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      "cell_type": "markdown",
      "metadata": {
        "id": "lV5vJNlkfQ_X",
        "colab_type": "text"
      },
      "source": [
        "#Librerias y diccicionarios\n",
        "\n"
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "acGT5rIeSeGx",
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          "height": 666
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        "outputId": "0d489ab2-1807-447f-f3be-8086afe76db4"
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      "source": [
        " # Importa/Instala y Boletas\n",
        "!pip install git+https://github.com/huggingface/transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-g1wpsn9g\n",
            "  Running command git clone -q https://github.com/huggingface/transformers /tmp/pip-req-build-g1wpsn9g\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (1.18.5)\n",
            "Collecting tokenizers==0.8.0-rc4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e8/bd/e5abec46af977c8a1375c1dca7cb1e5b3ec392ef279067af7f6bc50491a0/tokenizers-0.8.0rc4-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 15.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (20.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (2019.12.20)\n",
            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 49.5MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 53.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from transformers==3.0.1) (0.7)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.0.1) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==3.0.1) (1.12.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.1) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.1) (2.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.1) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==3.0.1) (3.0.4)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.0.1) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==3.0.1) (0.15.1)\n",
            "Building wheels for collected packages: transformers, sacremoses\n",
            "  Building wheel for transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-3.0.1-cp36-none-any.whl size=757375 sha256=9477e322e88bcaeb623674a206fe2f99ab47833f06243ae40f8e6be7d6501db8\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ophrbr4n/wheels/70/d3/52/b3fa4f8b8ef04167ac62e5bb2accb62ae764db2a378247490e\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=3dea75e2c7861f643d19e7cc2d402497359f5c8b3316857eb71254fae60cdd5e\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built transformers sacremoses\n",
            "Installing collected packages: tokenizers, sentencepiece, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.0rc4 transformers-3.0.1\n"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rN45KAAfam7",
        "colab_type": "text"
      },
      "source": [
        "###Modelo GTP-2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWQMw7LfSnWy",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "import torch\n",
        "from transformers import *\n",
        "\n",
        "# Estructura en tuplas para los modelos, con su tokenizer y sus preentrenados y Dj\n",
        "MODELS = [(GPT2LMHeadModel, GPT2Tokenizer , 'gpt2-large')]\n",
        "tokenizer = [] # esto no se instancia, para poder cambiarlo si cambiamos de modelo eventualmente\n",
        "model = []\n",
        "\n",
        "# Main y Entrada\n",
        "def main():\n",
        "  '''Metodo main, donde se incializan los modelos'''\n",
        "  for model_class, tokenizer_class, pretrained_weights in MODELS: #se trae la tupla de modelos\n",
        "      # Carga el preentrenado con el modelo/tokenizer\n",
        "      tok = tokenizer_class.from_pretrained(pretrained_weights)\n",
        "      tokenizer.append(tok) # añade el tokenizer a la lista tokenizer\n",
        "      mod = model_class.from_pretrained(pretrained_weights, pad_token_id=tokenizer[-1].eos_token_id)\n",
        "      model.append(mod) # añade el modelo a la lista model"
