği; Estonya, Letonya ile Litvanya\\'yı ve Romanya\\'nın Besarabya ile kuzey Bukovina bölgelerini zorla ilhak etti. Bu sırada, Nazi-Sovyet siyasi yakınlaşması ve ekonomik işbirliği yavaşladı ve her iki devlet de savaş hazırlıklarına başladı.Nisan 1940\\'ta Almanya, İsveç\\'ten gelen ve Müttefiklerin kesmeye çalıştığı demir cevheri tedarikini korumak için Danimarka ve Norveç\\'i istila etti. Danimarka birkaç saatlik çatışmanın ardından teslim oldu, Norveç ise Müttefik desteğine rağmen iki ay içerisinde tamamen istila edildi. Birleşik Krallık\\'ın Norveç\\'teki savaştan ve yenilgiden duyduğu hoşnutsuzluk, Winston Churchill\\'in 10 Mayıs 1940\\'ta Başbakan olarak atanmasına yol açtı.Aynı gün, Almanlar Fransa\\'ya karşı bir asker harekt başlattı. Fransa-Almanya sınırındaki güçlü Maginot Hattı\\'nın savunmalarını aşmak için Almanya, saldırısını tarafsız Belçika, Hollanda ve Lüksemburg\\'a yöneltti. Daha sonra Alman askerleri, Müttefikler tarafından yanlış bir şekilde zırhlı araçlara karşı aşılamaz bir doğal bariyer olarak görülen Ardenler bölgesine karşı bir taaruz gerçekleştirdi. Yeni blitzkrieg taktiklerini başarıyla uygulayan Wehrmacht, hızla Manş Denizi\\'ne doğru ilerledi ve Müttefik ordularının çoğunu Lille yakınlarında kuşatarak Belçika\\'daki Müttefik güçlerini birbirlerinden ayırdı. Birleşik Krallık, Haziran ayı başlarında önemli sayılardaki Müttefik askerini kıtadan bölgeden tahliye etmeyi başardı; ancak bu askerler neredeyse tüm ekipmanlarını terk etmek zorunda kaldı.10 Haziran\\'da, İtalya hem Birleşik Krallık\\'a hem de Fransa\\'ya savaş ilan etti ve Fransa\\'yı istilaya başladı. Almanlar, zayıflamış Fransız ordusuna karşı ilerlemelerini güneye çevirdi ve 14 Haziran\\'da Paris, Alman ordusuna düştü. Sekiz gün sonra Fransa, Almanya ile bir ateşkes imzaladı; ülke Alman ve İtalyan işgal bölgelerine, ve resmi olarak tarafsız olmasına rağmen genellikle Almanya ile beraber hareket eden Vichy Rejimi altında işgal edilmemiş bir devlete bölündü. Fransa\\'nın donanmasını elinde tutmasına ise izin verildi, ama 3 Temmuz\\'da Birleşik Krallık bu filoya saldırdı.Britanya Muharebesi, Temmuz ayı başlarında Luftwaffe\\'nin gemilere ve limanlara düzenlediği saldırılar ile başladı. Birleşik Krallık Hitler\\'in barış teklifini reddetti ve Alman hava harektı Ağustos\\'ta başladı; ancak Alman kuvvetleri Kraliyet Hava Kuvvetleri\\'ni mağlup etmekte başarısız oldu; bunun bir sonucu olarak Almanya\\'nın Britanya\\'yı işgali planı süresiz ertelendi. Alman stratejik bombardımanları Blitz sırasında Londra ve diğer İngiliz şehirleri üzerinde yoğunlaştı; ancak İngiliz savaş gayretini engellemekte başarısız oldu ve Mayıs 1941\\'de büyük ölçüde sona erdi.Yeni ele geçirilen Fransız limanlarını kullanan Alman Donanması, Atlantik\\'teki İngiliz gemilerine karşı U-botları kullanarak genişletilmiş Kraliyet Donanması\\'na karşı bir başarı elde etti. İngiliz Anavatan Filosu, 27 Mayıs 1941\\'de Alman savaş gemisi Bismarck\\'ı batırarak önemli bir zafer elde etti.Kasım 1939\\'da ABD, Çin\\'e ve Batılı Müttefikler\\'e yardım edebilmek için önlemler aldı ve Yansızlık Yasaları\\'nı Müttefiklerin \"peşin ödeyerek\" alımlarına izin verecek şekilde değiştirdi. 1940\\'ta Almanya\\'nın Paris\\'i ele geçirmesinin ardından ABD, donanmasının boyutunu büyük ölçüde arttırmaya başladı. Eylül ayında ABD, sadece İngiliz üsleri için Amerikan muhriplerinin satılmasını da kabul etti. Bunlara rağmen Amerikan halkının büyük bir çoğunluğu, 1941\\'e kadar savaşa herhangi bir doğrudan asker müdahaleye karşı çıkmaya devam etti. Aralık 1940\\'ta Franklin D. Roosevelt, Hitler\\'i dünyayı istila etmeyi planlamakla suçladı ve Mihver ile herhangi bir müzakereyi; yararsız olduğunu söyleyip ABD\\'yi \"demokrasinin cephaneliği\" haline getirme çağrısı yaparak ve İngiliz savaş gayretini desteklemek için Land-Lease programlarını teşvik ederek reddetti. ABD, Almanya\\'ya karşı kapsamlı bir asker harekt için stratejik planlamaya başladı.Eylül 1940\\'ın sonunda Tripartite Paktı, Japonya, İtalya ve Almanya\\'yı Mihver devletleri olarak resmen birleştirdi. Tripartite Paktı, bir Mihver devletine saldıran, Sovyetler Birliği istisnası dışında herhangi bir ülkenin Mihver\\'in hepsine karşı savaşmak zorunda kalacağını belirtiyordu. Mihver, Kasım 1940\\'ta Macaristan, Slovakya ve Romanya\\'nın da ittifaka katılmasıyla genişledi. Romanya ve Macaristan daha sonra; Romanya\\'nın durumunda Sovyetler Birliği tarafından ilhak edilen toprakları geri almak, için Mihver\\'in Sovyetler Birliği\\'ne karşı olan savaşında önemli rollerde bulundu.Haziran 1940\\'ın başlarında İtalya Kraliyet Hava Kuvvetleri bir İngiliz denizaşırı toprağı olan Malta\\'ya saldırdı ve adayı kuşattı. Yaz sonundan sonbaharın başlarına kadar İtalya, İngiliz Somalisi\\'ni istila etti ve İngilizlerin kontrolündeki Mısır\\'a bir taaruz düzenledi. Ekim\\'de İtalya Yunanistan\\'a saldırdı; ancak saldırı ağır İtalyan kayıplarıyla geri püskürtüldü; harekt aylar içinde küçük bölgesel değişiklikler ile sona erdi. Almanya; İtalya\\'ya yardım etmek, İngilizlerin orada Romanya petrol sahalarına karşı potansiyel bir tehdit oluşturacak bir yer edinmesini engellemek ve Akdeniz\\'deki İngiliz egemenliğine bir darbe vurmak için Balkanlar\\'ı istila hazırlıklarına başladı.Aralık 1940\\'ta Britanya İmparatorluğu askerleri, Mısır ve İtalyan Doğu Afrikası\\'ndaki İtalyan güçlerine yönelik karşı saldırılar düzenlemeye başladı. Bu harektlar oldukça başarılı oldu; Şubat 1940\\'ın başlarına kadar İtalya, doğu Libya\\'nın kontrolünü kaybetti ve çok sayıda İtalyan askeri esir alındı. İtalyan Donanması da önemli kayıplar verdi; Kraliyet Donanması Taranto\\'da bir uçak gemisi saldırısı ile üç İtalyan savaş gemisini hizmet dışı bıraktı ve Matapan Burnu Muharebesi\\'nde birkaç savaş gemisini batırdı.İtalyan mağlubiyetleri, Almanya\\'nın Kuzey Afrika\\'ya bir yurt dışı sefer kuvveti göndermesine sebep oldu ve Mart 1941\\'in sonunda Erwin Rommel\\'in Alman Afrika Kolordusu, İngiliz Milletler Topluluğu güçlerini geri püskürten bir harekt başlattı. Bir aydan kısa bir süre içerisinde, Mihver kuvvetleri batı Mısır\\'a kadar ilerledi ve Tobruk limanını kuşattı.Mart 1941\\'in sonlarına doğru, Bulgaristan ve Yugoslavya Tripartite Paktı\\'nı imzaladı; ancak Yugoslav hükümeti paktın imzalanmasından iki gün sonra İngiliz yanlısı milliyetçiler tarafından yapılan bir darbe ile devrildi. Almanya buna, hem Yugoslavya hem de Yunanistan\\'ın 6 Nisan 1941\\'de başlayan eşzamanlı istilalarıyla karşılık verdi; her iki ülke de bir ay içinde teslim oldu. Almanların Balkanlar\\'ı istilası, Mayıs sonunda Yunanistan\\'ın Girit adasının havadan istilası ile tamamlandı. Mihver zaferinin hızlı olmasına rağmen, Yugoslavya\\'da Mihver\\'in işgaline karşı, savaşın sonuna kadar devam eden şiddetli ve büyük çaplı bir partizan savaşı başladı.Mayıs\\'ta, Ortadoğu\\'daki İngiliz Milletler Topluluğu güçleri, Irak\\'ta Vichy kontrolündeki Suriye içindeki üslerden gelen Alman uçakları tarafından desteklenen bir ayaklanmayı bastırdı. Haziran ve Temmuz arasında, Özgür Fransa\\'nın yardımı ile İngiliz kuvvetleri, Fransız denizaşırı toprakları Suriye ve Lübnan\\'ı istila ve işgal etti.Avrupa ve Asya\\'daki durumun öncesine göre nispeten istikrarlı olması üzerine Almanya, Japonya ve Sovyetler Birliği bir savaş için hazırlıklar yapmaya başladı. Sovyetler, Almanya ile arasındaki gerilimin artmasından çekinirken ve Japonya, Güneydoğu Asya\\'daki kaynak zengini Avrupa denizaşırı topraklarını ele geçirerek Avrupa\\'daki savaştan yararlanmayı planlarken, iki güç Nisan 1941\\'de Sovyet-Japon Tarafsızlık Paktı\\'nı imzaladı. Diğer tarafta Almanlar, düzenli olarak Sovyetler Birliği\\'ne karşı bir saldırı için hazırlık yapıyor ve askerlerini Sovyet sınırına yığıyordu.Hitler, Birleşik Krallık\\'ın savaşı bitirme tekliflerini reddetmesinin, ABD ve Sovyetler Birliği\\'nin er ya da geç Almanya\\'ya karşı savaşa gireceği umuduna dayandığına inanıyordu. Bu nedenle Hitler, Almanya\\'nın Sovyetler ile ilişkilerini güçlendirmeye veya Sovyetler\\'e saldırmaya, böylece onları Birleşik Krallık için bir etken olarak ortadan kaldırmaya karar verdi. Kasım 1940\\'ta, Sovyetler Birliği\\'nin Tripartite Paktı\\'na katılıp katılmayacağını belirlemek için müzakereler yapıldı. Sovyetler paktı imzalamaya karşı bir ilgi gösterdi; ancak Finlandiya, Bulgaristan, Türkiye ve Japonya topraklarından Almanya\\'nın kabul edilemez olarak gördüğü taleplerde bulundu. 18 Aralık 1940\\'ta Hitler, Sovyetler Birliği\\'nin istilasına hazırlanmak için bir talimat yayınladı.22 Haziran 1941\\'de, Sovyetleri kendilerine karşı bir saldırı planlamakla suçlayan Almanya, İtalya ve Romanya desteği ile Barbarossa Harektı\\'nı başlatarak Sovyetler Birliği\\'ni istila etti. Kısa süre sonra Finlandiya ve Macaristan da istilaya katıldı. Bu sürpriz saldırının ana hedefleri Baltık bölgesi, Moskova ve Ukrayna; nihai hedef ise ilerlemeyi Hazar\\'dan Beyazdeniz\\'e kadar uzanan Arkhangelsk-Astrakhan hattı yakınında durdurmaktı. Hitler\\'in harektı düzenlemekteki amaçları; Sovyetler Birliği\\'ni asker bir güç olarak ortadan kaldırmak, Komünizmi yok etmek, yerel halkı evlerinden kovarak bir Lebensraumoluşturmak ve Almanya\\'nın geri kalan düşmanlarını yenmek için gereken stratejik kaynaklara erişimi garanti etmekti.Savaştan önce Kızıl Ordu, olası bir Alman istilası durumunda stratejik karşı saldırılara hazırlanıyor olsa da, Barbarossa Harektı Sovyet yüksek asker kurulunu bir stratejik savunma taktiği uygulamaya zorladı. Yaz aylarında Mihver, Sovyet toprakları içerisinde önemli kazançlar elde etti ve bu iki taraf için de hem personel hem de malzeme açısından büyük kayıplara neden oldu. Ağustos ortasına kadar, Alman Kara Kuvvetleri Genelkomutanlığı, yüksek ölçüde gücünü kaybetmiş olan Ordu Grubu Merkez\\'in saldırısını ertelemeye ve orta Ukrayna ve Leningrad\\'a doğru ilerleyen birlikleri takviye etmek için 2. Panzer Ordusu\\'nu başka yöne yönlendirmeye karar verdi. Kiev\\'e doğru yapılan harekt çoğunlukla başarılıydı; dört Sovyet ordusunun kuşatılıp imha edilmesiyle sonuçlandı ve Mihver\\'in Kırım ile endüstriyel olarak gelişmiş doğu Ukrayna\\'ya ilerlemesini mümkün kıldı.Mihver askerlerinin dörtte üçünün ve ve hava kuvvetlerinin çoğunun Fransa ve Orta Akdeniz\\'den Doğu Cephesi\\'ne kaydırılması, Birleşik Krallık\\'ı büyük stratejisini yeniden gözden geçirmeye yöneltti. Temmuz\\'da, Birleşik Krallık ve Sovyetler Birliği Almanya\\'ya karşı bir asker ittifak kurdu ve Ağustos\\'ta, ABD ve Birleşik Krallık, ortaklaşa savaş sonrası dünya için İngiliz ve Amerikan hedeflerinin ana hatlarını çizen Atlantik Bildirisi\\'ni yayınladı. Ağustos sonlarında İngilizler ve Sovyetler, İran\\'ın petrol sahalarını koruma altına almak ve Mihver\\'in İran üzerinden Bakü petrol yataklarına veya Britanya Hindistanı\\'na doğru ilerlemesini engellemek için tarafsız İran\\'ı istila etti.Ekim ayına kadar Mihver, harektın Ukrayna ve Baltık bölgesindeki hedeflerine ulaştılar ve sadece Leningrad ile Sivastopol\\'daki kuşatmalar devam ediyordu. Mihver Moskova\\'ya karşı yaptıkları büyük harektı tekrarladılar; iki ay boyunca giderek sertleşen havalarda geçen şiddetli çatışmalardan sonra Alman ordusu, neredeyse Moskova\\'nın dış banliyölerine kadar ilerledi ve burada yorgun askerler saldırılarını askıya almak zorunda kaldı. Mihveler tarafından büyük toprak kazanımları elde edildi; ancak harekt ana hedeflerine ulaşamadı: Sovyetler iki kilit şehrin kontrolünü kaybetmedi, Sovyetler\\'in direnmek için olan kabiliyeti kırılmadı ve ve Sovyetler Birliği askeri potansiyelinin önemli bir bölümünü korudu. Böylece, Avrupa\\'daki savaşın blitzkrieg dönemi sona erdi.Aralık başlarında yeni oluşturulan rezervler, Sovyetler\\'in Mihver askerleriyle sayısal eşitliğe ulaşmasını sağladı. Bu, asgari sayıda Sovyet askerinin Doğu\\'da Japon ordusu tarafından yapılacak herhangi bir saldırıyı caydırmak için yeterli olacağını tespit eden istihbarat verileri ile birlikte, Sovyetler\\'in 5 Aralık\\'ta başlayan ve Alman birliklerini tüm cephe boyunca 100-250 kilometrebatıya iten büyük bir karşı saldırıya başlamasını sağladı.1931\\'de sahte bayrak operasyonu Mukden Olayı\\'nın gerçekleştirilmesi, 1937\\'de Japonların Amerikan gambotu USS Panay\\'ı topa tutması ve 1937 ile 1938 yıllarındaki Nankin Katliamı üzerine ABD-Japonya ilişkileri giderek kötüleşmeye başladı. 1939\\'da ABD, Japonya\\'ya aralarındaki ticaret antlaşmasının süresini uzatmayacağını bildirdi ve Amerikan kamuoyunun çoğunluğunun Japon yayılmacılığına karşı olması ABD\\'nin Japonya\\'ya kimyasal, mineral ve asker parça ihraç etmesini yasaklayıp Japon rejimi üzerindeki ekonomik baskıyı arttıran bir dizi ekonomik yaptırım ile sonuçlandı. Japonya 1939\\'da stratejik olarak önemli bir Çin şehri olan Çangşa\\'ya karşı ilk saldırısını düzenledi; ancak saldırı Eylül sonuna doğru geri püskürtüldü. İki tarafın da gerçekleştirdiği harektlara rağmen, Çin ve Japonya arasındaki savaş 1940\\'ta çıkmaza girdi. Tedarik yollarını engelleyerek Çin üzerindeki baskıyı artırmak ve Batı devletlerine karşı bir savaş durumunda Japon kuvvetlerinin daha iyi konumlanması için Japonya, Eylül 1940\\'ta kuzey Hindiçin\\'i istila ve işgal etti.Milliyetçi Çin kuvvetleri, 1940\\'ın başlarında Japonlara karşı büyük ölçekli bir karşı saldırı başlattı. Ağustos\\'ta, Çinli komünistler orta Çin\\'de bir harekt başlattı; misilleme olarak Japonya, işgal altındaki bölgelerde komünistlerin insan ve materyal kaynaklarını tüketmek için sert önlemler aldı. Çinli komünist ve milliyetçi güçler arasında devam eden karşıtlık, Ocak 1941\\'de iki gücün işbirliğini etkili şekilde sona erdiren silahlı çatışmalarla sonuçlandı. Mart ayında Japon 11. Ordusu, 19. Çin Ordusu\\'nun kararghına saldırdı ancak Shanggao\\'da geri püskürtüldü. Eylül\\'de Japon kuvvetleri Çangşa\\'yı yeniden ele geçirmek için bir harekt düzenledi ve milliyetçi Çin kuvvetleriyle çatıştı.Avrupa\\'daki Alman başarıları, Japonya\\'yı Güneydoğu Asya\\'da Avrupa hükümetleri üzerindeki baskıyı artırmaya teşvik etti. Hollanda hükümeti, Japonya\\'ya Hollanda Doğu Hint Adaları\\'ndan bir miktar petrol tedarik etmeyi kabul etti; ancak kaynaklara ek erişim için yapılan müzakereler Haziran 1941\\'de başarısızlıkla sonuçlandı. Temmuz 1941\\'de Japonya, Uzak Doğu\\'daki İngiliz ve Hollanda topraklarını tehdit etmek için güney Çinhindi\\'ye asker gönderdi. ABD, Birleşik Krallık ve diğer Batılı hükümetler bu harekete ülkelerindeki Japon mülklerinin dondurulması ve Japonya\\'ya karşı genel bir petrol ambargosu ile tepki gösterdi. Japonya aynı zamanda Batı\\'daki Alman istilasından faydalanarak amacıyla Sovyet Uzak Doğusu\\'nu istila etmeyi planlıyordu; ancak yaptırım tehditlerinden sonra operasyondan vazgeçildi.1941\\'in başlarından beri ABD ve Japonya, gergin bir durumda olan ilişkilerini iyileştirmek ve Çin\\'deki savaşı sona erdirmek amacıyla müzakerelerde bulunuyorlardı. Bu müzakereler sırasında Japonya, Amerikalılar tarafından elverişsiz olduğu gerekçesiyle reddedilen bir dizi öneride bulundu. Aynı zamanda ABD, Birleşik Krallık ve Hollanda; aralarından herhangi birine karşı bir Japon saldırısı olması durumunda bölgelerinin ortak savunulmasının tartışılacağı gizli müzakerelere başladılar. Roosevelt 1946\\'da bağımsız olması planlanan bir Amerikan himayesi olan Filipinler\\'i olası bir Japon istilasına karşı güçlendirdi ve Japonya\\'yı, ABD\\'nin herhangi bir \"komşu ülkeye\" karşı yapılacak bir Japon saldırısına tepki vereceği konusunda uyardı.İlerleme eksikliğinden bıkan ve Amerikan, İngiliz ve Hollanda yaptırımlarının çıkardığı zorlukları hissetmeye başlayan Japonya savaş hazırlıklarına başladı. 20 Kasım\\'da Hideki Tj yönetimindeki yeni bir hükümet, Japonya\\'nın son teklifi olarak Müttefiklere geçici bir işbirliği planı sundu. Plan, Çin\\'e yapılan Amerikan yardımının sona erdirilmesi ve Japonya üzerinde olan petrol ve diğer kaynaklara yönelik ambargonun kaldırılması çağrısında bulunuyordu. Buna karşılık olarak Japonya, Güneydoğu Asya\\'ya herhangi bir saldırı düzenlemeyeceğine ve kuvvetlerini güney Çinhindi\\'den çekeceğine söz verecekti. 26 Kasım\\'da sunulan Amerikan karşı planı ise Japonya\\'nın Çin\\'in tamamından koşulsuz olarak çekilmesini ve tüm Pasifik devletleriyle saldırmazlık paktları imzalamasını gerektiriyordu. Bu, Japonya\\'nın Çin\\'deki taleplerinden vazgeçmek ile Hollanda Doğu Hint Adaları\\'nda bulunan, ihtiyaç duyduğu doğal kaynakları silah zoru ile ele geçirmek arasında seçim yapmak zorunda kaldığı anlamına geliyordu; Japon ordusu Çin\\'den çekilmeyi kabul edilebilir bir seçenek olarak görmedi ve birçok subay petrol ambargosunu üstü kapalı bir savaş ilanı olarak gördü.Akdenizde Müttefikler, özellikle İngilizler açısından deniz hakimiyeti yaşamsal bir önem taşımaktadır. İngiliz İmparatorluğu\\'nun Uzak Doğu bağlantısı Akdeniz üzerinden sağlanmaktaydı. Ayrıca Kuzey Afrika\\'daki askeri varlığının takviyesi ve ikmali açısından da bu deniz yolunun önemi büyüktü. Art arda uygulanan başarılı deniz operasyonlarıbu deniz yolunda İngiliz hakimiyetini sağlamış olmakla birlikte bir süre için Uzak Doğu bağlantısı Afrika kıtasının güney ucu dolaşılmak zorunda kalınarak sağlanmıştır.Atlas Okyanusu\\'ndaki deniz savaşları ise, Bismarck olayı dışında, Alman denizaltılarıyla Müttefik deniz ve hava güçleri arasında sürmüştür. Savaşın genel çizgisi, deniz ticaret hatlarına saldıran Alman denizaltılarıyla onları önlemeye çalışan Müttefik su üstü gemileri ve uçakları arasında geçmiştir.8 Kasım 1942\\'de Britanyalı ve ABD güçlerinden oluşan bir görev kuvveti Fas ve Cezayir kıyılarına bir çıkarma yaptı. 6 Ağustos 1942 günü başlayan İngiliz taarruzu karşısında , geri çekilmek zorunda kalan Rommel, bu çıkarma harektı sonucu iki ateş arasında kalmış oluyordu.General Montgomery komutasındaki Britanyalı 8. Ordusunun ileri Harektı, Rommel\\'in döşemiş olduğu onbinlerce mayın dolayısıyla ağır aksak ilerleyebiliyor.Böylece Britanyalı 8. Ordusu, 13 Aralık 1942\\'de Tobruka ulaşabiliyor. 1943 yılının ocak ayı sonunda ise Libya tümüyle Rommelin kontrolünden çıkmıştır. Artık Kuzey Afrikada durum tümüyle ABD ve Ingilizlerin kontrolü altındadır.10 Temmuz 1943 ve 2 Mayıs 1945 tarihleri arasında Britanyalı ve Amerikalı donanmalarının Husky Harektı ile Sicilya\\'ya yapmış olduğu çıkarmayla açılmış olan cephedir.Müttefikler, Kuzey Afrikadaki Alman askeri varlığını ortadan kaldırdıktan sonra İtalya\\'ya yöneldiler. İtalya\\'ya bir çıkarma yapılmasından önce Sicilya adasındaki Alman askeri gücünün de kırılması gerekmişti. İtalya topraklarına Müttefik çıkarması iki noktadan yapılmıştır. General Montgomerynin 8. Ordusu, Sicilyadan hareketle dar Messina boğazını geçerek İtalyan çizmesinin parmak ucuna çıkmıştır.İkinci çıkarma operasyonu olan Salerno çıkarması ise, Salerno\\'nun güneyindeki iki plaja, bir İngiliz, bir Amerikan kolordusu tarafından yapılmıştır. Çıkarmanın üçüncü gününde Müttefik harektı durdurulmuş; ancak ilerleyen günlerdeki takviyeler ve ağır bombardımanlar sonucu sağlam bir köprü başı oluşturulabilmiştir.Aynı gün İtalya, Müttefikler\\'le bir mütareke imzaladı, fakat bu mütareke Salerno çıkarmasına kadar gizli tutuldu. Çıkarma birlikleri esas hedefleri olan Napoli\\'ye Harektın üçüncü haftasında ulaşıyorlar.22 Ocak 1944\\'te Müttefikler Roma\\'nın 40km güneyinde, Anzioya bir çıkarma daha yapıyorlar.Çok çetin çatışmalarla geçen İtalya Cephesi, 29 Nisan 1945\\'te İtalya topraklarındaki Alman birliklerinin Müttefikler\\'e yenilmesi ve İtalya\\'nın kayıtsız şartsız teslim olması ile cephe kapanmıştır.Kursk Savaşı , II. Dünya Savaşı sırasında Doğu Cephesi\\'nde, Alman kuvvetlerinin Kursk çıkıntısına karşı 1943 Temmuz ve Ağustos aylarında giriştikleri genel taarruzdur. Bugüne kadar yapılmış en büyük tank çarpışmaları ve bir günde en fazla kayıp verilmiş hava çatışmaları bu muharebede gerçekleşmiştir. Almanların Doğu Cephesi\\'nde gerçekleştirdiği son stratejik taarruzdur. Sonucundaki Sovyet zaferi, Doğu Cephesi\\'nde inisiyatifi Sovyetlere vermiştir ve savaşın sonuna kadar da öyle kalmıştır.Kursk çıkıntısı, Almanların Stalingrad\\'daki yenilgisi sonrasındaki Sovyet taarruzu ve Alman karşı saldırısı sonucu oluşmuştu. Almanlar, çıkıntıyı kuzey ve güney kanatlarından keserek cepheyi kısaltmayı ve Kızıl Ordu birliklerini çembere alarak yeni bir büyük zafer elde etmeyi umuyorlardı. Ancak Sovyetlerin, Hitler\\'in planları hakkında iyi bir istihbaratı vardı. Bu ve Almanların yeni silahları, özellikle de Panter tankını bekleyerek taarruzu sürekli ertelemeleri Kızıl Ordu\\'ya derin bir savunma hattı oluşturmak ve karşı saldırı için stratejik rezervleri uygun yerlere konuşlandırmak için yeterli zamanı verdi.Almanlar derin savunma hatları içinde tamamen yorulduktan sonra Sovyetler kendi karşı saldırılarını yaparak 5 Ağustos\\'ta Orel ve Belgorod\\'u ve 23 Ağustos\\'ta da Harkov\\'u geri alarak Almanları geniş bir cephede geri attılar. Bu savaş 2 yıldır işgal altında kalan Kiev\\'in 2 Kasım 1943\\'te kurtarılmasının önünü açmıştır.Sovyetler daha önce kış harektlarında başarı elde etmişlerse de bu, Sovyetlerin savaştaki ilk başarılı stratejik yaz harektıydı. Bu stratejik operasyon daha sonra harp akademileri derslerinde yer aldı. Kursk Savaşı, bir yıldırım savaşının düşman hatlarını yaramadan yenilmesiyle sonuçlanan ilk muharebedir.Normandiya Çıkarması\\'ndan 16 gün sonra ve Almanya\\'nın Sovyetler Birliği\\'ne savaş ilan ettiği günün üçüncü yıldönümünde, Alman hatlarının merkez bölümünde, 22 Haziran 1944 günü geniş kapsamlı bir Kızıl Ordu taarruzu başlatılmıştır. Stalin, harektın kod adı olarak prens Bagrationun adını seçmiştir. Napolyon savaşları sırasında, çağın en üstün taktik kumandanlarından biri sayılan Bagration, 1812 de Napolyon ordularına karşı Borodinoda verilen savaşta ölmüştü.Ruslar, 124 tümende 1,2 milyon asker, 5.200 tank, 30 bin top ve 6 bin uçağı bu cepheye sürmüşlerdir. Almanlar bu saldırıya, eksik kadrolu 63 tümende 350 bin dolayında asker, 900 tank ve 10 bin topla göğüs germek durumundaydılar.Taarruzun ilk günü bitmeden Alman hatları iki cephede yarıldı ve 53. Kolorduyla birlikte Vitebsk kenti kuşatıldı. Aynı gece Luftwaffe, Ukraynadaki Poltava Amerikan üssüne bir hava akını düzenledi. 44 adet B-17 ağır bombardıman uçağı pistde imha edildi. Üsteki yarım milyon galonluk akaryakıt da kullanılamayacak duruma getirildi.Alman 53. Ordusunun yarı mevcudunu oluşturan 5 tümen için 25 Haziran\\'da Hitler, çekilme emrini verdi ama 28 Haziran\\'a gelindiğinde 53. Kolordudan geriye pek bir şey kalmamıştı. 53. Kolordunun 5 tümeninden sadece biri, ağır kayıplarla Rus kuşatmasından kurtulabilmiştir.Aynı gün mareşal Rokossovski emrindeki Sovyet kuvvetleri Minskin hemen güney doğusundaki Bobruisk kentini ve dolayısıyla Alman 9. Ordusunu kuşatırlar. Kuşatma altındaki bu ordudan sadece 15 kişi kurtulabilecektir; Kızıl Ordu 70 bin tutsak alır.2 Temmuz 1944\\'te Rokossovskinin öncü birlikleri, Minskin 60km batısında, Varşova bağlantısını sağlayan kara ve demiryolunu keserler. Kızıl Ordu birlikleri bir haftada 225km yol kat ederek Alman tümenleri, kolorduları arasından geçip Merkez Ordular Grubunun geri bağlantısını kesmiştir.Merkez Ordular Grubundan arta kalan 4. Ordu 3 Temmuz\\'da Berezina Nehri\\'nde kalan tek köprüden batıya geçerek imha olmaktan kurtulur. Nehri geçmişlerdir ama Berezina Savaşı Alman 4. Ordusu için çok ağır kayıplarla sağlanabilen bir geçiş olmuştur. Ne var ki Berezina Nehri\\'nin batısında tutunmak da mümkün olamaz. Zaten aynı gün Minskin Rusların eline geçmesiyle de 4. Ordu kuşatılmış duruma düşmüştür. Havadan ikmal girişimleri de başlar başlamaz başarısızlığa uğrar. 11 Temmuz 1944 günü, 4. Ordudan sağ kalanlar da teslim oldular. Böylece 1941 yazında Alman işgaline giren Belarus bu harektla kurtarıldı.12. Kolordu komutanı general Müller, direnmenin intihardan farksız olacağını anlayarak elindeki tüm kuvvetlerle 8 Temmuz 1944\\'te teslim olmuştur. Berezina bataklıklarını geçip uçsuz bucaksız ormanlarda birkaç kola ayrılan 27. Kolordu, çemberden çıkmak için çabalamaktadır şimdi. 13 temmuzda Vilna kenti Rus\\'ların eline geçer ve kenti savunan Alman tümeni imha edilir.Merkez Cephede yaşananlar tam bir bozgundur. Üç haftadan kısa bir sürede Kızıl Ordunun başardığı bu dev kıskaç harektıyla Merkez Ordular Grubu, neredeyse tümüyle savaş dışı kalmıştır. Almanlar, 200 bin asker ve subay ile 22 generalin içinde bulunduğu toplam 55 tümenini kaybetmiştir. Bazı kaynaklarda ise Alman kayıplarının 400 bini bulduğu belirtilmektedir.II. Dünya Savaşının özelliklerinden biri, gerek cephede olsun, gerekse cephe gerisinde, hava unsurlarının yoğun olarak kullanılmasıdır. Cephe gerisine yönelen hava taarruzları, lojistik hedeflere yönelmiştir, silah sanayi tesisleri, destek sanayi tesisleri, enerji santralleri, petrol depolama ve rafineri tesisleri, iletişim ve ulaşım hatları bombardımanın hedefleri olmuşlardır.Britanya Savaşı\\'nın son bulması ve Doğu Seferi\\'nin başlamasıyla Alman hava kuvvetlerinin önemli bir bölümü Rusya\\'da bulunmaktadır. Dolayısıyla Alman hava kuvvetlerinin Batı\\'daki faaliyetleri, önleme faaliyetleri olarak kalmıştır.Böyle olunca Stratejik Bombardıman, esas olarak Müttefik bombardıman filolarının Alman tesislerine yönelik bombardımanlarıdır. Ne var ki, zaman zaman sivil hedefler de bu bombardımana hedef olmuştur. Köln, Essen, Bremen, Hamburg gibi Alman kentlerine yoğun hava saldırıları düzenlenmiştir.6 Haziran 1944 gece yarısı Müttefik Paraşütçü Birlikleri\\'nin Normandiya sahili arkasındaki kritik yerleşim bölgelerine indirme yapmasıyla başlamıştır. Gün ağrırken Müttefikler\\'in hava ve donanma bombardımanı ile ilk Müttefik askerleri sahilde önceden belirlenen bölgelere çıkarma yapmışlardır. Bu bölgelere Utah, Omaha, Gold, Juno ve Sword kod adları verilmişti.Müttefik kayıplarının en yüksek olduğu çıkarma bölgesi Omaha kumsalıdır. Diğer çıkarma bölgelerinde de, sert bir direnişle karşılaşılmasına rağmen ilerleme sağlanmış, yeterli derinliği olan köprü başları oluşturulmuştu.26 Haziran 1944\\'te yoğun çatışmalardan sonra Amerikalıların eline geçen Cherbourg, ibrenin artık Müttefikler\\'den yana döndüğünün açık göstergesidir. Kuvvet üstünlüğü artık yerine oturmuş, işlemeye başlamaktadır. Amerikan savaş sanayi Avrupa topraklarına oluk oluk akmaktadır. Cherbourg gibi derin bir liman, büyük teknelerin bile yanaşıp yüklerini boşaltmaları için uygundur. Müttefikler için böyle bir liman, tüm kan dolaşımının ana atardamarıdır.General Bradley\\'in Normandiya\\'daki ordular grubuna bağlı 3. Ordu\\'nun komutasına 1 Ağustos 1944\\'te General Patton atanır. Patton, Müttefik ilerlemesi yönünden yeni bir soluk getirecektir.Hitler\\'in giriştiği birkaç karşı taarruz ise ağır kayıplarla sonuçlanmış, başarısız girişimler olarak kalmıştır.Market Garden Operasyonu,II. Dünya Savaşı sırasında, Amerika Birleşik Devletleri ve Birleşik Krallık ortak güçlerinin Belçika üzerinden Almanya\\'ya girmesini amaçlayan operasyondur. Alman birliklerinin düzenli geri çekilişi nedeniyle taktik Alman galibiyeti sağlamıştır. Amerikalılar büyük kayıplar vermelerine rağmen Belçika\\'yı ele geçirmişlerdir. Ancak Hollanda\\'ya yönelik harekt Arnhem\\'daki Alman karşı saldırısı nedeniyle sonuçsuz kalmıştır.Müttefikler\\'in planları nisan ayından önce Ren Nehri\\'ni geçmeyi öngörmemektedir. Fakat 7 Mart\\'ta Remagen yakınında Ludendorff Köprüsü sağlam olarak ele geçirilince iş değişir. Tam hızla bu köprüden Reni geçerler. Bu savaşın gidişatını değiştirecek bir olanaktır. Müttefik tank ve topları, motorize birlikleri, Bonn\\'un dolayısıyla Ruhr sanayi bölgesinin hemen güneyinden bu su kanalını geçmeye başlamıştır.Patton, 29 Mart 1945\\'te Frankfurt\\'u alır, 12 Nisan\\'da ABD 9. Ordusu, Magdeburg yakınlarında Elbe Nehri\\'ni geçer. Artık Berlin\\'e 80km kalmıştır.1945 yılı başlarından itibaren Alman orduları gerek Batı\\'da Amerikan ve Britanyalı orduları karşısında, gerek Doğu\\'da Kızıl Ordu karşısında gerilemeye devam etmektedir. Ocak ayında Amerikan birlikleri Arden bölgesini ele geçirirken Kızıl Ordu da Vistül nehrine dayanır.Mart ayında Müttefik kuvvetler Ren nehrini geçerek Alman topraklarında ilerlerken Kızıl Ordu da ilerlemesini sürdürür. Nisan ayı ise Nazi yönetiminin sonu olmuştur. 23 Nisan 1945\\'te Ruslar Berlin\\'e girmiş, 30 Nisan 1945\\'te ise Hitler intihar etmiştir. Almanlar, yarım milyona yakın bir kuvvetle Berlin\\'i 2 Mayıs 1945\\'e kadar savunsalar da, yoğun Rus taarruzları karşısında 150 bin kayıpla kenti kaybederler.Yalta Konferansı\\'ndaüç büyük Müttefik devletPolonya\\'nın SSCB ile sınırını batıya doğru, yaklaşık Curzon Hattı\\'na kadar çekmeyi kararlaştırdı. Bu düzenlemenin Polonya için yol açtığı toprak kaybı ise batıdaki sınırın Almanya\\'nın zararına olarak batı yönünde kaydırılmasıyla karşılandı. Josef Stalin Yalta\\'da, Baltık Denizindeki winoujcie\\'dengüneye doğru uzanarak Szczecin\\'in batısından geçen, daha sonra Frankfurt \\'ın güneyinde NeisseNehri ile birleştiği noktaya kadar Oder Nehriniizleyen, ardından Neisse Nehri boyunca Zittau yakınındaki Çekoslovakya sınırına kadar uzanan Oder-Neisse Hattı\\'nın sınır kabul edilmesini istedi. Stalin böylece 1939\\'daişgal ettiği ve geri vermek istemediği Curzon Hattının doğusundaki topraklarını yitiren Polonya\\'nın bu kaybına karşılık bir ödün vermeyi düşünüyordu.Ama yeni sınırın tam olarak nereden geçeceği konusunda Batılı Müttefikler ile SSCB arasında anlaşmazlık çıktı. ABD ile Birleşik Krallık böyle bir düzenlemenin yalnızca çok sayıda Almanın yer değiştirmesine yol açmakla kalmayacağını , aynı zamanda gelecekte Almanya\\'yı kayıplarını geri istemeye yönelteceğini, böylece kalıcı bir barışın kurulmasını önleyeceğini öne sürdü. Onların önerdiği sınır çizgisi Oder Nehri boyunca uzanıyor, sonra Wrocawile Opole arasındaki bir noktada Oder ile birleşen bir başka Neisse Nehriniizliyordu. Anlaşmazlık nedeniyle Almanya-Polonya sınırı konusunda Yalta\\'da bir karara varılamadı. Ancak Batılı Müttefikler bu hattı barış konferansına kadar geçici sınır olarak kabul etmeye razı oldular .Temmuz-Ağustos 1945\\'te Potsdam Konferansı toplandığında, Sovyet ordusu Oder-Neisse Hattı\\'nın doğusundaki bütün toprakları işgal etmiş ve bu bölgede Sovyet yanlısı geçici bir Polonya yönetimi oluşturulmuş bulunuyordu. ABD ve Birleşik Krallık bu tek yanlı hareketi şiddetle protesto etmekle birlikte oldubittiyi kabul ederek, SSCB\\'ye bırakılan Doğu Prusya\\'nın kuzey kesimi dışında, Oder-Neisse Hattı\\'nın doğusundaki bütün bölgenin Polonya\\'nın denetimine verilmesini onayladı. Potsdam Konferansı\\'nda ayrıca bölgedeki Almanların Polonyalılarca Almanya\\'ya gönderilmesine izin verildi. Ama Polonya-Almanya sınırının nihai olarak gelecekte toplanacak bir barış konferansında çizilmesi kararlaştırıldı.7 Mayıs 1945 günü General Jodl, Almanya\\'nın teslim belgesini imzaladı. Almanya resmi olarak 8 Mayıs 1945\\'te koşulsuz teslim oldu. Bu tarih Zafer Günü olarak kutlanmaktadır. Rusya zaman farkı dolayısıyla 9 Mayıs\\'ta kutlamaktadır.Iwo Jima Savaşı , 16 Şubat 1945 - 26 Mart 1945 tarihlerinde Pasifik Okyanusunda bulunan Iwo Jima adlı küçük bir adada Japon İmparatorluğu ile Amerika Birleşik Devletleri arasında meydana gelen çatışmadır.Okinawa Savaşı , Pasifik Savaşı\\'nın son aşamasında 1945 yılında Okinawa Adalarına çıkarma yapan Amerika Birleşik Devletleri ile Japon İmparatorluğu arasında meydana gelen savaştır.Japonya, kendi adasına kadar geri çekilmek zorunda kalmasına rağmen, yoğun stratejik bombardımana karşın direnmesini sürdürmektedir. ABD başkanı Truman, Pasifik\\'teki savaşı bir an önce bitirebilmek için atom bombası kullanmaya karar verildiğini açıklar. 