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              "model_id": "6c9cbdc245eb4324a2958abcaf462b4d",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3247202234.0, style=ProgressStyle(descr…"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at gpt2-large and are newly initialized: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias', 'h.12.attn.masked_bias', 'h.13.attn.masked_bias', 'h.14.attn.masked_bias', 'h.15.attn.masked_bias', 'h.16.attn.masked_bias', 'h.17.attn.masked_bias', 'h.18.attn.masked_bias', 'h.19.attn.masked_bias', 'h.20.attn.masked_bias', 'h.21.attn.masked_bias', 'h.22.attn.masked_bias', 'h.23.attn.masked_bias', 'h.24.attn.masked_bias', 'h.25.attn.masked_bias', 'h.26.attn.masked_bias', 'h.27.attn.masked_bias', 'h.28.attn.masked_bias', 'h.29.attn.masked_bias', 'h.30.attn.masked_bias', 'h.31.attn.masked_bias', 'h.32.attn.masked_bias', 'h.33.attn.masked_bias', 'h.34.attn.masked_bias', 'h.35.attn.masked_bias', 'lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCVhSiykfg_l",
        "colab_type": "text"
      },
      "source": [
        "###Diccionarios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfcNVJYbSnYP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# (Nada) Importa y Tablero de Anuncios\n",
        "import random\n",
        " \n",
        "# Lista de-generos y Clasificacion\n",
        "GENEROS = [\"Aventura\", \"Comedia\", \"Fantasia\", \"Terror\", \"Acción\", \"Thriller\", \"Romance\",\n",
        "           \"Ciencia Ficción\", \"Drama\", \"Misterio\"]\n",
        " \n",
        "# Diccionario de personajes por genero y Teloneros\n",
        "PERSONAJES = {\"Aventura\":[\"Zelda\", \"Samus\", \"Arthur Pendragon\", \"Arthur Eld\"],\n",
        "              \"Comedia\":[\"Groucho\", \"Groucho Marx\", \"Dandelo\", \"Robin Williams\",\n",
        "                         \"Abigail\"],\n",
        "              \"Fantasia\":[\"Link\", \"Navi\"],\n",
        "              \"Terror\":[\"Pennywise\", \"Jack Torrence\"],\n",
        "              \"Acción\":[\"Rambo\", \"Jacki Chan\"],\n",
        "              \"Thriller\":[\"Hannibal Lecter\", \"Jack Torrence\"],\n",
        "              \"Romance\":[\"Julieta\", \"Jack Dawson\", \"Abigail\"],\n",
        "              \"Ciencia Ficción\":[\"Rick Sanchez\", \"Doc Brown\"],\n",
        "              \"Drama\":[\"Dolores Claiborne\", \"Andy Dufresne\", \"Abigail\"],\n",
        "              \"Misterio\":[\"Sherlock Holmes\", \"Abigail\"]}\n",
        "\n",
        "# Diccionario de locaciones por genero y Gira\n",
        "LUGARES = {\"NN\":[\"the Opera\", \"the Races\", \"home\", \"his flat\", \"my roomie's job-place\",\n",
        "                 \"college\"],\n",
        "           \"Aventura\":[\"Lethal Lava Land\", \"seaward\", \"The Colosseum\", \"seaward\"],\n",
        "           \"Comedia\":[\"my mother-in-law's house\"],\n",
        "           \"Fantasia\":[\"Kindom\", \"Rhye\", \"Elfland\"],\n",
        "           \"Terror\":[\"Innsmouth\", \"R'lyeh\"],\n",
        "           \"Acción\":[\"The White House\"],\n",
        "           \"Thriller\":[\"abandoned house\"],\n",
        "           \"Romance\":[\"Roma\"],\n",
        "           \"Ciencia Ficción\":[\"spaceship\"],\n",
        "           \"Drama\":[\"elderhouse\"],\n",
        "           \"Misterio\":[\"haunted house\"]}\n",
        "\n",
        "# Diccionario de tiempos por genero y Numero\n",
        "# notese que estan en minuscula\n",
        "TIEMPOS = {\"NN\":[\"meanwhile\", \"at\", \"at night\", \"yesterday\", \"tonight\", \"before\"],\n",
        "           \"Aventura\":[\"once upon a time\", \"during his reign\"],\n",
        "           \"Comedia\":[\"in my youth\", \"before I married\", \"before he married\",\n",
        "                      \"before she married\"],\n",
        "           \"Fantasia\":[\"before elf revolution\", \"previously\"],\n",
        "           \"Terror\":[\"suddenly\"],\n",
        "           \"Acción\":[\"meanwhile\"],\n",
        "           \"Thriller\":[\"time Aao\"],\n",
        "           \"Romance\":[\"in my youth\",],\n",
        "           \"Ciencia Ficción\":[\"3M years over\"],\n",
        "           \"Drama\":[\"before\"],\n",
        "           \"Misterio\":[\"at a stormy night\"]}\n",
        "\n",
        "# Diccionario de acciones/verbos por genero e Improvisaciones\n",
        "# notese que estan en minuscula\n",
        "ACCIONES = {\"NN\":[\"eat\", \"drink\", \"walk\", \"talk\", \"think\", \"think of\", \"think about\",\n",
        "                  \"run\", \"feel\", \"do\", \"go to\", \"go\"],\n",
        "            \"Aventura\":[\"fight\", \"sail\", \"search\", \"trip\"],\n",