6 Ağustos 1945\\'te Hiroşima, 9 Ağustos 1945\\'te ise Nagasaki kentleri atom bombasıyla vurulur. Atom bombası binlerce sivilin kaybına neden olmuştur. Hem psikolojik harp olarak hem de toplu sivil tahrip açısından sıcaklığını günümüzde de korumaktadır.14 Ağustos 1945\\'te Japonya, kayıtsız şartsız teslim olmayı kabul etmiştir. Japonya\\'nın teslim belgesi ise 2 Eylül 1945\\'te USS Missouri savaş gemisinde imzalanmıştır.Birçok ölüm kaydedilmediği için savaştaki toplam kayıp sayısına ilişkin tahminler değişiklik gösteriyor. En yaygın tahmin, 20 milyon asker personel ve 40 milyon sivil olmak üzere savaşta yaklaşık 60 milyon insanın öldüğüdür. Ölen sivillerin çoğu; soykırımlar, katliamlar, stratejik bombardımanlar, bulaşıcı hastalıklar ve açlık nedeniyle öldü.Sadece Sovyetler Birliği\\'nde, savaş boyunca 8,7 milyon asker ve 19 milyon sivil olmak üzere 27 milyonu aşkın kişi öldü. Sovyetler Birliği\\'nde yaşayan insanların dörtte biri savaşta yaralandı veya öldü. Almanya 5,3 milyon askeri kayıp verdi, bunların çoğu Doğu Cephesi\\'nde ve Almanya\\'daki son muharebelerde gerçekleşti.Nazi ırkçı politikalarının doğrudan veya dolaylı bir sonucu olarak tahmini 11 ila 17 milyon sivil öldü; bunların arasında yaklaşık 6 milyon Yahudinin, Çingenelerin, eşcinsellerin, en az 1,9 milyon etnik Polonyalı\\'nın, milyonlarca diğer Slavınve diğer etnik ve azınlık grupların toplu katliamları da vardı. 1941 ve 1945 arasında 200.000\\'den fazla etnik Sırp, Yahudiler ve Çingeneler ile birlikte Yugoslavya\\'da Mihver müttefiği Hırvat Ustaşa tarafından öldürüldü. Diğer tarafta ise, aynı dönem içerisinde 50.000 ila 73.000 Boşnak ve Hırvat, Çetnikler olarak bilinen Sırp gerillalar tarafından katledildi. Ayrıca, 100.000\\'den fazla Polonyalı Ukrayna İsyan Ordusu tarafından 1943 ve 1945 arasında Volhinya ve Doğu Galiçya\\'da katledildi. Aynı zamanda, Armia Krajowa ve diğer Polonya birlikleri tarafından gerçekleştirilen misilleme saldırılarında yaklaşık 10.000 ila 15.000 Ukraynalı öldürüldü.Asya ve Pasifik\\'te, çoğu Çinliolmak üzere 3 milyon ila 10 milyondan fazla sivil, Japon kuvvetleri tarafından öldürüldü. Japonlar tarafından işlenen savaş suçları arasında en ünlü olan, elli ila üç yüz bin Çinli sivilin öldürüldüğü ve tecavüze uğradığı Nankin Katliamı\\'ydı. Tarihçi Mitsuyoshi Himeta\\'nın raporuna göre, Sank Sakusen taktiği sebebiyle 2,7 milyon sivil öldü. Bu taktikten kaynaklanan en büyük kayıplar, General Yasuji Okamura\\'nın emriyle taktiğin uygulandığı Şantung\\'da gerçekleşti.Mihver kuvvetleri savaş boyunca biyolojik ve kimyasal silahlar kullandı. Japon İmparatorluk Ordusu, bu türden çeşitli silahları Çin\\'i istila ve işgali sırasındave Sovyetler ile gerçekleşen çatışmalarda kullandı. Hem Almanlar hem de Japonlar, bu tür silahları siviller ve bazen de savaş esirleri üzerinde denediler.Sovyetler Birliği; 22.000 Polonyalı subayın katledildiği Katyn Katliamı\\'ndan, binlerce siyasi mahkmun NKVD tarafından hapsedilmesinden ve öldürülmesinden ve Kızıl Ordu tarafından ilhak edilmiş olan doğu Polonya ve Baltık devletlerinden Sibirya\\'ya sivillerin sürgünlerinden sorumluydu. Savaşın son dönemlerinde Kızıl Ordu askerleri iki milyon kadar Alman kadınına tecavüz etti.II. Dünya Savaşı öncesinde ve sırasında, hava savaşına ilişkin hiçbir uluslararası insancıl hukukun mevcut olmamasına rağmen, Avrupa ve Asya\\'daki şehirlerin stratejik bombardımanı bazen bir savaş suçu olarak sınıflandırılır. USAAF, toplam 67 Japon şehrini bombaladı, bu bombardımanlar sonucu 393.000 sivil öldü ve şehirlerdeki kentsel alanların %65\\'i yok oldu.Nazi Almanyası; bir kasıtlı imha programının parçası olarak yaklaşık 6 milyon Yahudi\\'nin öldürüldüğü Holokost ile birlikte 2,7 milyon Polonyalı ve 4 milyon diğer \"yaşamaya değmeyecek\"kişinin öldürülmesinden sorumluydu ve böylece bir \"soykırım devleti\" haline geldi. Özellikle Sovyet savaş esirleri, kasıtlı olarak kötü koşullarda tutuluyordu ve Almanya\\'nın elinde tuttuğu 5,7 milyon Sovyet savaş esirinden 3,6 milyonu Nazi kamplarında öldü. Toplama kamplarına ek olarak, insanları endüstriyel ölçekte imha etmek için için Nazi Almanyası\\'nın birçok yerinde imha kampları kuruldu. Nazi Almanyası\\'da köle işçiler sıklıkla kullanıldı; Alman işgali altındaki ülkelerden yaklaşık 12 milyon Avrupalı, Alman sanayisinde, tarımında ve savaş ekonomisinde zorla çalıştırılmak üzere kaçırıldı.Sovyetlerin Gulag sistemi, 1942 ve 1943 yıllarında savaşın neden olduğu yoksulluk ve açlığın, arasında Polonya ve Sovyetler tarafından istila edilen diğer ülkelerin vatandaşları ile Mihver savaş esirlerinin de bulunduğu mahkmların ölümüne sebep olduğu bir ölüm kampı sistemi haline geldi. Savaşın sonuna doğru, esir tutuldukları kamplardan özgürleştirilen Sovyet savaş esirleri ve ülkelerine geri gönderilen Sovyet vatandaşları NKVD tarafından bir değerlendirmeye tabi tutuldu, bu değerlendirme ile gerçek veya potansiyel Nazi işbirlikçileri olarak belirlenen 226.127 kişi Gulag kamplarına gönderildi.Çoğu köle işçi kampı olarak kullanılan Japon savaş esiri kampları da yüksek bir ölüm oranına sahipti. Uzak Doğu Uluslararası Asker Ceza Mahkemesi\\'nin bulgusuna göre Batılı savaş esirlerinin ölüm oranı, Almanlar ve İtalyanlar altındaki savaş esirlerinden 7 kat fazla olan %27\\'ydi . Japonya\\'nın teslim olmasından sonra Japonların ellerinde tuttuğu, Birleşik Krallık\\'tan gelen 37.583, Hollanda\\'dan gelen 28.500 ve ABD\\'den gelen 14.473 savaş esiri serbest bırakılırken serbest bırakılan Çinli esirlerin sayısı sadece 56\\'ydı.Kuzey Çin ve Mançurya\\'dan en az 5 milyon Çinli sivil 1935 ve 1941 yılları arasında Doğu Asya Kalkınma Kurulu tarafından madenlerde ve savaş endüstrilerinde çalışmaları üzere köleleştirildi. 1942\\'den sonra bu sayı 10 milyona ulaştı. Cava\\'da 4 ila 10 milyon kadar kişi, Japon ordusu tarafından köle işçiliğe zorlandı. Bu Cavalı işçilerinden yaklaşık 270.000\\'i Güneydoğu Asya\\'da Japonların kontrolleri altında tuttuğu diğer bölgelere gönderildi ve yalnızca 52.000\\'i Java\\'ya geri dönebildi.Avrupa\\'da işgal, iki şekilde meydana geldi. Batı, Kuzey ve Orta Avrupa\\'daAlmanya, çeşitli ekonomi politikaları kurdu ve bu politikalar üzerinden savaşın sonuna kadar 69,5 milyar reichsmarktopladı; bu değer, Nazilerin endüstriyel ürünleri, askeri teçhizatı, hammaddeleri ve diğer malları büyük ölçüde yağmalamasını içermiyor. Bu sebeple, işgal edilmiş ülkelerden elde edilen gelir Almanya\\'nın vergilerden elde ettiği kazancın %40\\'ından fazlaydı ve bu oran, savaş devam ederken toplam Alman gelirinin yaklaşık %40\\'ına yükseldi.Doğu\\'da, Lebensraum\\'un amaçlanan kazanımları hareketli cephe hatları nedeniyle asla elde edilmedi ve Sovyet yakıp yıkma taktikleri, Alman işgalcilerin kaynaklara erişimini engelledi. Batı\\'nın aksine, Nazi ırksal politikaları \"aşağı insanlar\" olarak görülen Slav kökenli insanlara karşı büyük ölçüde şiddeti teşvik etti; bu sebeple çoğu Alman ilerleyişini toplu idamlar izliyordu. İşgal edilmiş toprakların çoğunda direniş hareketlerinin kurulmuş olmasına rağmen bu gruplar, 1943\\'ün sonlarına kadar hem Doğu\\'da hem de Batı\\'da Alman harektlarına önemli bir zarar veremediler.Asya\\'da Japonya, işgali altındaki ulusları, sömürgeleştirilmiş halkları özgürleştirmek amacıyla kurulduğunu iddia eden bir Japon hegemonyası olan Büyük Doğu Asya Ortak Refahı Alanı altında birleştirdi. Japon kuvvetleri istila ettikleri bazı yerlerde yerel halk tarafından kendilerini Avrupa egemenliğinden kurtaracak kişiler olarak memnuniyetle karşılansa da, Japon savaş suçları çoğu yerde yerel kamuoyunu Japonlara karşı çevirdi. Japonya, ilk istilaları sırasında geri çekilen Müttefik kuvvetleri tarafından geride bırakılan 4.000.000 varilpetrolü ele geçirdi ve 1943\\'e kadar Hollanda Doğu Hint Adaları\\'ndaki üretim 1940\\'daki toplam üretim oranının yüzde 76\\'sı, yani 50.000.000 varilkadar yükseldi.Avrupa\\'da, savaşın başlamasından önce Müttefikler hem nüfus hem de ekonomi olarak önemli avantajlara sahipti. 1938\\'de Batılı Müttefikler , Avrupalı Mihver güçlerindenyüzde 30 daha büyük bir nüfusa ve yüzde 30 daha yüksek bir GSYİH\\'ye sahipti; eğer sömürgeler dahil edilirse, Müttefiklerin avantajları nüfusta Mihver\\'in 5 katı, GSYİH\\'de ise 2 katı olurdu. Aynı zamanda Asya\\'da, Çin Japonya\\'dan 6 kat yüksek bir nüfusa sahipti ancak GSYİH\\'si Japonya\\'dan sadece %89 kadar fazlaydı; eğer Japon sömürgeleri dahil edilirse bu oran 3 kat yüksek bir nüfusa ve %38 daha yüksek bir GSYİH\\'ye düşüyordu.ABD, savaş boyunca Müttefikler tarafından kullanılan savaş gemileri, nakliye araçları, savaş uçakları, topçular, tanklar, kamyonlar ve cephaneler gibi tüm mühimmatların yaklaşık üçte ikisini üretti. Müttefiklerin ekonomi ve nüfus avantajları Almanya ve Japonya\\'nın ilk blitzkrieg saldırıları ile büyük ölçüde hafifletilmiş olsa da, 1942\\'de ABD ve Sovyetler Birliği\\'nin Müttefiklere katılmasının ardından, savaş büyük ölçüde bir yıpratma savaşına dönüştüğü için sivil cephe ve üretim belirleyici faktör haline geldi. Müttefiklerin üretim kabiliyetinin Mihver\\'den daha yüksek olmasının ana sebebi Müttefiklerin doğal kaynaklara daha fazla erişimi olmasıyken; Almanya ve Japonya\\'nın kadınları istihdam etme konusundaki isteksizliği, Müttefiklerin stratejik bombardımanı, ve Almanya\\'nın savaş ekonomisine geç geçişi gibi diğer faktörler de buna katkıda bulundu. Ek olarak, ne Almanya ne de Japonya bu kadar uzun bir savaşın parçası olmayı planlamıyorlardı ve bunun için hazır değillerdi. Üretimlerini arttırabilmek için Almanya ve Japonya milyonlarca köle işçi kullandı; Almanya çoğu Doğu Avrupa\\'dan olmak üzere 12.000.000 insanı köleleştirirken, Japonya Uzakdoğu Asya\\'da 18.000.000 insanı köle olarak çalıştırdı.',\n",
              " 'İliştirilmiş gazetecilik ya da embedded gazetecilik, savaş ve sıcak çatışma alanlarında, çatışmanın bir tarafındaki askerlerle beraber hareket eden ve savaşı onların açısından görüp yansıtan muhabirler için kullanılan terim.Kimi yazarlara göre iliştirilmiş gazetecilik; siviller ve ordu arasındaki bağları düzenleyen ilerici bir yoldur. Buna göre, bu gazetecilik yönteminin en genişçe kullanıldığı zaman aralığı II. Dünya Savaşı\\'dır ve savaş sırasında Sovyetler Birliği bu yöntemi kullanarak döneme ait 280 film çekilmiş ve bu görüntüler günümüze kadar ulaşmıştır. Bu yazarlara göre bu durum, Büyük Vatanseverlik Savaşı sırasında silahlı kuvvetler ve gazeteciler arasındaki ilişkinin olumlu olduğunun bir göstergesidir.2001 yılındaki Afganistan İşgali\\'nde kısıtlı olarak denenmiş, güncel uygulama ise 2003 yılında başlayan Irak Savaşında yapılmaya başlanmıştır.Bu yöntem II. Felluce Muharebesi\\'nde ve Bağdat\\'ın işgalinde Amerika Birleşik Devletleri yönetimince sıkça başvurulmuştur.Colombia Üniversitesi desteğiyle yapılan bir araştırmada embedded gazetecilerin yaptığı haberlerin derinlikten yoksun olduğu, açılan ateşle vurulan insanların gösterilmediği belirtilmiştir.Değişik zamanlarda toplam 775 gazeteci ve fotoğrafçı, bulundukları birliğin yerini, silahların konumunu ifşa etmeyeceklerine dair bir sözleşme imzalayarak ve kısa bir eğitim alarak, ABD ordusuna iliştirilmiş olarak savaş bölgesine götürülmüş, sadece zırhlı araçlar içinden gördükleri savaşı Amerika Birleşik Devletleri kamuoyuna yansıtmışlardır. Neden böyle bir uygulamaya gittikleri sorulan Deniz piyade yarbayı Rick Long, \"Açık söyleyeyim, savaşı kazanmak istiyoruz. Ve enformasyon da bu savaşın önemli bir parçası. Biz de enformasyon alanını domine etmek istiyoruz\" yanıtını vermiştir.İngiliz yayın kuruluşu BBC tarafından hazırlanan raporda, iliştirilmiş gazetecilerin haberlerini otosansürden geçirerek sundukları belirtildi. Raporda ayrıca, Irak halkının tepkilerinin çarpıtıldığı, ağırlıklı olarak koalisyon güçlerini sıcak karşılayan Iraklıların görüntülerini sundukları vurgulandı. BBC\\'nin üst düzey yöneticileri, embedded gazetecilerin bu tavrının demokrasiye yapılmış bir haksızlık olduğunu belirttiler.Şubat 2016\\'da, Amerika Birleşik Devletleri gazetesi The Boston Globe\\'da çıkan bir makalede, ülkede basının Suriye İç Savaşı yaklaşımı konusunda basınının savaştaki gerçekleri tamamen çarpıttığı bildirilmiş ve ülke basınında yer alan gelişmelerin birçoğunun gerçek ile ilgisi olmadığından bahsedilmiştir.Hindistan medyasında iliştirilmiş gazetecilik yapıldığı ve hükmetin savunulduğu haberlerin kurgulandığı ifade edildi.Sovyetler Birliği II. Dünya Savaşı sırasında iliştirilmiş gazetecilik yöntemlerini kullanarak savaş dönemine ait 280 film çekmiş bu görüntüler günümüze kadar ulaşmıştır.İliştirilmiş gazeteciliğin yaygın olarak ilk kez kullanıldığı Irak Savaşı\\'nda Türkiye\\'den gazeteciler de yer almıştır. Savaşı Türkiye\\'den \"iliştirilmiş\" olarak izleyen isimler arasında Cüneyt Özdemir, Mithat Bereket , Atilla Alicikoğlu ve Ümit Bektaş gibi isimler yer almıştır.Türkiye\\'de de zaman zaman bu yöntemin, taraflı medya ile paralel olarak kullanıldığı belirtilmiş, örnek olarak Uludere olayı gösterilmiştir. Olayın ardından iktidar yanlısı haber kanalları tarafından saatlerce hiçbir haber yapılmamış, hiçbir altyazı geçmemiş, buna karşın saatler sonra yaptığı haberlerde ise PKK mensubu bir grubun savaş uçakları ile vurulduğu bildirilmiştir. Ardından haber yönünü değiştiren ana akım medya üzerilerine bomba düşen bir grup kaçakçının öldüğü haberlerini servis etmiştir. Türkiye\\'deki iliştirilmiş gazetecilik ve taraflı medya kapsamında söz konusu medya-ordu ilişkilerine dair başka bir örneğin, Fatih Çekirge\\'nin Aralık 2016\\'daki bir televizyon programında MİT TIRları haberine atlamazdım ... Biz sonuçta devlet düşmanı değiliz şeklinde söylemi olduğu belirtilmiştir.Dicle Haber Ajansı , Anadolu Ajansı\\'nın bu gazetecilik türüne göre yayın yaptığı, TRT ve Show TV\\'nin de bu tür haberler yayınladığını iddia etti. Gezi Parkı olayları\\'nda anaakım medyanın iliştirilmiş gazetecilik yöntemlerinin uyguladığı belirtildi.Suriye İç Savaşı\\'nin bir parçası olan Suriye Kürdistanı çatışmalarında IŞİD\\'in Rojava\\'ya yönelik saldırılarında sırasında çekilen fotoğrafların medyada kullanımında iliştirilmiş gazetecilik olduğu ifade edildi. Fotoğrafları çekenlerden biri olan gazeteci Refik Tekin \"İliştirilmiş medya, çatışmalar sırasında çektiğimiz ve tanıklık ettiğimiz fotoğrafları, daha sonra olayları çarptırarak veya hükmetin olaylara ilişkin yaptığı açıklamalara dair haberlerde kullanmakta. Yaşananları görmeyen havuz medya, yaşamını yitiren 35 günlük Miray bebeğin fotoğrafını, gerçeğin aksine bir haberde kullandı\" ifadelerini kullandı.Anadolu Ajansı, aralarında Frankfurter Allgemeine Zeitung, BBC News, The Independent ve Agence France-Presse\\'inde bulunduğu birçok Avrupa basın kuruluşunun 20 Ocak 2018\\'de Türk Silahlı Kuvvetleri\\'nin Suriye\\'nin Afrin İlçesi\\'ndeki PYDYPG, PKK ve IŞİD\\'e karşı başlattığı Zeytin Dalı Harektı\\'nı bölgede DEAŞ\\'a karşı savaşan Kürt gruplara\" yönelik gibi yansıtmaya ve yanlış algı oluşturmaya çalıştığını ileri sürdü. Ajans ayrıca Avrupa basınının sahte görsel ve bilgileri kullanarak bölgedeki sivil kayıplar hakkında da yanlış bilgi yaydığını iddia etti.2014 yılında Ukrayna\\'da başlayan Donbass Savaşı sırasında Ukrayna Silahlı Kuvvetleri tarafında yer alan iliştirilmiş gazeteciler olduğu bildirilmiş ve bu gazetecilerin topladığı görüntülerle çeşitli filmlerin yapıldığı ifade edilmiştir. Bu filmler daha sonra Ukrayna Savunma Bakanlığı tarafından dağıtılmıştır.',\n",
              " \"Niki Operasyonu , Zafer Tanrıçası Nike adınahitaben,Kıbrıs Harektı sırasında 2122 Temmuz 1974 tarihinde Souda, Giritten bir tabur taşınması amacıyla Yunan komandosunun gerçekleştirdiği gizli hava ikmali operasyonu. Uçakların Lefkoşa'ya varışları üzerine, Dost ateşine maruz kalıp 33 Yunan askerinin kaybına ve üç uçağın imha edilmesi ile sonuçlanan hava harektıdır.\",\n",
              " 'Rus ve Rumen ordularının Pirus zaferiİlk Muharebe 12.000İkinci Muharebe 20.000Üçüncü Muharebe 40.000İlk Muharebe 25.000 Rusİkinci Muharebe 60.000 RusÜçüncü Muharebe 130.000 Rus ve 43.000 RumenPlevne Savunması , 19 Temmuz 1877-10 Aralık 1877 1877-1878 Osmanlı-Rus Savaşı veya diğer adıyla 93 Harbi sırasında Rusya İmparatorluk Ordusu\\'nun kuşattığı Plevnekentinin Gazi Osman Paşa komutasındaki Osmanlı Ordusu tarafından Rus ve Rumen taarruzlarına karşı 145 gün savunulması olayıdır.Osmanlı Ordusu 145 gün boyunca sayıca çok kalabalık Rus ve Rumen ordularının gerçekleştirdiği 3 büyük taarruzu püskürtmüş ve Plevne şehrini teslim etmemiştir. 145 gün süren kuşatma sonunda Osmanlı kuvvetlerininyiyecek ve cephaneleri tükenmiş ve kış mevsiminin gelmesiyle şiddetli soğukla karşı karşıya kalıp bitap düşmüş ve bunun üzerine kuşatmayı yarmak için düzenledikleri bir huruç harektı başarısızlıkla sonuçlanarak Rus ve Rumen ordularına teslim olmuşlardır.28 Nisan 1877\\'de Osmanlı İmparatorluğuna Rus İmparatorluğu tarafından gönderilen savaş ilanını içeren nota ile 1877-1878 Osmanlı-Rus Muharebesi başlamış oldu. Rusların Balkanlarda ordularını gönderip, saldırıya geçebileceği tek bir yer bulunmaktaydı, o da Osmanlı himayesi altındaki Romanya topraklarıydı ve Ruslar bu topraklar üstünde Siretnehri üzerinde Barboşi köprüsünün olduğu yerden ordularını geçirmek zorundaydı. Bu köprü kritik bir öneme sahip olmakla beraber, köprünün havaya uçurulması Osmanlılara en az 2 veya 3 ay vakit kazandıracaktı. Nitekim Osmanlı kuvvetlerine askeri danışmanlık veren İngiliz danışmanlarda bu durumun farkındaydı. Köprünün hemen yakınındaki Osmanlı Tuna donanmasının komutanınaİngiliz askeri danışmanı Hobart Paşa, Barbosinin tahrip edilmesi emrini vermiş fakat Tuna\\'daki 4 gemilik Osmanlı filo komutanı bunun bir aldatmaca veya casusluk oyunu olduğuna dair şüpheleri yüzünden emri uygulamakta 4-5 gün kadar gecikmiş ve tam emri uygulayacak iken de bu defa iş işten çoktan geçmiştir, zira Ruslar orduları ile Sava Nehri kıyısına gelip filoyu donanmaları ve karadaki topları ile ateşe alıp batırmış ve yok etmiştir. Sonuçta Osmanlılar için büyük bir fırsat daha muharebenin başında kaybedilmiştir.Zira Osmanlı ordusu Sırp ve Karadağlı isyancıları yenmiş ve ordu balkanlara hakim olmak üzereydi. Buna rağmen Osmanlı hakimiyeti altındaki Romanya\\'da kral I. Carol Osmanlıların yanındadır. Osmanlılardan belli bazı tavizler ve ayrıca kuvvet göndermesini ve buna karşılık kendisine bağlı kalacağını belirtir. Osmanlı ordusu ne Romanya\\'nın isteklerini kabul eder ne de destek amaçlı kuvvet yollar. Sonunda kral taraf değiştirip halkından da destek görerek Rusların safına geçer ve Ruslara destek için ordusunu toplamaya başlar.Vidin\\'deki Osmanlı kuvvetlerinin komutanı olan Osman Nuri Paşa, Kırım Savaşı\\'nda Ömer Lütfi Paşa\\'nın yaptığı gibi 25.000 askerle Romanya\\'ya girme ve henüz daha yerleşmemiş Rus birliklerinin üzerine saldırarak onları geriletme ve muharebeyi buraya taşıma planlarını açıklayarak Osmanlı Seraskerliğinden bu konuda izin ister. Ancak başkomutan Çırpanlı Abdülkerim Nadir Paşa ve heyeti bu öneriyi kabul etmez. Bunun yerine Romanya\\'yı boşaltıp Tuna nehri boyunca savunma yapmayı seçerler. Oysaki bölgede Osmanlı askerlerinin sayısı Ruslardan fazla olması yanında Sultan Abdülaziz döneminde dış borçlanmaya karşın, harcanan paraların önemli bir kısmı orduya harcanmış ve Osmanlı ordusu en modern teçhizatlara iyi bir donanmaya sahip bir ordu haline gelmişti. Yine redif askerleri dahil pek çok askeri birliği iyi şekilde eğitimliydi. Ancak Osmanlı ordusunun komuta kademesi için aynı durum geçerli değildi. Komutanlar arasında çekişme bulunması yanında ordudaki bir kısım subaylar siyasete katılmıştı. Bunun yanında komuta kademesinde tecrübeli kumandanlara karşın; tecrübesiz veya yeteneksiz, yetersiz kumandan ve subaylarda bulunmaktaydı. Yine komuta kademesinde çekişmeler yanında bir de kurmaylık ve askeri planlama, sevk ve idare yönünden de büyük zafiyetler mevcuttu.Ruslar Osmanlının bu hareketsiz durumundan istifade ederek Romanya\\'daki yığınaklarını iyice arttırdılar. Sonrasında teknelerle karşıya geçip Osmanlı mevzilerine hattın zayıf noktalardan saldırıya geçtiler. Oysaki Rusçuk ve Niğbolu\\'yu topa tutan Ruslar ilerleyecekleri noktaları belli etmişler ve durum Osman Paşa\\'nın dahi dikkatini çekmiştir. Bu ağır stratejik hatalar ve komuta kademesindeki sorunlar neticesi, iyi donanımına karşın Osmanlı ordusu yenilgi üstüne yenilgiye uğramaya başladı.Romanya\\'da ilerlemede dahil pek çok önerisi kabul edilmeyen Osman Nuri Paşa; son olarak bu defa Osmanlı Seraskerliğini atlayıp bizzat padişah II. Abdülhamid\\'e bir öneri sundu. Planına göre Rahova ve Vidin\\'den 20 tabur askerle kendisi yola çıkacak ve Niğbolu kalesine ulaşıp buradaki birlikleri yanına alarak bir kolordu oluşturup, Teteven ve Turyan geçitlerini ele geçirip Ruslara ani bir hücumda bulunmak olduğunu belirtti. Öneri Padişah tarafından kabul edildi. Ancak buna rağmen padişahın bu kararı, bilinmeyen bir nedenle Osman Paşa\\'nın kendisine 2 gün geç iletildi. Bu 2 günlük geç bildirme sonrasında ağır neticelere sebep olmuştur. Zira emrin gelmesinin hemen akabinde kuvvetlerin eksiklerini hızla tamamlayıp 13 Temmuzda Osman Paşa Vidin\\'deki birlikleri ile yola çıktı ve Niğbolu üzerine ilerlemeye başladı. Ancak yoldayken buranın Rusların eline geçtiğini öğrenmesi üzerine ordusunu savunma ve karşı saldırı için diğer uygun bir mevzi olarak öngördüğü Plevne\\'ye yönlendirmek zorunda kaldı. 20 Temmuzda da buraya ulaştı. Bu arada Süleyman Paşa komutasında, bölgede bulunan Osmanlı ordusunun Şıpka geçidini neredeyse boş bir şekilde bulundurmasından ve tedbirsizliğinden yararlanan Ruslar bu tarihten bir gün öncestratejik öneme sahip Şıpka geçidini ele geçirdi ve Osman Paşa\\'nın geri hatlarına sarktı.Böylece Osman Paşa\\'nın planı, daha ilk uygulamada, kendi emri altında olmayan Süleyman Paşa komutasındaki diğer Osmanlı kuvvetlerinin bu gafleti neticesi zora düşmeye başladı. Bununla birlikte Osman Paşa en azından Rus kuvvetlerinin çoğunun dikkatini buraya yönlendirip, Şıpka\\'nın bu sayede diğer Osmanlı ordularının taarruzları neticesinde geri alınması umudundaydı.Plevne\\'ye Rus ordusu, Osman Paşa\\'nın hareketinden önce 8 temmuzda saldırıya geçilen Niğbolu ile birlikte buraya öncü kuvvet göndermişler ve buradaki Osmanlı askerlerinin bir kısım bölüklerini bozguna uğratmışlardı. Ancak sayıları yetersiz olduğundan öncü kuvvetler, Niğbolu ve çevreden gelen takviye Osmanlı birliklerinin gelmesi ile geri çekilmek zorunda kaldılar. Buna karşın Rus birlikleri bu defa takviye birlikler alarak 8500 kadar askerle 19 temmuzda tekrar Plevne önlerine geldiler. Kent top ateşi altına alındı. İlerleyen Rus 5.tümen komutanı General Şuldner Osman Paşa\\'nın ilerleyen kuvvetlerinden haberdardı. Bununla birlikte Osmanlı askerlerinin yorgun ve moralsiz olduğunu, hiçbir tahkimat kurmaya vakitlerinin olmadığını, elini hızlı tutarsa kenti alabileceğini düşünmekteydi. Bu sebeple birliklerinin tam olarak toplanmasını beklemeden, 20 Temmuz günü sabaha karşı saat 5.00\\'de top ateşi ile şehrin kuzeyindeki ve güneyindeki 2 noktadan genel saldırıya geçti. Rus 18. piyade alayı 1.,3. ve 5.nişancı taburları ile Türk savunma hatlarını yararak ilerleyip Osmanlıların bölgedeki redif taburları ve nizamiye taburunu zora düşürdüler. Tüfek ve top ateşi ve verilen ağır zayiata karşın bu Rus taburları ilerlemeye devam ederek Plevne eteklerine kadar ilerlediler. Ancak Osman Paşa kuvvetleri ile gelir gelmez; birliklerinin yorgunluğuna aldırmadan askerlerine saldırı ve Plevne\\'nin ele geçirilen dış tahkimatlarından Rusları atma emri verdi. 13.000 kadar Osmanlı askerinin bir seri karşı taarruzu ile pek çok asker ve en önemlisi subay kaybına uğrayan Şuldner\\'in ilerleyen 3 taburu başta olmak üzere bütün taburları önce saldırıya başladıkları noktaya ardından Plevne gerisine doğru çekilmeye başladı. Şuldner yakındaki Rus 9.kolordu komutanlığından yardım istese de bu kolordu Ruslara hemen yardım gönderemeyecek kadar uzaktaydı. Ruslar, birliklerini Plevne\\'ye 3 saat uzaklıktaki Brösliniçe şehrine kadar geri çekti. Böylece muharebe Osmanlı üstünlüğü ile sona erdi. Muharebede Ruslar 4.000 kayıp verirken, Osmanlıların kaybı 1.000\\'di.Plevne\\'yi Rusların eline geçmekten kurtaran Osman Paşa Rusların takviye alıp tekrar saldıracağını bildiğinden hızla şehir çevresinde daha fazla tahkimat yaptırmaya başlar. Osmanlı mühendisi Tevfik Paşa tarafından öncesinde; sonrasında Osman Paşa tarafından Plevne\\'de kazdırılan tahkimatlar bir nal şeklini andırsa da bir birbirleri ile tünellerle bağlantılı siperler avcı çukurları şeklinde düzenlenmiştir. Uzunluğu 36 kilometre ve hat derinliği 5 kilometreyi bulabilmekteydi. Bu da Osmanlı ordusuna aynı anda takviyelerini hattın değişik yerlerine hızla gönderme imknı sağlamaktaydı. Dahası her tabya bu siperleri ihtiyaca göre genişletme imknına sahipti.Bu arada kendi birliklerine Sofya ve çevre bölgelerden gelen takviye güçlerle Osman Paşa\\'nın birliklerinin sayısı 20-23.000 kişiye kadar ulaşır. Top sayısı ise 58\\'dir. Bunun yanında Osman Paşa muharebeden hemen önce Plevne yakınında 17 temmuzda Rusların eline geçen stratejik öneme sahip Lofça şehrini Ruslardan 27 temmuzda geri almayı başararak ikmal hatlarının daha düzenli hale gelmesini sağlar. Ruslar Lofça için karşı saldırıda bulunsalar da yerleşim birimini tekrar ele geçiremezler. Lofça\\'nın yanında Osmanlı ikmal hatlarını kesmek için Tristinik yerleşimindeki Osmanlı mevzilerine saldırıda bulunan Ruslar bunda da başarısız olurlar.Buna karşın Rusların Nikolay Kridener yönetimindeki 9.kolordusu ise özellikle savaşa katılan Romanya kralı I. Carol\\'dan ve çevre birliklerden gelen takviye kuvvetlerle36.176 asker ve 176 topa ulaşırlar. 31 Temmuzda Rus genelkurmayı asker yönünden üstünlük yanında büyük topçu üstünlüğünü de göz önünde tutarak Rus-Rumen birleşik ordusunun zafer kazanacağı ümidiyle Kridener\\'e saldırı emri verir. 9.kolordu kenti kuşatıp 3 koldan saldırıya geçer. General Schakofsky\\'nin süvarileri doğudan, Mihail Skobelev\\'in piyadeleri ise Gravitsa mevzilerinin olduğu kuzeyden saldırır. General Schakofsky \\'nin kuvvetleri iki tahkimatı ele geçirip ilerler ancak Rus birlikleri topçu birliklerine ve sayı fazlalığına karşın tepede iyi şekilde mevzilenmiş ve iyi şekilde topçuları konumlanmış Osmanlı birliklerinin etkili saldırıları ile karşılaşırlar ve direnişi bir türlü kıramazlar. Birliklerinin dörtte birinden fazlası ya yaralanmış ya da ölmüş olan Şahovski geri çekilmek zorunda kalır. Greviça\\'da mevzilere bütün güçleri ile yüklenmeye çalışan ve hatta süvarilerini bile ateş hattına süren Ruslar buna rağmen fazla bir kazanç elde edemeyip yine ağır kayıplarla geri çekilirler.Günün sonunda Osmanlı birlikleri geriledikleri mevzileri de geri alıp Rusları geri püskürtürler. Rusların 7.300 kaybına karşılık Osmanlıların sadece 2000 kaybı bulunmaktaydı.2. Plevne Muharebesinden sonra Osman Paşa bir taarruza geçerek kuşatma yapan Rus birliklerini yok etme imknını kullanamadı. Osman Paşa\\'nın Rusların kendi ordusunu tamamen kuşatma yönünde bir harekta girişeceğini fark edip, kuvvetlerini Plevne\\'den geri çekerek Ruslara karşı başka bir yerde savunma ve muharebe harektına girişmesi yönündeki planı ise II.Abdülhamit tarafından kabul edilmedi. Zira uluslararası kamuoyunda bu savunma dikkat çekmişti ve padişah, bu direnişin sürmesi ile hem diplomatik olarak Rusları uluslararası kamuoyunda baskı altına alarak savaşın Osmanlı lehine sonuçlanmasını sağlayacak diplomatik bir başarı kazanmayı; hem de Şıpka geçidini ordusunun ele geçirerek çekilmeye gerek kalmadan Rusların askeri açından zorlanabileceğini düşünüyordu. Bu arada Osman Paşa, Süleyman Paşa ve Mehmet Ali Paşa ile yazışmalar yapar ve bunun akabinde bu paşalar Osman Paşa\\'nın bir aldatma amaçlı saldırı düzenleyip düşmanı oyalarken eş zamanlı Rus ordusuna saldırıp Rusları Bulgaristan\\'daki mevzilerinden atılabileceğini belirtirler. Osman Paşa bu plana ve iki paşanın sözüne uyarak 31 Ağustos 1877 tarihinde Pelişat yönüne 1.300 Rus askerinin hayatına mal olan, Osmanlıların ise 1.000 asker kaybettiği küçük bir atlı taarruz yaptı. Ancak Şıpka ordusu komutanı Süleyman Paşa ve Mehmet Ali Paşa Osman Paşa\\'ya söz verdikleri şekilde Ruslara aynı anda eş zamanlı bir saldırı yapamadılar. Neticede harekt yarıda kesilmek zorunda kalındı.Bu esnada Ruslar askerlerini takviye etmeye devam ettiler. Grandük Nikolay Nikolayeviç bizzat kumandaya geçtiği Rus ordusunun sayısı 100.000 askere ulaşmıştı. Bunun yanında bir de general Alexandru Cernat komutasında 43,414 kişilik Rumen askeri birliği de ruslara yardıma geldi. Osmanlı ordusunun Lofça Muharebesi\\'ni kaybetmesi neticesi Plevne 4 koldan Rus Ordusu tarafından sarıldı. Osman Paşa\\'nın kaybedilen Lofça kalesinden katılan Osmanlı askerleriyle takviye ettiği savunma birliklerinin sayısı ancak 30.000\\'i zor buluyordu. Osman Paşa savunmanın geri kalan süresinde önemli bir asker takviyesi alamadı, Plevne\\'yi savunmaya devam etti. 11 Eylül\\'de kuşatan birlikler kapsamlı bir taarruza geçtiler. Rus birlikleri Plevne\\'nin güneyindeki mevzileri ele geçirirken Rumen askerler Grivitza mevzilerini ele geçirdiler. Ancak kayıpları ağırdı zira Osmanlı ordusunun sahip olduğu ancak Rus ve Rumen birliklerinde olmayan, o dönemin en modern silahlarından olan Krupp topları, hızlı ve seri ateş edebilen Henri Martin ve Winchester tüfekleri kuşatmada Osmanlılara sayıca az olmalarına karşın büyük üstünlük sağlıyordu. Ertesi gün Osmanlılar Rusların ele geçirdiği mevzileri tekrar geri almayı başardılar ama Romanyalılara karşı aynı başarıyı gösteremediler. Bu tarihe kadar Ruslar 20.000 asker kaybetmişti. Osmanlıların kayıpları ise 5.000 civarındaydı. Yine de bu muharebe de Osmanlı ordusunun başarısı ile sonuçlandı.Ruslar verdikleri bu yüksek kayıplardan sonra ön cepheden yaptıkları taarruzlara son verdiler. Müşir Osman Paşa\\'nın hatları yararak şehirden çıkma ve Orhaniye\\'ye çekilip burada savunmaya devam etme talebi ise Müşir Mehmet Ali Paşa\\'nın desteklemesine karşın Osmanlı Genelkurmayı tarafından kabul edilmedi. Bunun üzerine Osman Paşa bir süre daha Plevne\\'yi savunmaya devam etti. Osmanlı başkumandanlığı ve özellikle Şevket Paşa II. Abdülhamit\\'i Plevne\\'ye yardım götürebilecekleri bu sebeple savunmaya devam edilmesi hususunda ikna etmişti. Ancak eski kumandan Mehmet Ali Paşa ve Müşir Rauf Paşa ise verilen emre rağmen Plevne\\'ye yardım götürecek yolu açamadılar, dahası Osmanlı başkumandanlığında Şevket Paşa, Radomirce yenilgileri gibi belli başarısızlık ve yenilgileri Padişah\\'tan örtbas ederek durumu daha da kötüleştirdive bu konuda verilecek çekilme emrini geciktirdiler.Plevne\\'nin bu kadar uzun süre dayanmasından sabırsızlık duyan Rus ordusunun Genel Komutanı Eduard İvanoviç Todleben bizzat birliklerin başına geçti. Bu arada Graviça\\'daki mevzilerinden Baştabya\\'daki mevzilere 19 Ekim\\'de Rumenler kısmi saldırıya geçti; ancak 1.000\\'den fazla ölü bırakarak, bozgun halinde geri çekilmek zorunda kaldılar. Bununla birlikte daha önce Kırım Savaşı\\'nda kuşatma tarzı savaş konusunda büyük bir tecrübe kazanmış olan Todleben 24 Ekim\\'de Plevne\\'yi çember altına almaya karar verdi. Yapılan kısmi taarruzlarla Plevne yakınındaki çeşitli istihkam ve tepelerzapt edilip Plevne üzerindeki çember daraltıldı. Şıpkadan gelen Gorni Dubnik Muharebesi\\'ni kazanıp ilerleyenGeneral İosif Gurko 28 Ekimde 35.000 askerle Plevne-Sofya yolunu tamamen kapattı ve Plevne, dört taraftan tamamen kuşatılmış oldu. 2 Kasım\\'da Grandük Nikolay, Plevne\\'deki kuvvetlere teslim olma çağrısı yaptı, 12 Kasım\\'da Osman Paşa bu çağrıyı reddetti. Ruslar Aralık ayına kadar kuşatma altındaki birlikleri her türlü olumsuz propaganda yöntemlerini kullanıp teslim olmaya zorlasalar da başarılı olamadılar. Bu arada Rus General Skobelev 13 ve 14 Kasımda emri altındaki birliklerle general Tobleben\\'den izinsiz Plevne\\'deki Osmanlı mevzilerine bir saldırı düzenlese de, bu hareketi başarısızlıkla ve 400 Rus askerinin ölümü ile sonuçlandı.Bunun yanında Osmanlı tarafından 4 Aralıkta müşir Deli Fuat Paşa Elena Muharebesi\\'nde Rusları yenip çeşitli ağırlıklarını zapt etse de, bu zafer Plevne üzerindeki kuşatmayı kaldırmaya yetmedi ve zafer sonuçsuz kaldı. İşin daha da kötü yanı Seraskerliğe getirilen ve sözlerini tutamayıp görevden alınan müşir Mehmet Ali Paşa\\'nın kendisinden yaşça küçük Süleyman Paşa ile çekişmesi neticesi Şıpka\\'daki birliklerinin tekrar taarruzunu ve Plevne\\'ye yardım gönderme amaçlı planını şura vasıtasıyla reddedip birliklerini savunmaya zorlaması oldu. Osman Paşa\\'nın, Plevne\\'deki birliklerin çekilmesi yönündeki talebi başkomutanlıkça sonunda kabul ettiğinde artık çok geç olmuştu, bu çekilme planının kabulüne dair karar zaten bütün haberleşme hatları kesilmiş ve Ruslarca tamamen kuşatılmış Osman Paşa\\'ya da zamanında ulaştırılamamıştır.Yiyecek ve mühimmatların tükenmek üzere olduğunu gören Osman Paşa ordusuyla birlikte kuşatmayı yararak Sofya\\'ya doğru çekilme yönünde bir plan yaptı. 