        "            \"Comedia\":[\"say\", \"punch\", \"be racist\", \"say jerk\"],\n",
        "            \"Fantasia\":[\"fly\", \"hunt\", \"travel\", \"trip\", \"fight\", \"search\", \"found\",\n",
        "                        \"dream\", \"scream\"],\n",
        "            \"Terror\":[\"scream\", \"run\", \"run away\", \"yell\", \"flee\", \"shout\",\n",
        "                      \"dream\", \"have a nightmare\", \"have a dreamscape\", \"have a hallucination\",\n",
        "                      \"feel\"],\n",
        "            \"Acción\":[\"fight\"],\n",
        "            \"Thriller\":[\"think\"],\n",
        "            \"Romance\":[\"fight\"],\n",
        "            \"Ciencia Ficción\":[\"fight\"],\n",
        "            \"Drama\":[\"cry\"],\n",
        "            \"Misterio\":[\"found\"]}\n",
        "\n",
        "# Diccionario de eventos por genero y Estilo Propio\n",
        "# notese que estan en minuscula\n",
        "EVENTOS = {\"NN\":[\"pee\"],\n",
        "           \"Aventura\":[\"seeking\"],\n",
        "           \"Comedia\":[\"pantomime\", \"mummery\"],\n",
        "           \"Fantasia\":[\"invasion\", \"drought\", \"oddesy\", \"elf invasion\"],\n",
        "           \"Terror\":[\"pandemic\", \"invasion\"],\n",
        "           \"Acción\":[\"gunfire\"],\n",
        "           \"Thriller\":[\"pandemic\"],\n",
        "           \"Romance\":[\"marriage\", \"date\", \"death\"],\n",
        "           \"Ciencia Ficción\":[\"pandemic\", \"oddesy\", \"interstellar trip\", \"invasion\"],\n",
        "           \"Drama\":[\"divorce\"],\n",
        "           \"Misterio\":[\"call the police\", \"questioning\", \"hiding\", \"seeking\"]}\n",
        " \n",
        "# Diccionario de eventos por genero y Playback\n",
        "# notese que estan en minuscula\n",
        "CLAVES = {\"NN\":[\"[personaje] is a\", \"[personaje] in [lugar] then\",\n",
        "                \"[personaje] in [lugar] then [evento]\", \"finally [evento]\",\n",
        "                \"finally after [lugar] now\", \"at last [personaje]\",\n",
        "                \"so [personaje] [accion]\", \"[lugar] is a\", \"[tiempo] is a\",\n",
        "                \"so [personaje]\", \"[personaje] then\", \"at last [personaje] in [evento]\",\n",
        "                \"at last [personaje] in [lugar]\", \"[personaje] in [lugar] then [evento]\",\n",
        "                \"[personaje] speak his mind\", \"exit [personaje]\", \"enter [personaje]\",\n",
        "                \"finally [personaje] [accion] in [lugar]\", \"[personaje] was [accion], however\",\n",
        "                \"However [personaje]\", \"and then [personaje]\", \"and then [lugar]\",\n",
        "                \"[personaje] suddenly\", \"[lugar] suddenly\", \"almost [personaje]\",\n",
        "                \"[personaje] has been working on\", \"[personaje] was working on\",\n",
        "                \"[personaje] has been working in\", \"[personaje] was working in\",\n",
        "                \"[personaje] has been working at\", \"[personaje] was working at\",\n",
        "                \"[personaje] was in [lugar] when\", \"[personaje]  was in  the  middle of\",\n",
        "                \"[personaje] and [personaje] are [accion]\", \"[personaje] went to\",\n",
        "                \"as soon as [personaje]\", \"[personaje] was with\"],\n",
        "          \"Aventura\":[],\n",
        "          \"Comedia\":[],\n",
        "          \"Fantasia\":[\"[personaje] who in other time\", \"[personaje] who in [tiempo]\"],\n",
        "          \"Terror\":[],\n",
        "          \"Acción\":[],\n",
        "          \"Thriller\":[],\n",
        "          \"Romance\":[],\n",
        "          \"Ciencia Ficción\":[],\n",
        "          \"Drama\":[],\n",
        "          \"Misterio\":[]}\n",
        " \n",
        "#Diccionario de categorias y Logistica\n",
        "CATEGORIAS = {\"personajes\": PERSONAJES, \"lugares\": LUGARES, \"tiempos\": TIEMPOS,\n",
        "              \"acciones\": ACCIONES,\"eventos\": EVENTOS, \"claves\": CLAVES, \"generos\": GENEROS}\n",
        "CATEGORIAS[\"categorias\"] = CATEGORIAS\n",
        "\n",
        "#Diccionario de ids y ISBN\n",
        "IDs = {}\n",
        "# = {\"Aventura\":[],\"Comedia\":[],\"Fantasia\":[],\"Terror\":[],\"Acción\":[],\"Thriller\":[],\"Romance\":[],\"Ciencia Ficción\":[],\"Drama\":[],\"Misterio\":[]}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DB4mQfaRfp8Y",
        "colab_type": "text"
      },
      "source": [
        "#Metodos de control"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SwSB11MykZ6Z",
        "colab_type": "text"
      },
      "source": [
        "###Ensambladores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PV8ObHPy0ztE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Ensamblador especifico y Musa:\n",
        "def ensambladorMini(frase:str, clave:str, insercion:list) -> str:\n",
        "  '''Metodo que busca una palabra especifica en una frase y la remplaza por\n",
        "     las palabras que se le pasen en lista\n",
        "     Parametros:\n",
        "         frase (str): una frase la cual editar\n",
        "         clave (str): una palabra que va a buscar en la frase, solo si esta entre parentesis cuadrados\n",
        "         insercion (list(str)): es una lista de palabras que se inserta donde este la clave\n",
        "     Retorno:\n",
        "         retorna la frase con las palabras insertadas\n",
        "     Ejemplo:\n",
        "         ensambladorMini(\"[personaje] and [personaje] in [lugar]\",\"personaje\",[\"Jose\",\"Juliet\"]):\n",
        "             retorna -> \"Jose and Juliet in [lugar]\"\n",
        "         El metodo solo inserta las palabras en donde encuentra la clave y remueve solo los corchetes \n",
        "         que tienen la clave'''\n",
        "  if insercion == None or insercion == []:\n",
        "    return frase # si no hay nada que insertar\n",
        "  fin = str(\"#\" *random.randint(1, 5) + \"%\" * random.randint(1, 5) + \"$\" * random.randint(1, 5))\n",
        "  frase = \" \" + frase + fin  # esto es para evitar los casos en que la frase comience o termine con la clave\n",
        "  j = 0\n",
        "  \n",
        "  for i in frase.split(\"[\" + clave +\"]\"):\n",
        "    if j == 0:\n",
        "      frase = i # esto sobreescribe la frase, para no crear una variable\n",
        "    else:\n",
        "      frase += i\n",
        "    if fin not in i:\n",
        "      try: frase += insercion[j] # esto es para los casos en que la lista trae más inserciones de las que hay en la frase\n",
        "      except:\n",
        "        print(\"Una excepcion a ocurrido\",frase,\n",
        "              \"necesitaba más incersiones de las que le fueron pasadas en el\",\n",
        "              \"parametro insercion\")\n",
        "        frase += \"[\" + clave + \"]\"\n",
        "    j += 1\n",
        "  return frase.split(fin)[0][1:] #esto retorna la frase\n",
        "\n",
        "\n",
        "# Ensamblador de frases y Ensayos\n",
        "#(este debe ser modificado para utilizar las claves de multiplicidad y use un diccionario)\n",
        "def ensambladorFrase(frase:str, personajes:list, lugares:list, tiempos:list, acciones:list, eventos:list) -> str:\n",
        "    '''Metodo que recibe una frase con todos los ensambladores posibles\n",
        "       y devulve la frase ensamblada\n",
        "       Parametros:\n",
        "          frase (str): una frase la cual editar que puede o no contener\n",
        "                       espacio para insertar el resto de parametros\n",
        "          personajes (list(str)): una lista de palabras a insertar donde\n",
        "                                  encuentre la palabra personaje\n",
        "          lugares (list(str)): una lista de palabras a insertar donde\n",
        "                               encuentre la palabra lugar\n",
        "          tiempos (list(str)): una lista de palabras a insertar donde\n",
        "                               encuentre la palabra tiempo\n",
        "          acciones (list(str)): una lista de palabras a insertar donde\n",
        "                                encuentre la palabra accion\n",
        "          eventos (list(str)): una lista de palabras a insertar donde\n",
        "                               encuentre la palabra evento\n",
        "       Retorno:\n",
        "          retorna la frase con las palabras insertadas\n",
        "       Ejemplo:\n",
        "          ensambladorMini(\"[personaje] and [personaje] in [lugar]\",[\"Jose\",\"Juliet\"], [\"Kingdom\"],[\"yesterday\"],[\"fly\"],[\"pee\"]):\n",
        "              retorna -> \"Jose and Juliet in Kingdom\"\n",