9 Aralık\\'ı 10 Aralık\\'a bağlayan gece sabaha yakın bir saatte kuşatmayı yararak Plevne\\'den çıktı. Rus hatlarına taarruza başladı ilk Rus hatları dağıtıldı ancak Rusların 5:1 asker avantajına sahip olduğu bir çatışma sonucu Osmanlılar tekrar Plevne\\'ye geri çekilmek zorunda kaldılar. Kimi kaynaklar bu muharebede Osman Paşa\\'nın ayağından vurulması ve atının ölmesi neticesi kuvvetlerinde Osman Paşa öldü diye yayılan söylenti üzerine askerin moralinin bozulması ve ilerlemeyi sürdürememesinden dolayı ordunun geri çekilmek zorunda kaldığından söz eder. Buna karşın bir kısım kaynaklarda zaten bu huruç harektını Rusların Plevne\\'ye soktukları Bulgar casuslar vasıtasıyla bildiklerini iddia etmektedir. Bu çatışmada Ruslar 2.000 asker, Osmanlılar ise 4.000 asker kaybetmişti. Durumun çaresizliğini gören Osman Paşa ertesi gün40.000\\'e yakın asker ve sivil bir kuvvetle birlikte teslim oldu. Teslim olan bu askerlerden sadece 10.000\\'i vatana sağ olarak dönebildi. Zaten o sırada Süleyman Paşa\\'nın sonunda yaptığı Tırnova\\'yı Ruslardan geri alma ve Plevne\\'ye yardım götürme amacı da taşıyan \"Maçka Muharebesi\"\\'ni kaybetmesi ile Plevne\\'nin durumu tamamen umutsuz bir hldeydi.Plevne Savunması tarihin akışını değiştiren birkaç olaydan biridir. Burada Osman Paşa 5 ay süre ile Rusları durdurarak ilerlemelerini geciktirmiştir. Ancak bu şehrin düşmesi Osmanlılar açısından 93 harbinin kaybedilmesinin önünü açmıştır. Zira Osmanlılar stratejik hata ve başarısız bir savunma ile stratejik öneme sahip ve Bulgaristan ile Trakya topraklarının yolunu açan Şıpka geçidini Ruslara kaptırmışlardı. Yine de geçidin çevresini askerlerle sarmışlardı. Plevne\\'nin Osmanlılarca tutulması neticesi ele geçirilen bu geçide Ruslar takviye kuvvet gönderemediklerinden savunma konumunda kaldılar. Buna rağmen Osmanlılar geçidi geri almak için yaptıkları 2. ve 3.Şıpka geçidi muharebelerini kaybettiler. Plevne\\'nin düşmesi ile Ruslar takviye kuvvetleri bu geçide göndererek 4.Şıpka Muharebesini yapıp geçit çevresindeki Osmanlı kuvvetlerini büyük bir yenilgiye uğratıp İstanbul\\'a kadar ilerlediler. Eğer Şıpka geçidini Osmanlılar tutabilip, 2. veya 3. Plevne Muharebelerinden birinde kaçan düşmanı takip etselerdi, Balkan cephesinde her şey Osmanlı lehine değişebilirdi. Bununla birliktebu büyük savunma \"Avrupa\\'nın hasta adamı\" olarak bilinen Osmanlı İmparatorluğu\\'na Batıda bir nebze hayranlık kazandırdı. Ruslara Berlin Konferansında pek de sempatik olmayan muamele yapılmasının sebeplerinden biri oldu ve Osmanlıların bu konferansta diplomatik başarı kazanmasının anahtarlarından biri oldu. Teslim olan Osman Paşa ise Rus Çarı ve ordusu tarafından iyi bir şekilde karşılanıp başarılı savunması nedeniyle Ruslarca da saygın bir muamele gördü, şehri teslim ederken Rus komutanlara teslim ettiği kılıcı askerlik kuralları gereği başarılı muharebe çıkaran generallere gösterilen saygının bir ifadesi olarak kendisine geri verildi. Savaş sonunda Osmanlı topraklarına geri dönen Osman Paşa II. Abdülhamit tarafından başarısı nedeniyle saray mareşalliğine yükseltildi. Bununla birlikte esaret koşullarının kötülüğünden esir düşen Osmanlı askerlerinin az bir kısmı savaş sonunda sağ olarak ülkelerine dönebilmiştir. Wikimedia Commons\\'ta Plevne Savunması ile ilgili çoklu ortam belgeleri bulunur.Gazi Osman PaşaRomanya Domnitoru I. CarolMoskova\\'da Plevne kuşatması sırasında ölen rus askerleri anısına 1882 yılında St Elijah meydanında yapılan Plevne ŞapeliKuşatnayı temsilen düzenlenen Scobelev Parkı,PlevneFatih St George Şapel Anıt Mezarı, PlevneEduard TotlebenBulgar sanatçı Vassia Atanassova tarafından plevne kuşatmasını resmeden tabloBükreş Romanya\\'daki askeri müzede Plevne Kuşatmasını resmeden panoramik resimler-11880\\'de rus ressam Dimitriev tarafından resmedilen Plevne önünde topçu düellosuPlevne önünde kuşatmayı yarma harektını resmeden The Illustrated London News gazetesinde 1878\\'de yayınlanan resimBükreş Romanya\\'daki askeri müzede Plevne Kuşatmasını resmeden panoramik resimler-2Saldırı öncesi Rus askerlerini resmeden tabloGravitsa\\'daki mevzilerin alınmasını gösteren bir başka resim',\n",
              " 'Savaş felsefesi, savaşları sıradan silah ve strateji konularının dışında inceleyen, savaşın anlamı ve kökeni, savaşın insanlık için anlamı ve savaş etiği felsefesidir.Savaş felsefesinin bazı çalışma alanları, tarih felsefesi, siyaset felsefesi ve hukuk felsefesi ile örtüşür.',\n",
              " \"Türkiye'nin katıldığı savaşlar listesi, Türkiye Cumhuriyeti'nin kurulduğu günden beri katıldığı savaşları içeren listedir. YPJ KCK Suriye hükmeti YPJ KCK Suriye hükmeti Ulusal Mutabakat Hükmeti Suriye Geçici HükmetiKalıcı ateşkes 23 Ekim 2020'de onaylandı Ulusal Ulusal Birlik Hükmeti 10 Mart 2021'de kuruldu\",\n",
              " \"Sefer Haliloviç Bosna Hersek Cumhuriyeti Ordusu ya daARBiHBosna Hersek Cumhuriyeti'nin ordusudur. Ordu 15 Nisan 1992 tarihinde kurulmuştur. Bosna Savaşı'nda Bosna Hersek Cumhuriyeti'nin temsilcisi olarak savaştı. Bosna Savaşı'nın bitmesiyle birlikte ARBiH, Bosna Hersek Federasyonu Ordusu'na dönüştürüldükten sonra Bosna-Hersek Silahlı Kuvvetleri tarafından feshedildi.ARBiH, Saraybosna'yı hafif silahlarla savunmuştu. Ordu kuşatılmıştı ve erzak nakli imkansız değilse de zordu. Bununla birlikte, Bihac cebindeki ARBiH kuvvetleri, düşman güçlerle çevrili olmasına rağmen bölgeyi sürekli olarak savunmaktaydı.Sırplara karşı cephede önemli bir değişiklik görmedi. Bunun yerine, bu yıl Orta Bosna'da ve Hersek'te , özellikle de Mostar bölgesinde Hırvat-Boşnak Savaşı'nın başlangıcı oldu . Bosna-Hersek ve Hırvatistan'daki ağır silahlı Sırp kuvvetleri tarafından baskı altında tutulan ve kontrol altına alınan etnik Hırvat milis güçleri - Hırvat Savunma Konseyi- odaklarını Bosna'nın kendi bölgelerini Sırplardan korumaktan, Bosnalıların elinde kalan toprakları ele geçirmeye çalışmaya kaydırdı.HVO ve ARBiH arasında, çok daha güçlü ve daha donanımlı VRS ile savaşabilecek güçlü bir kuvvet oluşturmak amacıyla yenilenmiş bir ittifak üzerinde anlaşmaya varıldı. Bu, sık sık barış görüşmelerinin yapıldığı zamandı.Bölünmüş Anlaşmanın ardından, Hırvat Ordusu, ARBiH ve HVO'nun işbirliğiyle bir dizi operasyon başlattı: Flash , Yaz '95 , Fırtına ve Mistral 2 ve Bosna kuvvetleri Sana gibi operasyonlar başlattı.Savunma Kuvvetleri\",\n",
              " \"İspanya - Peru Savaşıİspanya ve Peru arasında başladı. Daha sonra Ekvador ve Bolivya'da katıldı. Savaş 14 Şubat 1864'te İspanyolların Cinca adasını işgaliyle başladı,12 Temmuz 1883'te bir barış anlaşması imzalanmasıyla sona erdi. Ama aslında aktif asker harektlar 1866'da sona erdi. Bu adım, İspanya'nın Güney Amerika'yı geri almak için son girişimiydi. İspanyollar bu savaşı Guerra del Pacifico veya Pasifik Savaşı olarak adlandırdı.\",\n",
              " \"Mondros Ateşkes Antlaşması sonrasında Fransızların Anadolu'nun güneyini işgal etmeleri sonucu Fransız ve yerel Ermeni Kuvvetleri ile Kuvayı Milliye güçleri arasında Osmaniye'de günümüzde de Kanlıgeçit olarak bilinen mevkiide gerçekleşen savaş. Osmaniye, Gaziantep ve Suriye'yi işgali altında tutan Fransız kuvvetleri, Osmaniye-Gaziantep karayolu ve demiryolu hattını kontrol altında tutmak ve Gaziantep ve Suriye'deki kuvvetlerine lojistik destek sağlamak amacında idiler. Bu amaçla Osmaniye'den Gaziantep istikametine harekete geçmiş ve Mamure Tren İstasyonu'nu ele geçirmişlerdir. Kuvayı Milliye birliklerinin düzenlediği Mamure Baskını'nda Saim bey ile birlikte 15 Türk eri şehit olmuştur. Kadirli ve Dörtyol birlikleri ile Kanlıgeçit'i tutarak başarılı mücadeleler vermişlerse de Fransız birliklerine engel olamamışlar,Fransız birlikleri Hasanbeyli üzerinden İslahiye'ye ve oradan da Antep'e ulaşmışlardır.\",\n",
              " \"Kiev'in Moğollar tarafından kuşatılması 28 Kasım ile 6 Aralık 1240 arasında gerçekleşti ve Moğol zaferiyle sonuçlandı. Galiçya-Volhinya'ya ağır bir moral ve askeri darbe oldu ve Batu Han'ın batıya doğru Avrupa'ya ilerlemesine izin verdi.Batu Han ve Moğollar, kuzeydoğu Rusya'daki Ryazan Prensliğini fethederek 1237'nin sonlarında istilasına başladılar. Sonra, 1238'de Moğollar Güney-batıya gitti ve Vladimir ve Kozelsk şehirlerini tahrip etti. 1239'da, hem Pereyaslav hem de Çernihiv'i Kiev kentindeki manzaraları ile ele geçirdilerç Moğollar teslim talep etmek için kiev'e birkaç elçi gönderdiğinde, Çernigovlu Mihail ve daha sonra Dmytro tarafından idam edildi.Ertesi yıl, Batu Han'ın büyük Moğol general Subutay'nin taktik komutasındaki ordusu Kiev'e ulaştı. O zaman, şehir Galiçya-Volhinya Prensliği tarafından yönetildi. Kiev'nın baş Komutanı Dmytro Voyvoda iken, Galiçyali Danylo o zaman Macaristan'da oldu, işgali önlemek için bir askeri birlik arayançBatu'nun kuzeni Möngke'nin komutasındaki öncü ordu şehre yaklaştı. Möngke görünüşe göre Kiev'in ihtişamı tarafından alındı ve teslim olmak için şehir şartlarını teklif etti, ancak elçileri öldürüldü. Moğollar şehre saldırmayı seçti. Batu Han, Kiev'i rahatlatmak için yolda olan Rus vasallarının, Çorni Klobuky'nin kuvvetlerini, yok etti ve tüm Moğol ordusu şehir kapılarının dışında kamp kurdu ve Möngke'nin birliklerine katıldı.28 Kasım'da Moğollar, ağaç örtüsünün neredeyse şehir duvarlarına kadar uzandığı eski Kiev'in üç kapısından birinin yakınında mancınık kurdular. Moğollar daha sonra birkaç gün süren bir bombardımana başladı. 6 Aralık'ta Kiev'in duvarları ihlal edildi ve sokaklarda el ele mücadele izledi. Kievliler ağır kayıplar yaşadı ve Dmytro bir okla yaralandı.Gece düştüğünde Moğollar pozisyonlarını korurken lakin Kievliler şehrin orta bölgelerine geri çekildi. Birçok insan Tithes kilisesine kalabalıktı. Ertesi gün, Moğollar son saldırıya başladığında, kilisenin balkonu üzerinde duran insanların ağırlığı altında çöktü ve birçok kişiyi ezdi. Moğollar Savaşı kazandıktan sonra Kiev'i yağmaladılar. Nüfusun çoğu katledildi. İstiladan önce 50.000 kişiden yaklaşık 2.000 kişi hayatta kaldı. Şehrin çoğu yakıldı ve kırk büyük binadan sadece altı tanesi ayakta kaldı. Ancak Dmytro, cesareti için merhamet gösterdi.Kiev'deki zaferlerinden sonra Moğollar Macaristan ve Polonya'ya ilerlemek için özgürdü.\",\n",
              " 'Veraset savaşı, veraset krizi sonucu oluşan savaşlara verilen isimlerdir. Hayatını kaybeden, tahttan hal edilen ya da tahttan hal edilen eski hükümdarların tahtına ve unvanına, yönetme hakkına sahip iki veya daha fazla kişinin yönlendirilmesi ile oluşur. Saraydaki taraftarları ve hatta bazen diğer ülkeler tarafından desteklenen kişiler büyük bir iç savaşa ya da yanlarında toplanan müttefikleri ile bölgesel ya da küresel bir savaşa neden olabilen veraset savaşları; her dönem, her bölge, her din ve çoğu hanedanda etkili bir dönem noktasıdır.',\n",
              " \"Atıcı veya nişancı, kör nişancılıktan farklı olarak, silahıyla tam bir hassasiyet içerisinde atış yapabilen eğitimli ve yetenekli kişi. Ancak nişancılık hedef vurma bazlı okçuluk gibi spor dalları için de kullanılır. Bu bağlamda Nişancı'da genellikle nişancılık eğitimi almış, özel nişancı tüfeğikeskin nişancı tüfeği veya silah mermisi kullanan bir tüfek ile uzun menzilli hedeflere hassas şekilde ateş ederek, isabet oranı diğer atıcılara oranla daha yüksek olan kişilere verilen isimdir.Ancak günümüzde nişancıve keskin nişancı birbirine karıştırılarak aynı anlamlarda kullanılmaktadır. Bir nişancıyı, keskin nişancıdan ayıran temel fark; keskin nişancıların bir timin organik bir parçası veya özel keskin nişancı timi olarakgenellikle savaşma eğilimi içerisinde gizlenerek çalışması ,nişancıların ise nişancılığı bir eğitimin parçası olarak spor veya kişisel gelişim amaçlı uygulamasıdır. Ayrıca birbiriyle karıştırılan nişancı ve keskin nişancı kelimeleri gibi, sözlük anlamı olarak aynı anlamı taşıyan atıcı ve nişancı kelimeleri de günümüzde hatalı şekilde keskin nişancılık ile aynı anlamda kullanılmaktadır.Bu durumun ve hatalı kullanımın istinası ise; nişancılık eğitimi alan nişancılar tarafından spor amaçlı olarak düzenlenen müsabakaların veya bazı kategorilerinin Keskin Nişancı Müsabakası veya kategorisi olarak adlandırılmasıdır. Bunun nedeni, müsabakalarda atıcının nişancılığı çok ileri seviyelere getirdiğinin anlaşılması amaçlanmıştır.Bir nişancı için keskin nişancı kelimesinin kullanılmasına 19'ncu yüzyılda başlanılmıştır. Bu kelime Almanca Scharfschütze kelimesinden türetilmiştir.Hedefin rastlantı sonucunda, nişancı tarafından vurulmasına verilen isimdir.Nişancılık eğitimi temelde, atıcının kullanacağı silahların teknik özellikleri hakkında bilgi sahibi olmasıyla başlar.\",\n",
              " 'Belirlenmiş nişancı bir piyade takımında askeri atıcı rolüdür. Keskin nişancı terimi, Sovyet doktrininde kullanıldı, ancak askerler Dragunov terim\\'ini kullanıyordu. İsrail ordusundaki benzer rol \"sharpshooter\"dir.',\n",
              " 'Bahriye kolordusu, ya da deniz piyadeleri, kendi harektlarıyla beraber, karada ve suda deniz ve ordu harektlarını desteklemekte uzmanlaşmış bir piyade gücü ve askeri sınıf. Çoğu ülkede bahriye kolordusu deniz kuvvetlerinin bir parçasıdır, ama Troupes de marine gibi kara kuvvetlerine bağlı, veya U.S. Marines ve Royal Marines gibi bağımsız bir komutanlık da olabilmektedir.20. asırda savaşın endüstriyelleşmesiyle birlikte iniş harektları ölçeği artmıştır. Bununla beraber yüksek ihtimal karşıtlık ve farklı askeri unsurların koordinasyon ihtiyacını getirmiştir. Bahriye güçleri amfibik savaş için gerekli maharet ve yeteneklerde özelleşmekte gelişmiştir.',\n",
              " 'Denizci, dünyadaki birçok deniz kuvvetleri tarafından kullanılan küçük bir askeri rütbedir. Diğer birlik türlerinde sıradan bir rütbeyle aynı statüye sahiptir. Kayıtlı rütbe olarak kabul edilir. Donanmaya bağlı olarak, kendi başına tek bir rütbe veya benzer şekilde birkaç küçük rütbe tarafından paylaşılan bir ad olabilir.',\n",
              " \"Grenadiyekelimesinden türetilmiştir, ilk olarak 17. yüzyılın ortalarıyla sonları arasında, el bombası atmak ve bazen saldırı operasyonlarında görev almak üzere ortaya çıkan uzman bir asker türüdür. O zamanlar grenadiyeler en güçlü ve iri askerler arasından seçilirlerdi. 18. yüzyıla gelindiğinde bu tarz el bombası atıcılığı yaygın değildi ancak grenadiyeler hala fiziksel olarak en güçlü askerler arasından seçiliyordu ve savaş alanında saldırılara öncülük ederlerdi. Grenadiyeler, ayrıca kuşatma savaşlarında tahkimatlardaki gediklere yapılan taarruzlara öncülük ederlerdi.Fransave Arjantingibi bazı ülkeler Atlı Grenadiye birlikleri kurmuştu ve Britanya Ordusu bir zamanlar Atlı Grenadiye Muhafızları'na sahipti. Piyade grenadiye mevkidaşları gibi bu atlı askerler de boyutları ve güçleri göz önünde bulundurularak seçilmişti .Günümüzde terim Batı dillerinde özel olarak tasarlanmış büyük kalibreli bir patlayıcı, sis ya da gaz başlığı fırlatan bir bomba atar kullanan bir askeri tanımlamak için de kullanılmaktadır. Bu askerler bir timin parçası olarak hareket ederler.\",\n",