        "          El metodo inserta las palabras que encuentre en la frase, si no\n",
        "          encuentra una clave, aunque se le haya pasado una lista, no las inserta'''\n",
        "\n",
        "    return ensambladorMini(\n",
        "        ensambladorMini(\n",
        "            ensambladorMini(\n",
        "                ensambladorMini(\n",
        "                    ensambladorMini(frase, \"personaje\", personajes),\n",
        "                          \"lugar\", lugares),\n",
        "                    \"tiempo\", tiempos),\n",
        "                \"accion\", acciones),\n",
        "            \"evento\", eventos) #retorna usando el metodo ensambladorMini(), debe modificarse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VV9f6yr2iKhE",
        "colab_type": "text"
      },
      "source": [
        "###Extracción y segregación de contenido"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjT0e3UxivP5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retorno aleatorio y Ordenanza\n",
        "def obtenerAleatorio(L:list):\n",
        "  '''Metodo que retorna un valor aleatorio en una lista enumerada\n",
        "       Parametros:\n",
        "          L (list): una lista\n",
        "       Retorno:\n",
        "          retorna un valor cualquiera que contenga L\n",
        "       Ejemplo:\n",
        "          obtenerAleatorio([\"Jose\",\"Juliet\",\"Rosero\",\"Fabian\"]):\n",
        "             retorna -> \"Jose\"'''\n",
        "  while True:\n",
        "    i = random.randint(0,len(L)-1)\n",
        "    if L[i]: return L[i]\n",
        "    \n",
        "\n",
        "def desordenar(L:list)->list:\n",
        "  '''metodo que recibe un list y lo devuelve en un orden aleatorio\n",
        "      Parametros:\n",
        "          L (list): una lista\n",
        "      Retorno:\n",
        "          retorna L con sus elementos intercambiados aleatoriamente\n",
        "      Ejemplo:\n",
        "          desordenar([\"Jose\",\"Juliet\",\"Rosero\",\"Fabian\"]):\n",
        "             retorna -> ['Fabian', 'Rosero', 'Jose', 'Juliet']'''\n",
        "  try:\n",
        "    orden = [] # aqui se almacenan los nuevos indices\n",
        "    for i in L:\n",
        "      while True:\n",
        "        rand = random.randint(0,len(L)-1) # genera un numero aleatorio en el rango de L\n",
        "        if rand not in orden:\n",
        "          orden.append(rand)\n",
        "          break\n",
        "    return [L[i] for i in orden] # retorna L en el nuevo orden de indices\n",
        "  except: return L # en caso de error retorna nuevamente L"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9NdpHTIlE35",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Elimina lo inconcluso en una frase y Filtro EMI\n",
        "def extraerFrase(texto:str) -> str:\n",
        "  '''metodo que toma el ultimo punto autoconclusivo de un texto y retorna todo lo anterior a este\n",
        "    Parametros:\n",
        "        texto (str): un fragmento de texto\n",
        "    Retorno:\n",
        "         retorna frase despues de haberle quitado frases inconclusas\n",
        "     Ejemplo:\n",
        "        extraerFrase(\"Fabian is a Great Player. The first thing that stands out \n",
        "                      about him, and I think it's important to note here because\n",
        "                      he has been so good for the team this year (and in his \n",
        "                      rookie season), but also what makes Adrian an exceptional \n",
        "                      player. He doesn't just play well on defense; when you \n",
        "                      watch video of games where they're playing against each \n",
        "                      other or\")\n",
        "        retornara -> \"Fabian is a Great Player. The first thing that stands out \n",
        "                      about him, and I think it's important to note here because\n",
        "                      he has been so good for the team this year (and in his \n",
        "                      rookie season), but also what makes Adrian an exceptional \n",
        "                      player. He doesn't just play well on defense;'\n",
        "      Como se puede ver el metodo debe eliminar la ultima frase, pues el modelo\n",