              " '100 Devlet adamı, Subay ve Din görevlisi İslam Dni İsmaili Şiliğiyle alkalı bir dizidir. Haşhaşiler , Sabbahler ya da Suikastçılar , Şa mezhebinin İsmlyye koluna mensup din adamı Hasan bin Sabbah tarafından 1090 yılının Eylül ayında Elemt Kalesi\\'ni zapt ettiğinde kurulmuş olan din tarikat ve siyas örgüt.\"Haşhaşi\" kelimesinin kökeni ve anlamı 19. yüzyıla kadar Batı dünyasında tartışma konusu olmuştur. 19 Mayıs 1809 tarihinde Silvestre de Sacy\\'nin Institut de France\\'da yayınladığı bildiride kelimenin etimolojisine getirdiği açıklama kabul görmüştür. Sacy\\'e göre Batı dillerinde \"suikastçı, kiralık katil\" gibi anlamlara gelen ve en erken Haçlı Seferleri kayıtlarında rastlanan \"assasini, assissini, heyssisini\" gibi kelimelerin kökeni Arapçadaki \"haşhaş\" kelimesidir. Bu kelimenin çoğulu ise \"haşhaşiyyun, haşhaşin\" gibi kelimelerdir.\"Haşhaş\" kelimesi Arapçada \"kuru ot\" ve \"hayvan yemi\" anlamına gelir. Sonraları kelimenin anlamı uyuşturucu etkisiyle bilinen hint keneviri ile özdeşleştirilmiştir. Silvestre de Sacy, Haşhaşiler\\'e bu adın haşhaş kullanma alışkanlıkları yüzünden verildiği kanısını benimsememekle beraber bu adın, şeyhin fedailerine vadettiği cenneti tattırabilmek için onlara gizlice haşhaş içirmesiyle ilgili olabileceğini düşünmüştür. Bunu da özellikle Marco Polo\\'nun seyahatnmelerinde geçen cennet bahçeleri hikyesiyle temellendirmiştir. 1273 yılında İran\\'dan geçmiş olan Marco Polo\\'nun seyahatnmesindeki hikye kısaca şöyledir:Alamut\\'tan günümüze ulaşan metinlere göre Hasan müritlerine dinin esaslarına bağlı kalanlar manasında, \"esasiyim\" demekten hoşlanırdı ve yabancı seyyahların yanlış anladıkları bu terim \"haşhaş\", afyon kuşkularının ortaya çıkmasına neden oldu. Buna ek olarak \"haşişin\" Farsça da \"huzur bozan\" veya \"huzur kaçıran\" anlamına gelmektedir.Araştırmacı yazar Faik Bulut\\'a göre ise Marco Polo\\'nun hatıralarında anlattığı bu ifadeler iki nedenden ötürü gerçekleri yansıtmamaktadır:Faik Bulut, Marco Polo\\'nun yazdıklarının İtalya\\'da hapishanedeyken gemicilerden işittiği efsanelerden ibaret olduğunu vurgulamaktadır.Bu görüşü destekler şekilde Orta Çağ İslam Tarihi konusunda dünyanın önemli üniversitelerinde görev yapan uzman tarihçiler erken dönemlerde ortaya çıkan, geniş bir alana yayılmış olan ve bazı tarihi roman yazarlarının eserlerini süsleyen bu sıra dışı cennet bahçeleri hikyesinin neredeyse tamamen gerçek dışı olduğunu belirtmektedir. Çünkü tarikatın faaliyet gösterdiği dönemde yaşamış olan hem İsmaili hem de Sünni tarihçilerineserlerinde böyle bir söyleme rastlanmamaktadır. Ayrıca Haşhaşi ismi tarihi belgelerde sadece Suriye İsmailileri\\'ni nitelemek amacıyla kullanılan yerel bir addır. İran İsmailileri için hiçbir belgede bu isim kullanılmamaktadır. Tarihçilere göre bu isim tarikat üyelerinin eylemlerine bir açıklama getirme çabası yerine alaycı bir yaklaşımla onların garip inanışlarını ve abartılı tavırlarını küçümsemeye yönelik bir ifadedir. Bunun yanında \"dağın şeyhi\" tabiri de Suriye\\'ye özgüdür. İran İsmailileri\\'nin lideri için tarihi belgelerde böyle bir isimlendirmeye rastlanmamaktadır.Nizari İsmaililerini ile aynı mezhepten olan Haşhaşiler onları saldırılardan koruyan ve zamanı gelince suikaste giden fedailerdir ve ayrı bir mezhep gibi gösterilmemelidir. Bu algının oluşma sebebi ise bazen fedailerin ibadet olarak daha farklı aktiviteler yapması veya yapıyor gibi gösterilmesidir. İslam\\'daki ilk kırılma peygamber Muhammed\\'in ölümünden sonra gerçekleşmiştir. Muhammed\\'den sonra din ve siyasi liderin kim olacağı hakkındaki tartışmalar ve gerilimler Şiilik ve Sünni mezheplerini ortaya çıkarmıştır. Başlarda Sünnilik, Arap aristokrasisi temelli iktidarın, Şiilik ise Arap olmayan muhalif Müslüman kesimin temsilcisi olmuştur. Şiilik, Arap olmayan milletlerin eski dinlerinden daha çok etkilenmiştir. Şii mezhebi 765 yılında altıncı imam Cafer es-Sadık\\'ın ölümü sonrası yeni imamın belirlenmesinde iki kola ayrılmıştır. Ana akım Şii gruplar Cafer\\'in küçük oğlu Musa Kazım\\'ı yedinci imam olarak tanımışlardır. Bu grup günümüzün On İki İmamcılık koludur. Uç gruplar ise Cfer-i Sdık\\'in büyük oğlu İsmil bin C\\'fer el-Mbarek\\'i yedinci imam olarak tanımış ve İsmililer olarak adlandırılmışlardır. İsmaililik, Yeni Platonculuk felsefeden etkilenen, ezoterik bir mezheptir. Öğreti açısından İslam\\'daki en zengin, sistematik ve felsef mezhep olarak görülür.Tarikat, İsmaililik mezhebini temel alan Fatımi Devleti\\'nde dinsel bir hizipleşme sonucu ortaya çıkmıştır. Ortaya çıkan iki koldan biri olan Nizariliğin temsilcisi olan Haşhaşiler önce İran sonra da Suriye\\'ye yayılmıştır. Kuşatılması ve ele geçirilmesi güç kaleler temelinde örgütlenmiş olan Haşhaşiler önemli kişilere yönelik suikastlere dayanan etkili bir asker strateji geliştirerek Orta Çağ İslam dünyasında çok önemli ve farklı bir güç olarak ortaya çıkmıştır. Haşhaşiler ideolojik açıdan dönemin Sünni siyasi ve din çevrelerini, özellikle de Abbasi Devleti ve onun koruyucusu olan Büyük Selçuklu Devleti\\'ni düşman kabul etmiştir. Bununla birlikte Haşhaşiler\\'in Haçlı devletlerini ve Moğol İmparatorluğu\\'nu hedef alan bazı saldırıları da olmuştur.İsmaililer ilk büyük başarılarını Fatımiler adlı Kuzey Afrika, Sicilya, Hicaz, Mısır\\'ı kontrol altında tutan bir devlet kurarak kazanmışlardır. Burada Kahire adlı yeni bir şehir kuran İsmaililer El-Ezher Medresesi\\'ni kurup burayı din öğretilerinin ve misyonerlik faaliyetlerinin merkezi haline getirmişlerdir. Fatımiler\\'in Sekizinci halifesi Mstensir\\'in ölümünden sonra Ftımler Hlifeliği\\'nin veziri ve Mstensir\\'in küçük oğlu Ahmed el-Mustl\\'nin eniştesi olan El-Efdl Şehinşh, doğal olarak halife olması gereken Mstensir\\'in büyük oğlu Nizr el-Mustaf\\'nın yerine küçük oğul Mustl\\'yi \"Dokuzuncu Ftım Hlifesi\" olarak iln edince İsmililer iki ayrı kola ayrılmış oldular. Fatımileri yöneten askeri diktatörlük halifenin küçük oğlu Mustali\\'yi, Doğu İsmailileri ve Fatımiler\\'deki din hiyerarşi ise halifenin büyük oğlu Nizar\\'ı halife olarak tanımışlardır.Onuncu Ftım Hlifesi El-mir bi\\'Ahkmil-Lh\\'ın \"Haşhaşiler\" tarafından katledilmesinden sonra Mustalilik kolu İkinci bir bölünme hadisesi daha yaşamıştır. El-mir\\'in yerine hlife olan kuzeni Onbirinci Ftım Hlifesi El-Hfız li-Dn-Allh\\'ın hlifeliğini tanımayarak El-mir bi\\'Ahkmil-Lh\\'ın yeni doğmuş oğlu Et-Tyyb Ebl-Ksım\\'ın hlife olması gerektiğini savunanlar ise Tyybyye kolunu oluşturarak Davud İsmaillik\\'ten türeyen ve Bohralar adı verilen tasavvuf-yollar halinde günümüze kadar gelebilmişlerdir. Ftımler Hlifeliği\\'nin resm mezhebi ise önce Hfızliğe dönüşmüş, daha sonra da Ftım Devleti\\'nin Selahaddin Eyyubi tarafından yıkılması neticesinde ortadan kalkmıştır.Nizarilik kolu ise Haşhaşiler\\'in koruması altında bugün IV. Ağa Han tarafından temsil edilmekte olan hanedanlarını günümüze kadar devam ettirmeyi başarmışlardır.Haşhaşiler\\'in tarihi Elemt Kalesi\\'nin alınmasıyla başlar. Hasan Sabbah uzun süren misyonerlik ve insan kazanma faaliyetleri sırasında Selçuklularla mücadele etmek için rahat edebileceği ulaşılmaz bir yer aramış, Deylem\\'de yaptığı faaliyetler sırasında Alamut Kalesi\\'nde karar kılmıştır. Büyük ve yüksek bir kayalık tepe üzerine inşa edilmiş olan bu kaleye sadece dar bir patikadan ulaşılmaktaydı.Hasan Sabbah buraya vardığında kale onu Selçuklu sultanından almış olan Zeyd-Alevler Hanedanlığı soyundan gelen Alevi Mehdi adındaki bir hükümdarın elindeydi.:23 Önce bölgeye dlerini yollayan Hasan, bölge halkını ve Alamut\\'ta yaşayanları kendi tarafına çekmiştir. Hasan Sabbah bu olayları şöyle anlatmaktadır:Bundan sonra 4 Eylül 1090 günü gizlice kaleye alınmış, kalenin önceki sahibi elinden bir şey gelmediği için kaleyi terk etmiştir. İranlı tarihçilere göre Hasan Sabbah, Mehdi\\'ye üç bin altın dinar değerinde bir senet vermiştir. Böylece Hasan Sabbah ve Haşhaşiler örgütlerini resmen kurmuş ve faaliyetlerine başlamışlardır.Haşhaşiler, Orta Çağ İslam dünyasında çok önemli rol oynamışlardır. Büyük Selçuklu Devleti\\'nin en parlak döneminde düşüşe geçmesine ve Sencer, Berkyaruk ile Muhammed Tapar arasındaki taht kavgalarına önemli etkide bulunmuşlardır. Bu süreçte bazı Selçuklu sultanlarıyla müttefik olan Haşhaşiler çoğuyla da mücadele içinde olmuşlardır. Selçukluların dağılmasından sonra da etkisini sürdüren İran Haşhaşileri Moğolların İran\\'ı ve Bağdat\\'ı ele geçirmesine kadar ayakta kalmış, sonrasında ise Moğol-Nizari Savaşı sonucu son liderleri Rükneddin Hür Şah\\'ın Hülagü\\'nün isteklerine uymasıyla 1256 yılında Alamut Kalesi, teslim olmuş 1258 yılında Lemeser Kalesi ele geçirilmiştir. Ancak Girduh kalesi 1270 yılına kadar direnmiştir.Moğollar başta Alamut Kalesi olmak üzere tüm kaleleri yakıp yıkmışlardır. Bölgedeki 100.000\\'i aşkın İsmailiyi Möngke Hanın emriyle katletmişlerdir. Suriye Haşhaşileri Haçlı Seferleri sırasında siyasal olaylarda önemli bir rol oynamışlardır. Raşidüddin Sinan el-İsmaili döneminde siyasal ve öğretisel olarak en parlak dönemlerini yaşamışlardır. 1273 yılında ise kalelerini Baybars\\'a teslim etmişlerdir.İsmaililerin Suriye hikayesi İran\\'a göre farklı. Burada fedailer aranırken İran\\'daki gibi Dai\\'ler halkla konuşup ikna etmiyordu. Dai\\'ler valilerle iletişimde kalıp valilerin getirdiği insanları fedai yapıyorlardı. Burada bulunan Sünni halk bu duruma sinirlenmiş ve durumun ciddiyetini anlayan Sinan elindeki İsmaililer ile önce Bahra Dağları\\'na sonrasında ise Masyaf Kalesi El-Kehf Kalesine çekilmişlerdir. İlk dönemlerinde Sünni olan Selahaddin ile savaşmışlardır ve Sinan verdiği stratejiler ile fedailerinin Selahaddin\\'in kişisel kampına kadar girip korkuttuğu ve bu düşmanlığın orada bitirildiğine inanılır. Sonrasında Selahaddin, Sinan ile anlaşma yapar ve beraber Haçlılara karşı savaşırlar. Bu sırada Sinan başarılarından dolayı ve Masyaf Kalesini sahibi olmasından \"Dağın Efendisi\" lakabını alır. Aynı zamanda Sinan\\'ın Avrupalı liderlerle anlaşmalar yapıp, suikastlar için Avrupalı liderler bir grup fedai vermiştir. En iyi örneği ise Montferratlı Conrad\\'ı öldüren fedaiyi Aslan Yürekli Richard\\'ın yönlendirdiği söylenir. 1164\\'te Kıyam-ı Kıyamet fermanına kulak asmayıp önceden davrandıkları gibi davranmaya devam etmişlerdir.Herkesin bildiği gibi kaleler yüksek, ulaşması zor ve kimi zaman Alamut Kalesi\\'nde oluğu gibi tepeye sadece tek yol bulunan yerlere yapılmıştır. Kalelerin en alt kısımlarda kuru gıdaların bulunduğu depolar vardır, onların üstünde kalede bulunan fedailerin odaları, onların üstü ise insanların beraber oturup konuştukları oturma odaları, sanatçılar ve bilim insanlarının bilimle uğraştıkları odalar olarak yapılmıştı. Bütün bu odalar tepenin içi oyularak yapılmıştır ve tepenin tam ortasına denk gelmektedir. Bu odaların kenarlarında ise kaleye saldıranları yavaşlatmak için kurulan tuzaklar, toplar ve askerlerin içinden çıkıp veya mevzilenip ok atması için büyük kapılar vardı.. Avlunun içinde ise küçük pazar alanları vardı ve burada satılan meyveler, bitkiler, sebzeler , tahıllar ve kalenin içinde bulunması gereken tüm eşyalar kalenin bulunduğu tepenin eteklerinde bulunan yaşayıp tarımı orada yapan İsmaililerden geliyordu. Kalelerde en üst odalar kalenin sahibi olan \"İmam\"lara ve bölgede faaliyet gösteren \"Dai\"lere verilmişti.En farklı özelliğe sahip kale halkın zekası sayesinde Peter Willey\\'e göre şuan büyük mühendislerin bile yapamayacağı özellikler bulunduran İran\\'ın Kuzey Doğusunda bulunan Girduh Kalesi\\'dir. Bu kalede su kanallarından su aşağıdan yukarıya doğru akmaktadır ve dağıneğik arazisinde çok verimli tarım alanları yapılmıştır. Bunların olma sebeplrinden biri de kalenin ilk imamı Muzaffer\\'in insanların temel ihtiyaçlarını gidermenin önemli olduğunu bilmesidir. Aynı şekilde kale, bulunduğu dağın şeklinden dolayı etrafı hep tuzaklarla dolu olmuştur. Bu düşünceler o kadar etkili olmuştur ki Girduh Kalesi 17 yıl Moğol istilasına dayanmıştır.Ayrıca Suriye\\'de bulunan kaleler için genel bir durum vardır. Bu kaleler İsmaililerden Moğollara, onlardan Memlüklülere, onlardan Osmanlılara kalmıştır. Moğollar kaleleri zapt ettiklerinde çoğu zaman kale yakınındaki insanları öldürmüşlerdir. Memlükler\\'de ise bu kaleler boş bırakılmış, Osmanlı döneminde ise İsmaili amirleri kaleleri padişahlarında yönetme isteğin de bulunmuş ve padişahtan gelen onaylarla yönetmişlerdir. Ancak Osmanlı hiçbir zaman o kalelere gereken önemi vermedi. Sadece El-Kehf kalesi bir amaç için kullanılmıştır ve o amaçta hapis görevi görmek olmuştur.İran\\'da kuşatmalardan kurtulan kalelerin çoğunun sanat depoları boştur çünkü kalelerin etrafında yaşayan İsmaili mezhepine bağlı bireyler oradaki eşyaları kültürlerini Dünya\\'ya göstermek için çıkarmışlardır ve küçük binaları müze yapıp oralarda göstermektedirler. Hasan Sabbah\\'ın kurduğu tarikat sıkı bir hiyerarşi ve katı kurallara dayanmaktadır. Tarikat kendi örgütlenmesini \"davet\" olarak adlandırmıştır. Tarikatın temsilcileri \"davetçiler\" anlamındaki dlerdir. Dlerin en alt kademesinde \"davete cevap veren\" anlamına gelen \"müstecip\"ler, en üst kademede ise \"delil\" manasına gelen \"hücce\" yani baş d yer almaktadır. \"Cezire\", dnin faaliyet gösterdiği bölgedir. İsmaililer de diğer mezhepler gibi din liderlerine şeyh, pir, ata gibi unvanlarla hitap eder. Tarikat mensuplarının birbirleri için kullandıkları terim ise \"yoldaş\" anlamına gelen \"refik\"tir. Sıklıkla fedai olarak bilinen suikastçılar ise tarikat tarafından esasiyun olarak adlandırılmıştır.Haşhaşiler tarihte kendilerinden önce pek görülmemiş olan bir asker taktik geliştirdiler. Özel olarak tek bir önemli kişiyi öldürmeyi temel asker taktik olarak kullanan Haşhaşiler, suikastı da kendilerince din ve psikolojik bir şekilde uygulamışlardır. Haşhaşilerce yapılan suikastların hiçbirinde ok, zehir gibi silahlar kullanılmamıştır. Neredeyse tüm suikastlarda hançer kullanılmıştır. Diğer önemli husus ise suikastı gerçekleştiren Haşhaşi\\'nin kaçmaya çalışmaması ve öldürülen kişinin korumaları veya halk tarafından linç edilmesidir. Uzmanlar bunu Haşhaşiler\\'in eylemlerine ayinsel bir hava katmak ve insanları korkutma, etkileme amacıyla bu şekilde yaptığını düşünmektedir. Haşhaşiler\\'in bu eylem biçimi Batılılar tarafından günümüzün Müslüman intihar eylemcileri ile ilişkilendirilmiştir. Ancak Orta Çağ İslam tarihi uzmanı Bernard Lewis\\'e göre Haşhaşiler\\'in kendilerini öldürmeyip korumalar tarafından öldürülmeyi beklemesinin günümüzün intihar bombacılarının kendilerini öldürmesinden kesin biçimde ayrıldığını, İslam dinine göre ikincisinin günah sayıldığını belirtmektedir. Hurfilik, Batınilik ve Ezoterizm üzerinde uzmanlaşmış Aytunç Altındal\\'a göre ise Tapınak Şövalyeleri ile Haşhaşiler arasında kendilerine has ezoterik ve batıni itikatların paylaşımında pek çok ortak husus mevcuttu.Haşhaşiler 100 üzeri suikast gerçekleştirmiş saysız devlet adamı, Sultan, din adamı ve Subayı öldürmüşlerdir. Detaylı liste için bknzHaşhaş kullanımı olayı gerçekte bilinenin aksine çok farklıdır. 1162 yılına gelindiğinde Alamut Kalesi\\'nin yeni imamı II.Hasan olmuştu ve 1164 yılında Kıym-ı Kıymet fermanını yayınlamış ve Kur-an\\'ı Kerim\\'deki çoğu farz ibadeti yalanlayıp yasaklamış ve kendi ibadetlerini oluşturmuştur. Bunlardan biri ise suikaste giden fedainin Cennet Bahçelerinde haşhaş çekip bahçelerde bakire genç kızlarla son bir eğlence yapma ibadetidir. Bakire kızlarla eğlence durumu kesinlik kazanmasa bile haşhaş kullanımında şunu biliyoruz ki 1164\\'ten sonra İran\\'da bulunan kalelerde suikastçi haşhaş kullanıp suikast bölgesine gidiyordu.Bu kanunların çıkma sebebi II.Hasan\\'ın kıyametin geldiğini düşünmesi ve Kur\\'an\\'ın etkisini yitirdiğini düşünmesidir. İranlı fanatiklerde bu kurallara harfi harfine uymuştur. Bilinmesi gereken başka bir husus ise şudur ki bu ibadetler sadece İran\\'daki kalelerde yürürlülüğe girmiştir. O zamanlarda Suriye\\'nin baş imamı olan Raşidüddin Sinan el-İsmaili bu fermanın Suriye\\'de yürürlülüğe girmesini engellemiştir çünkü II.Hasan\\'ın bunu yapma sebebinin otorite sağlamak istediğini bilmesi ve böyle ibadetlerle fedailerin etkisini düşürmemek istemesiydi. Çünkü Suriye o zamanlar 3.Haçlı Seferi\\'ne ev sahipliğine ev yapmasaydı ve herkesin birbirine yan gözle bakmasıydı. Müslümanlar sefer yapan Hristiyanlar, Tapınakçılar ve Hospitalier Şovalyeleri ile uğraşırken Selahaddin Eyyubi ise Kutsal Toprakların tek sahibi olmak için Müslümanlar ile de savaşıyordu. Bundan dolayı Sinan, fedailerini formda tutmak istiyordu.Cennet Bahçesi olarak tanımlanan bölge ise gerçekten var olan bir şeydi. Kaleler yapılırkenbüyük balkonlar yapıldı. Bu balkonlarda uzun kolonlar olur ve kolonlarda çeşit çeşit sarmaşıklar ve çiçekler bulunuyordu. Bu balkonlar yükseklerde olduklarından aşağısı gözükmüyordu ve orada bulunan insana havadaymış etkisi yaratıyordu. Bundan dolayı Cennet Bahçesi deniyordu.Haşhaşiler, Ubisoft\\'un Assassin\\'s Creed video oyunu serisine konu olmuştur. Oyun Üçüncü Haçlı Seferi yıllarında Altar Ibn-La\\'Ahad isimli suikastçının ustası Raşidüddin Sinan el-İsmaili\\'den aldığı görevlerle gerçekleştirdiği suikastları konu alır. Oyun, Haçlı Seferleri yıllarındaki Kutsal Topraklar\\'ı betimlemesi yönünden de büyük ilgi çekmiştir. Aynı zamanda Mike Newell\\'ın yönetmenliğindeki Pers Prensi: Zamanın Kumları filminde de Haşhaşiler\\'den yararlanılmıştır.']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Y75qjA_yJzU"