        "      no pudo concluirla por nuestro limite de tamaño. Notese que no siempre es \n",
        "      a partir de un punto lo que se busca eliminar, pues en el ejemplo es un \";\",\n",
        "      pero en escecia siempre debe ser una puntuacion el final.\n",
        "      Ademas, no importa si queda \";\", \",\" o \".\" al final, el retorno sea con un\n",
        "      punto al final'''\n",
        "  import nltk\n",
        "  nltk.download('punkt')\n",
        "  if texto.replace(nltk.sent_tokenize(texto)[-1], \"\"):\n",
        "    return texto.replace(nltk.sent_tokenize(texto)[-1], \"\")\n",
        "  return texto + \".\\n\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "haEiJc_Ph8md",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "b4cacb6f-0b21-48ae-f8db-f3ac3668fef1"
      },
      "source": [
        "# Identifica las categorias en una frase y la descompone y Pistas\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "def extraerCategorias(frase:str) -> dict:\n",
        "  '''metodo que retorna un diccionario con las categorias identificadas en la frase más la misma\n",
        "      Parametros:\n",
        "        frase (str): una frase en la cual se basara el metodo\n",
        "                     para generar el fragmento\n",
        "      Retorno:\n",
        "         Diccionario con las categorias identificadas y la frase original\n",
        "      Ejemplo:\n",
        "        extraerCategorias(\"so Groucho say, \\\"I'm not a man of my word.\\\".\\nThe first time I saw \n",
        "                           the film was in New York City at an event called The Film Society. It\n",
        "                           had been screened there before and it seemed to be well received by everyone\")\n",
        "          retornara -> {'frase': 'so Groucho say, \"I\\'m not a man of my word.\".\\nThe first time\n",
        "                                  I saw the film was in New York City at an event called The \n",
        "                                  Film Society. It had been screened there before and it seemed\n",
        "                                  to be well received by everyone',\n",
        "                        'personajes': [\"Groucho\",\"everyone\"],\n",
        "                        'lugares': [\"New York City\"],\n",
        "                        'tiempos': [\"The first time\",\"before\"]},\n",
        "                        'acciones': [\"say\",\"saw\",\"was\",\"had been\",\"seemed\",\"received\"],\n",
        "                        'eventos': [\"saw the film\",\"The Film Society\",\"to be well received\"] '''\n",
        "  import nltk\n",
        "  nltk.download('punkt')\n",
        "  nltk.download('averaged_perceptron_tagger')\n",
        "\n",
        "  tagged = nltk.pos_tag(nltk.word_tokenize(frase))\n",
        "  personajes = []\n",
        "  lugares = []\n",
        "  eventos = []\n",
        "  i = 0\n",
        "  while i < len(tagged):\n",
        "    if tagged[i][1] == \"NNP\":\n",
        "      ins = personajes\n",
        "      if i > 0 and tagged[i-1][1] == \"IN\":\n",
        "        ins = lugares\n",
        "      ins.append(tagged[i][0])\n",
        "      for j in range(i + 1, len(tagged)):\n",
        "        if tagged[j][1] != \"NNP\":\n",
        "          i = j\n",
        "          break\n",
        "        ins[-1] += \" \" + tagged[j][0]\n",
        "    if tagged[i][1] == \"NN\":\n",
        "      for j in range(i - 5, i):\n",
        "        try:\n",
        "          if tagged[j][1][:2] == \"VB\" and tagged[j][0].isalpha():\n",
        "            eventos.append(tagged[j][0])\n",
        "            for k,q in tagged[j + 1: i + 1]:\n",
        "              eventos[-1] += \" \" + k\n",
        "            break\n",
        "        except: continue\n",
        "    i += 1\n",
        "  return {\"frase\": frase, \"personajes\": personajes, \"lugares\": lugares,\n",
        "          \"tiempos\": [i.lower() for i, j in tagged if j[:2] == \"RB\"],\n",
        "          \"acciones\": [i for i, j in tagged if j[:2] == \"VB\" and i.isalpha()],\n",
        "          \"eventos\": eventos}"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GyWuKcqp-wp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def agregarACategoria(diccionario:dict):\n",
        "  '''metodo que añade de manera ordenada palabras a los diccionarios\n",