      },
      "source": [
        "## speechModule (child)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oyl7u5XloLy8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4a5de168-e9ba-4c94-9bac-6229a033dfba"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install simpletransformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "Building wheels for collected packages: subprocess32, pathtools, seqeval, blinker\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-py3-none-any.whl size=6502 sha256=edafa257c8c538c743626ca954d9ad2162bcd285b9e8b603a4e42e40bc0915b0\n",
            "  Stored in directory: /root/.cache/pip/wheels/50/ca/fa/8fca8d246e64f19488d07567547ddec8eb084e8c0d7a59226a\n",
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            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
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            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
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            "  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=6b027cce8df1741f240444ffb96aeb86155f3aa0dc970cf152c5641a7fd0ae23\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/f5/18/df711b66eb25b21325c132757d4314db9ac5e8dabeaf196eab\n",
            "Successfully built subprocess32 pathtools seqeval blinker\n",
            "Installing collected packages: prompt-toolkit, ipython, ipykernel, smmap, gitdb, xxhash, watchdog, validators, subprocess32, shortuuid, sentry-sdk, pydeck, pathtools, GitPython, fsspec, docker-pycreds, configparser, blinker, base58, wandb, tensorboardx, streamlit, seqeval, sentencepiece, datasets, simpletransformers\n",
            "  Attempting uninstall: prompt-toolkit\n",
            "    Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.20 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.2.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.26.0 which is incompatible.\u001b[0m\n",
            "Successfully installed GitPython-3.1.18 base58-2.1.0 blinker-1.4 configparser-5.0.2 datasets-1.11.0 docker-pycreds-0.4.0 fsspec-2021.7.0 gitdb-4.0.7 ipykernel-6.2.0 ipython-7.26.0 pathtools-0.1.2 prompt-toolkit-3.0.20 pydeck-0.6.2 sentencepiece-0.1.96 sentry-sdk-1.3.1 seqeval-1.2.2 shortuuid-1.0.1 simpletransformers-0.61.13 smmap-4.0.0 streamlit-0.87.0 subprocess32-3.5.4 tensorboardx-2.4 validators-0.18.2 wandb-0.12.0 watchdog-2.1.5 xxhash-2.0.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "ipykernel",
                  "prompt_toolkit"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gE3Tob9bn9oS"
      },
      "source": [
        "from os import path\n",
        "from IPython.display import Audio\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, Wav2Vec2Processor, Wav2Vec2ForCTC\n",
        "import librosa\n",
        "import torch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bSYGbxLixz7"
      },
      "source": [
        "class speechModule(CombineInator):\n",
        "\n",
        "  def __init__(self):\n",
        "    self.SAMPLING_RATE = 16_000\n",
        "    self.git_repo_url = 'https://github.com/CorentinJ/Real-Time-Voice-Cloning.git'\n",
        "    self.project_name = splitext(basename(self.git_repo_url))[0]\n",
        "    \n",
        "  def get_repo(self):\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    Metinin sese çevrilmesi sırasında kullanılacak ses klonlama kütüphanesini çeker.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if not exists(self.project_name):\n",
        "      # clone and install\n",
        "      !git clone -q --recursive {self.git_repo_url}\n",
        "      # install dependencies\n",
        "      !cd {self.project_name} && pip install -q -r requirements.txt\n",
        "      !pip install -q gdown\n",
        "      !apt-get install -qq libportaudio2\n",
        "      !pip install -q https://github.com/tugstugi/dl-colab-notebooks/archive/colab_utils.zip\n",
        "      # download pretrained model\n",
        "      !cd {self.project_name} && wget https://github.com/blue-fish/Real-Time-Voice-Cloning/releases/download/v1.0/pretrained.zip && unzip -o pretrained.zip\n",
        "    from sys import path as syspath\n",
        "    syspath.append(self.project_name)\n",
        "  \n",
        "  def wav2vec_model(self, source):\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    Sesin metne çevrilmesi sırasında kullanılacak ilgili dile göre wav2vec modelini belirler.\n",
        "\n",
        "    :param source: ses dosyası dili (\"tr\" / \"en\")\n",
        "    :type source: str\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    processor = None\n",
        "    model = None\n",
        "\n",
        "    if source == \"en\":\n",
        "      processor = Wav2Vec2Processor.from_pretrained(\"facebook/wav2vec2-large-960h\")\n",
        "      model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-large-960h\")\n",
        "      \n",
        "    elif source ==\"tr\":\n",
        "      processor = Wav2Vec2Processor.from_pretrained(\"m3hrdadfi/wav2vec2-large-xlsr-turkish\")\n",
        "      model = Wav2Vec2ForCTC.from_pretrained(\"m3hrdadfi/wav2vec2-large-xlsr-turkish\")  \n",
        "\n",
        "    return model, processor\n",
        "\n",
        "    \n",
        "  def speech2text(self, audio_file, model, processor, language):\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    Girdi olarak verilen sesi metne çevirir.\n",
        "\n",
        "    :param audio_file: ses dosyasının yer aldığı dizin\n",
        "    type audio_file: str\n",
        "    \n",
        "    :param model: sesin metne çevrilmesi esnasında kullanılacak huggingface kütüphanesinden çekilen model\n",
        "\n",
        "    :param processor: sesin metne çevrilmesi esnasında kullanılacak huggingface kütüphanesinden çekilen istemci\n",
        "\n",
        "    :param language: girdi olarak verilen ses doyasının dili (\"tr\" / \"en\")\n",
        "    :type language: str\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    #load any audio file of your choice\n",
        "    speech, rate = librosa.load(audio_file, sr=self.SAMPLING_RATE)\n",
        "    input_values = processor(speech, sampling_rate=self.SAMPLING_RATE, return_tensors = 'pt').input_values\n",
        "\n",
        "    #Store logits (non-normalized predictions)\n",
        "    logits = model(input_values).logits\n",
        "\n",
        "    #Store predicted id's\n",
        "    predicted_ids = torch.argmax(logits, dim =-1)\n",
        "\n",
        "    #decode the audio to generate text\n",
        "    response = processor.decode(predicted_ids[0]).lower()\n",
        "\n",
        "    if language == \"en\":\n",
        "      response = \">>tur<< \" + response\n",
        "\n",
        "    return response\n",
        "  \n",
        "  \n",
        "  def text2speech(self, audio, translation):\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    Metini sese çevirir.\n",
        "\n",
        "    :param audio: klonlanacak ses doyasının yer aldığı dizin\n",
        "    :type audio: str\n",
        "\n",
        "    :param translation: çevirisi yapılmış metin\n",
        "    :type translation: str\n",
        "\n",
        "    \"\"\"\n",
        "    from numpy import pad as pad\n",
        "    from synthesizer.inference import Synthesizer\n",
        "    from encoder import inference as encoder\n",
        "    from vocoder import inference as vocoder\n",
        "    from pathlib import Path\n",
        "\n",
        "    encoder.load_model(self.project_name / Path(\"encoder/saved_models/pretrained.pt\"))\n",
        "    synthesizer = Synthesizer(self.project_name / Path(\"synthesizer/saved_models/pretrained/pretrained.pt\"))\n",
        "    vocoder.load_model(self.project_name / Path(\"vocoder/saved_models/pretrained/pretrained.pt\"))\n",
        "\n",
        "    embedding = encoder.embed_utterance(encoder.preprocess_wav(audio, self.SAMPLING_RATE))\n",
        "    specs = synthesizer.synthesize_spectrograms([translation], [embedding])\n",
        "    generated_wav = vocoder.infer_waveform(specs[0])\n",
        "    generated_wav = pad(generated_wav, (0, self.SAMPLING_RATE), mode=\"constant\")\n",
        "\n",
        "    return Audio(generated_wav, rate=self.SAMPLING_RATE, autoplay=True)\n",
        "\n",
        "  \n",
        "\n",
        "\n",
        "  \n",
        "  def speech2text2trans2speech(self, filename:str, source_lang:str, output_type:str = \"text\"):\n",
        "    \n",
        "    \"\"\"\n",
        "\n",
        "    Aldığı ses dosyasını text'e dönüştürüp, hedeflenen dile çeviren ve çevirdiği metni ses olarak\n",
        "    döndüren fonksiyon.\n",
        "  \n",
        "    :param filename: Ses dosyasının adı\n",
        "    :type filename: str\n",
        "\n",
        "    :param lang: Ses dosyası dili (\"en\"/\"tr\") \n",
        "    :type lang: str \n",
        "    \n",
        "    \"\"\"\n",
        "\n",
        "    output_types = [\"text\", \"speech\"]\n",
        "    source_languages = [\"en\", \"tr\"]\n",
        "\n",
        "    if source_lang not in source_languages:\n",
        "      print(\"Kaynak dil olarak yalnızca 'en' ve 'tr' parametreleri kullanılabilir.\")\n",
        "      return None\n",
        "    \n",
        "    if output_type not in output_types:\n",
        "      print(\"Çıkış türü için yalnızca 'text' ve 'speech' parametreleri desteklenmektedir.\")\n",
        "      return None\n",
        "\n",
        "    if source_lang == \"en\" and output_type==\"speech\":\n",
        "      print(\"Üzgünüz, text2speech modülümüzde Türkçe dil desteği bulunmamaktadır.\\n\")\n",
        "      return None\n",
        "\n",
        "    model_trs, tokenizer_trs, pipe_trs = CombineInator.translate_model(self, source_lang)\n",
        "\n",
        "    model_s2t, processor_s2t = self.wav2vec_model(source_lang)\n",
        "    \n",
        "    input_text = self.speech2text(filename, model_s2t, processor_s2t, source_lang)\n",
        "    print(input_text)\n",
        "    translation = CombineInator.translate(self, pipe_trs, model_trs, tokenizer_trs, input_text)\n",
        "  \n",
        "    if output_type == \"text\":\n",
        "      return translation\n",
        "  \n",
        "    else:\n",
        "      print(\"\\n\" + translation + \"\\n\")\n",
        "      return self.text2speech(filename, translation)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ft9Mfg63y0WB"
      },
      "source": [
        "### Örnek kullanım"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoGWApCypG1j"
      },
      "source": [
        "filename = \"_path_to_wav_file\" # ses dosyası pathi verilmelidir"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZ71cLW4UvEf"
      },
      "source": [
        "speechM = speechModule()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncVgqA0_pVJb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c50f934f-a898-4125-b767-92dd15bc9929"
      },
      "source": [
        "speechM.get_repo()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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            "2021-08-26 18:25:52 (29.3 MB/s) - ‘pretrained.zip’ saved [397171814/397171814]\n",
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      },
      "source": [
        "speechM.speech2text2trans2speech(filename, \"tr\", \"speech\")"
      ],
      "execution_count": null,
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          "output_type": "stream",
          "text": [
            "merhava yarın partiye gelecek misiniz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n",
            "To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at  /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
            "  return torch.floor_divide(self, other)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Will you come to the party tomorrow?\n",
            "\n",
            "Loaded encoder \"pretrained.pt\" trained to step 1564501\n",
            "Synthesizer using device: cuda\n",
            "Building Wave-RNN\n",
            "Trainable Parameters: 4.481M\n",
            "Loading model weights at Real-Time-Voice-Cloning/vocoder/saved_models/pretrained/pretrained.pt\n",
            "Trainable Parameters: 30.870M\n",
            "Loaded synthesizer \"pretrained.pt\" trained to step 295000\n",
            "+----------+---+\n",
            "| Tacotron | r |\n",
            "+----------+---+\n",
            "|   295k   | 2 |\n",
            "+----------+---+\n",
            " \n",
            "\n",
            "| Generating 1/1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:652: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Done.\n",
            "\n",
            "{| ████████████████ 38000/38400 | Batch Size: 4 | Gen Rate: 3.1kHz | }"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMd4r71Uyo4C"
      },
      "source": [
        "## Lxmert (child)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wI4R0Evhxq07"
      },
      "source": [
        "!git clone https://github.com/hila-chefer/Transformer-MM-Explainability\n",
        "\n",
        "import os\n",
        "os.chdir(f'./Transformer-MM-Explainability')\n",
        "\n",
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rgz64vj4Yww"
      },
      "source": [
        "%cd Transformer-MM-Explainability"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftI7jafQxtLm"
      },
      "source": [
        "from lxmert.lxmert.src.modeling_frcnn import GeneralizedRCNN\n",
        "import lxmert.lxmert.src.vqa_utils as utils\n",
        "from lxmert.lxmert.src.processing_image import Preprocess\n",
        "from transformers import LxmertTokenizer\n",
        "from lxmert.lxmert.src.huggingface_lxmert import LxmertForQuestionAnswering\n",
        "from lxmert.lxmert.src.lxmert_lrp import LxmertForQuestionAnswering as LxmertForQuestionAnsweringLRP\n",
        "from tqdm import tqdm\n",
        "from lxmert.lxmert.src.ExplanationGenerator import GeneratorOurs, GeneratorBaselines, GeneratorOursAblationNoAggregation\n",
        "import random\n",
        "import numpy as np\n",
        "import cv2\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import torchvision.transforms as transforms\n",
        "from captum.attr import visualization\n",
        "import requests"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OXpPrhZxfr5"
      },
      "source": [
        "class Lxmert(CombineInator):\n",
        "\n",
        "  def __init__(self):\n",
        "    self.OBJ_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/objects_vocab.txt\"\n",
        "    self.ATTR_URL = \"https://raw.githubusercontent.com/airsplay/py-bottom-up-attention/master/demo/data/genome/1600-400-20/attributes_vocab.txt\"\n",
        "    self.VQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json\"\n",
        "    self.model_lrp = self.ModelUsage()\n",
        "    self.lrp = GeneratorOurs(self.model_lrp)\n",
        "    self.baselines = GeneratorBaselines(self.model_lrp)\n",
        "    self.vqa_answers = utils.get_data(self.VQA_URL)\n",
        "    \n",
        "  class ModelUsage:\n",
        "    \n",
        "    \"\"\"\n",
        "\n",
        "    Model kullanımı için sınıf yapısı\n",
        "\n",
        "    \"\"\"\n",
        "    \n",
        "    def __init__(self, use_lrp=True):\n",
        "      self.VQA_URL = \"https://raw.githubusercontent.com/airsplay/lxmert/master/data/vqa/trainval_label2ans.json\"\n",
        "\n",
        "      self.vqa_answers = utils.get_data(self.VQA_URL)\n",
        "\n",
        "      # load models and model components\n",
        "      self.frcnn_cfg = utils.Config.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\")\n",
        "      self.frcnn_cfg.MODEL.DEVICE = \"cuda\"\n",
        "\n",
        "      self.frcnn = GeneralizedRCNN.from_pretrained(\"unc-nlp/frcnn-vg-finetuned\", config=self.frcnn_cfg)\n",
        "\n",
        "      self.image_preprocess = Preprocess(self.frcnn_cfg)\n",
        "\n",
        "      self.lxmert_tokenizer = LxmertTokenizer.from_pretrained(\"unc-nlp/lxmert-base-uncased\")\n",
        "\n",
        "      if use_lrp:\n",
        "          self.lxmert_vqa = LxmertForQuestionAnsweringLRP.from_pretrained(\"unc-nlp/lxmert-vqa-uncased\").to(\"cuda\")\n",
        "      else:\n",
        "          self.lxmert_vqa = LxmertForQuestionAnswering.from_pretrained(\"unc-nlp/lxmert-vqa-uncased\").to(\"cuda\")\n",
        "\n",
        "      self.lxmert_vqa.eval()\n",
        "      self.model = self.lxmert_vqa\n",
        "\n",
        "      # self.vqa_dataset = vqa_data.VQADataset(splits=\"valid\")\n",
        "\n",
        "    def forward(self, item):\n",
        "      \n",
        "      PATH, question = item\n",
        "      \n",
        "      self.image_file_path = PATH\n",
        "\n",
        "      # run frcnn\n",
        "      images, sizes, scales_yx = self.image_preprocess(PATH)\n",
        "      output_dict = self.frcnn(\n",
        "          images,\n",
        "          sizes,\n",
        "          scales_yx=scales_yx,\n",
        "          padding=\"max_detections\",\n",
        "          max_detections= self.frcnn_cfg.max_detections,\n",
        "          return_tensors=\"pt\"\n",
        "      )\n",
        "      inputs = self.lxmert_tokenizer(\n",
        "          question,\n",
        "          truncation=True,\n",
        "          return_token_type_ids=True,\n",
        "          return_attention_mask=True,\n",
        "          add_special_tokens=True,\n",
        "          return_tensors=\"pt\"\n",
        "      )\n",
        "      self.question_tokens = self.lxmert_tokenizer.convert_ids_to_tokens(inputs.input_ids.flatten())\n",
        "      self.text_len = len(self.question_tokens)\n",
        "      # Very important that the boxes are normalized\n",
        "      normalized_boxes = output_dict.get(\"normalized_boxes\")\n",
        "      features = output_dict.get(\"roi_features\")\n",
        "      self.image_boxes_len = features.shape[1]\n",
        "      self.bboxes = output_dict.get(\"boxes\")\n",
        "      self.output = self.lxmert_vqa(\n",
        "          input_ids=inputs.input_ids.to(\"cuda\"),\n",
        "          attention_mask=inputs.attention_mask.to(\"cuda\"),\n",
        "          visual_feats=features.to(\"cuda\"),\n",
        "          visual_pos=normalized_boxes.to(\"cuda\"),\n",
        "          token_type_ids=inputs.token_type_ids.to(\"cuda\"),\n",
        "          return_dict=True,\n",
        "          output_attentions=False,\n",
        "      )\n",
        "      return self.output\n",
        "  \n",
        "  def ceviri(self, text: str, lang_src='tr'):\n",
        "    \n",
        "    \"\"\"\n",
        "    \n",
        "    Aldığı metni istenilen dile çeviren fonksiyon.\n",
        "\n",
        "    :param text: Orjinal metin\n",
        "    :type text: str\n",
        "\n",
        "    :param lang_src: Metin dosyasının dili (kaynak dili)\n",
        "    :type lang_src: str\n",
        "    \n",
        "    :param lang_tgt: Çevrilecek dil (hedef dil)\n",
        "    :param lang_tgt: str\n",
        "\n",
        "    :return: translated text\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if lang_src == \"en\":\n",
        "      text = \">>tur<< \" + text\n",
        "\n",
        "    model, tokenizer, pipeline = CombineInator.translate_model(self, lang_src)\n",
        "    return (CombineInator.translate(self, pipeline, model, tokenizer, text))\n",
        "  \n",
        "  \n",
        "  def save_image_vis(self, image_file_path, bbox_scores):\n",
        "    \"\"\"\n",
        "    Resim üzerinde sorunun cevabını çizer ve kaydeder.\n",
        "\n",
        "    :param image_file_path: imgenin yer aldığı dizin\n",
        "    :type image_file_path: str\n",
        "\n",
        "    :param bbox_scores: tespit edilen nesnelerin skorlarını içeren tensor\n",
        "    :type bbox_scores: tensor\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "    _, top_bboxes_indices = bbox_scores.topk(k=1, dim=-1)\n",
        "    img = cv2.imread(image_file_path)\n",
        "    mask = torch.zeros(img.shape[0], img.shape[1])\n",
        "    for index in range(len(bbox_scores)):\n",
        "      [x, y, w, h] = self.model_lrp.bboxes[0][index]\n",
        "      curr_score_tensor = mask[int(y):int(h), int(x):int(w)]\n",
        "      new_score_tensor = torch.ones_like(curr_score_tensor)*bbox_scores[index].item()\n",
        "      mask[int(y):int(h), int(x):int(w)] = torch.max(new_score_tensor,mask[int(y):int(h), int(x):int(w)])\n",
        "    mask = (mask - mask.min()) / (mask.max() - mask.min())\n",
        "    mask = mask.unsqueeze_(-1)\n",
        "    mask = mask.expand(img.shape)\n",
        "    img = img * mask.cpu().data.numpy()\n",
        "    cv2.imwrite('lxmert/lxmert/experiments/paper/new.jpg', img)\n",
        "  \n",
        "  \n",
        "  \n",
        "  def get_image_and_question(self, img_path:str, soru:str):\n",
        "    \n",
        "    \"\"\"\n",
        "\n",
        "    Input olarak verilen imge ve soruyu döndürür.\n",
        "\n",
        "    :param img_path: Soru sorulacak imgenin path bilgisi\n",
        "    :type img_path: str\n",
        "\n",
        "    :param soru: Resim özelinde modele sorulacak olan Türkçe soru \n",
        "    :type soru: str\n",
        "\n",
        "    :return: image_scores, text_scores\n",
        "\n",
        "    \"\"\"\n",
        "    \n",
        "    ing_soru = self.ceviri(soru, \"tr\")\n",
        "    R_t_t, R_t_i = self.lrp.generate_ours((img_path, ing_soru), use_lrp=False, normalize_self_attention=True, method_name=\"ours\")\n",
        "    \n",
        "    return R_t_i[0], R_t_t[0]  \n",
        "\n",
        "  \n",
        "  def resim_uzerinden_soru_cevap(self, PATH:str, turkce_soru:str):\n",
        "    \"\"\"\n",
        "    Verilen girdi imgesi üzerinden yine veriler sorular ile sorgulama yapılabilmesini\n",
        "    sağlar. \n",
        "\n",
        "    PATH: imgenin path bilgisi\n",
        "    turkce_soru: Resimde cevabı aranacak soru\n",
        "    \"\"\"\n",
        "\n",
        "    #Eğer sorgulanacak resim local'de yok ve internet üzerinden bir resim ise:\n",
        "    if PATH.startswith(\"http\"):\n",
        "      im = Image.open(requests.get(PATH, stream=True).raw)\n",
        "      im.save('lxmert/lxmert/experiments/paper/online_image.jpg', 'JPEG')\n",
        "      PATH = 'lxmert/lxmert/experiments/paper/online_image.jpg'\n",
        "\n",
        "\n",
        "    image_scores, text_scores = self.get_image_and_question(PATH, turkce_soru)\n",
        "    self.save_image_vis(PATH, image_scores)\n",
        "    orig_image = Image.open(self.model_lrp.image_file_path)\n",
        "\n",
        "    fig, axs = plt.subplots(ncols=2, figsize=(20, 5))\n",
        "    axs[0].imshow(orig_image);\n",
        "    axs[0].axis('off');\n",
        "    axs[0].set_title('original');\n",
        "\n",
        "    masked_image = Image.open('lxmert/lxmert/experiments/paper/new.jpg')\n",
        "    axs[1].imshow(masked_image);\n",
        "    axs[1].axis('off');\n",
        "    axs[1].set_title('masked');\n",
        "\n",
        "    text_scores = (text_scores - text_scores.min()) / (text_scores.max() - text_scores.min())\n",
        "    vis_data_records = [visualization.VisualizationDataRecord(text_scores,0,0,0,0,0,self.model_lrp.question_tokens,1)]\n",
        "    visualization.visualize_text(vis_data_records)\n",
        "    cevap = self.ceviri(self.vqa_answers[self.model_lrp.output.question_answering_score.argmax()], lang_src='en')\n",
        "    print(\"ANSWER:\", cevap)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-7wMbJBysnZ"
      },
      "source": [
        "### Örnek kullanım"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "O9vNLEea0akI",
        "outputId": "018fc19f-a6a3-4780-87c3-642ee8e92282"
      },
      "source": [
        "lxmert = Lxmert()\n",
        "\n",
        "PATH = '_path_to_jpg_' # jpg dosyası pathi verilmelidir\n",
        "turkce_soru = 'Resimde neler var'\n",
        "\n",
        "lxmert.resim_uzerinden_soru_cevap(PATH, turkce_soru)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading configuration file cache\n",
            "loading weights file https://cdn.huggingface.co/unc-nlp/frcnn-vg-finetuned/pytorch_model.bin from cache at /root/.cache/torch/transformers/57f6df6abe353be2773f2700159c65615babf39ab5b48114d2b49267672ae10f.77b59256a4cf8343ae0f923246a81489fc8d82f98d082edc2d2037c977c0d9d0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py:434: UserWarning: Setting attributes on ParameterList is not supported.\n",
            "  warnings.warn(\"Setting attributes on ParameterList is not supported.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "All model checkpoint weights were used when initializing GeneralizedRCNN.\n",
            "\n",
            "All the weights of GeneralizedRCNN were initialized from the model checkpoint at unc-nlp/frcnn-vg-finetuned.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use GeneralizedRCNN for predictions without further training.\n",
            "tensor(0.9380, device='cuda:0')\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table width: 100%><div style=\"border-top: 1px solid; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>0 (0.00)</b></text></td><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>0.00</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 50%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> what                    </font></mark><mark style=\"background-color: hsl(120, 75%, 87%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> '                    </font></mark><mark style=\"background-color: hsl(120, 75%, 83%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> s                    </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> in                    </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> the                    </font></mark><mark style=\"background-color: hsl(120, 75%, 82%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> picture                    </font></mark><mark style=\"background-color: hsl(120, 75%, 87%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ?                    </font></mark><mark style=\"background-color: hsl(120, 75%, 87%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [SEP]                    </font></mark></td><tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "ANSWER: laptop\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aza9_OfQZjue"
      },
      "source": [
        "## Web Arayüz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fimfoXqCZjW-",
        "outputId": "01d4a292-8e2f-4dda-9d35-28e5f8edf1d0"
      },
      "source": [
        "!pip install flask-ngrok"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting flask-ngrok\n",
            "  Downloading flask_ngrok-0.0.25-py3-none-any.whl (3.1 kB)\n",
            "Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.7/dist-packages (from flask-ngrok) (1.1.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from flask-ngrok) (2.23.0)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (2.11.3)\n",
            "Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (7.1.2)\n",
            "Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (1.1.0)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (1.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=0.8->flask-ngrok) (2.0.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2.10)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ad3R6OrvZ6k-"
      },
      "source": [
        "from flask import Flask, redirect, url_for, render_template, request, flash\n",
        "from flask_ngrok import run_with_ngrok"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Db1DlvV5Z9vV"
      },
      "source": [
        "# Burada web_dependencies klasörü içerisinde bulunan klasörlerin pathi verilmelidir.\n",
        "template_folder = '_path_to_templates_folder_'\n",
        "static_folder = '_path_to_static_folder_'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5kFGIRzZ_6M",
        "outputId": "f302725c-4c8f-47d6-cfd0-738858bc6c6e"
      },
      "source": [
        "app = Flask(__name__, template_folder=template_folder, static_folder=static_folder)\n",
        "run_with_ngrok(app)  # Start ngrok when app is run\n",
        "\n",
        "@app.route(\"/\", methods=['GET', 'POST'])\n",
        "def home():\n",
        "  if request.method == 'POST':\n",
        "    konu = request.form[\"topic\"]\n",
        "    library.categorical_scraper(konu, PATH, 20, text_into_sentences_param=False)\n",
        "    \n",
        "  return render_template(\"index.html\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "\n",
        "\t#app.debug = True\n",
        "\tapp.run()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://7c1c-35-229-254-225.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/mobirise/css/mbr-additional.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/web/assets/mobirise-icons2/mobirise2.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/web/assets/mobirise-icons-bold/mobirise-icons-bold.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/bootstrap/css/bootstrap.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/web/assets/mobirise-icons/mobirise-icons.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:08] \"\u001b[37mGET /static/assets/bootstrap/css/bootstrap-grid.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/bootstrap/css/bootstrap-reboot.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/dropdown/css/style.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/socicon/css/styles.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/images/ouzhan-460x460.jpg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/theme/css/style.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:09] \"\u001b[37mGET /static/assets/images/secilay-696x1237.jpeg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:11] \"\u001b[37mGET /static/assets/images/combine-inator-1000x667.jpg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:11] \"\u001b[37mGET /static/assets/socicon/fonts/socicon.woff2 HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:11] \"\u001b[37mGET /static/assets/web/assets/mobirise-icons-bold/mobirise-icons-bold.ttf?m1l4yr HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:11] \"\u001b[37mGET /static/assets/web/assets/mobirise-icons2/mobirise2.ttf?f2bix4 HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [26/Aug/2021 19:16:12] \"\u001b[37mGET /static/assets/images/436-4367231-phineas-and-ferb-picture-dr-heinz-doofenshmirtz-hd-96x161.png HTTP/1.1\u001b[0m\" 200 -\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}