        "      Parametros:\n",
        "        diccionario (dict): Diccionario con los datos a guardar en los\n",
        "                            correspondientes diccionarios \n",
        "      Retorno:\n",
        "         No tiene retorno\n",
        "      Ejemplo:\n",
        "          almacenarTexto({\"claves\":[\"[personaje] went to\",\"[lugar] suddenly\"],\n",
        "                          \"personajes\":{\"Fantasia\":[\"Childe Roland\"],\n",
        "                          \"Acción\":[\"Jackie Chan\",\"Bruce Lee\"]}})\n",
        "            Guardara los textos \"[personaje] went to\" y \"[lugar] suddenly\" en el diccionario CATEGORIAS[\"claves\"],\n",
        "            el texto \"Childe Roland\" en el diccionario CATEGORIAS[\"personajes\"][\"Fantasia\"] \n",
        "            y los textos \"Jackie Chan\" y \"Bruce Lee\" en el diccionario CATEGORIAS[\"personajes\"][\"Acción\"]'''\n",
        "  for i in diccionario.keys():\n",
        "    if i in CATEGORIAS:\n",
        "      if type(diccionario[i]) == dict:\n",
        "        for j in diccionario[i].keys():\n",
        "          try: CATEGORIAS[i][j] += diccionario[i][j]\n",
        "          except: continue\n",
        "      if type(diccionario[i]) == list:\n",
        "        try: CATEGORIAS[i] += diccionario[i]\n",
        "        except: continue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXnSEWljhley",
        "colab_type": "text"
      },
      "source": [
        "###Generación de historias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khWQCORsSnU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Codifica textos para cada estructura, almacena en un arreglo y Acto\n",
        "def generadorHistoria(INPUT:str=None, genero:str=None) -> str:\n",
        "  '''Metodo que retorna un fragmento de historia\n",
        "    Parametros:\n",
        "        INPUT (str)[opcional]: una frase en la cual se basara el metodo\n",
        "                               para generar el fragmento\n",
        "        genero (str)[opcional]: un genero para que el metodo genere el\n",
        "                                fragmento solo con este genero\n",
        "    Retorno:\n",
        "         retorna un fragmento de historia\n",
        "    Ejemplos:\n",
        "     1 -> generadorHistoria(\"Dolores Claiborne speak his mind.\"):\n",
        "             retorna -> \" Dolores Claiborne speak his mind.\n",
        "                         The first time I saw him, he was in the middle of a \n",
        "                         conversation with another woman who had been sitting \n",
        "                         next to me for about five minutes when she suddenly \n",
        "                         turned and said something that made my stomach turn: \n",
        "                         \"I'm not going anywhere.\" She then walked away from \n",
        "                         us without\"\n",
        "          El modelo expande la frase a un parrafo consiso\n",
        "\n",
        "     2 -> generadorHistoria(genero=\"Fantasia\"):\n",
        "             retorna -> \"finally invasion of the United States.\n",
        "                         The first step in this process was to establish a \n",
        "                         new government, which would be called \"the Republic.\" \n",
        "                         The Constitution\"\n",
        "          El modelo genera un fragmento con las claves, personajes, eventos,\n",
        "          lugares, tiempos y acciones que halle en los diccionarios con el\n",
        "          genero recibido o con \"NN\"\n",
        "\n",
        "     3 -> generadorHistoria():\n",
        "             retorna -> \"finally after Innsmouth now, and I'm not sure if \n",
        "                         it's because of the weather or just my own\n",
        "                         laziness.\n",
        "                         The first thing that struck me about this book was\n",
        "                         how much more interesting a story is when you're in\n",
        "                         your twenties than at any\"\n",
        "          El modelo genera un fragmento con las claves, personajes, eventos,\n",
        "          lugares, tiempos y acciones que halle en los diccionarios con un \n",
        "          genero aleatorio de los diccionario y con \"NN\" '''\n",
        "  if INPUT == None:\n",
        "    if genero == None:\n",
        "        genero = obtenerAleatorio(GENEROS) # define el genero aleatorio\n",
        "    INPUT = ensambladorFrase(obtenerAleatorio(CLAVES[\"NN\"]),\n",
        "                           desordenar(PERSONAJES[genero]),\n",
        "                           desordenar(LUGARES[genero]),\n",
        "                           desordenar(TIEMPOS[genero]),\n",
        "                           desordenar(ACCIONES[genero]),\n",
        "                           desordenar(EVENTOS[genero])) # crea una frase con el genero\n",
        "  for i in range(len(MODELS)):\n",
        "      # codifica en tokens INPUT para que lo procese el modelo\n",
        "      input_ids = tokenizer[i].encode(INPUT, add_space_before_punct_symbol = True, return_tensors = 'pt')\n",
        "      # ingresa INPUT a la red y almacena el retorno\n",
        "      INPUT = (model[i].generate(input_ids, max_length = max(2 * len(INPUT), 75),\n",
        "                                 no_repeat_ngram_size = 2, repetition_penalty = 4)) \n",
        "      # convierte INPUT nuevamente en palabras y remueve frases sin terminar a traves del metodo depuradorHistoria()\n",
        "      return depuradorHistoria(str(tokenizer[i].decode(INPUT[0], skip_special_tokens = True))) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-5YdMOzpWlU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Generador de frases por parametros y Anuncio\n",
        "#(esta es la parte que le correspondera a otra fase)\n",
        "def generarFrase(genero:str=None,clave=None,contexto:str=None):\n",
        "    ''' Metodo que ensambla una frase con las claves identificadas\n",
        "    # Si el genero no esta entre los generos se hace None\n",
        "    if genero not in GENEROS:\n",
        "      genero = None\n",
        "    # Si no recibio genero o lo perdio se hace una frase general\n",
        "    if genero == None:\n",
        "      genero==\"NN\"\n",
        "    # Si no tiene un contexto\n",
        "    #(esta es la parte que le correspondera a otra fase)\n",
        "    if contexto != None:\n",
        "    CLAVES[]'''\n",
        "    return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VD-PCnCiZMH",
        "colab_type": "text"
      },
      "source": [
        "###Depurado de historias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eU2hPocpfsSR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retorno y Tecnico\n",
        "#(este debe filtrar las frases, para no repetir y esas vainas)\n",
        "def depuradorHistoria(frase:str):\n",
        "  '''metodo que retorna un fragmento de historia\n",
        "    Parametros:\n",
        "        frase (str): un fragmento de texto\n",
        "    Retorno:\n",
        "         retorna frase despues de haberle quitado incoherencias\n",
        "     Ejemplo:\n",
        "         depuradorHistoria(\"finally invasion of the United States.\n",
        "                         the first step in this process was to establish a \n",
        "                         new government, which would be called \"the Republic.\" \n",
        "                         The Constitution\"):\n",
        "             retorna -> \"Finally invasion of the United States.\n",
        "                         The first step in this process was to establish a \n",
        "                         new government, which would be called \"the Republic\".\"\n",
        "         El metodo convirtio en mayuscula los inicio de los parrafos y omitio\n",
        "         la frase que estaba comenzando'''\n",
        "  frase = extraerFrase(frase) # extrae las frases inconclusas utilizando el metodo extraerFrase()\n",
        "  frase = frase[0].upper() + frase[1:] # convierte a mayuscula el inicio de la entrada\n",
        "  return frase"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNkDcjV0kRE2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adapta una frase con referencias a particularidades de la historia y Cover\n",
        "def adapter(frase:str,referencias:list,especificacion:list)->str:\n",
        "  '''metodo que toma una frase que construimos a punta de referencias y debe eliminar todas las referencias y reemplazarlas por otras palabra\n",
        "  ejemplo de como deberia quedar:\n",
        "  adapter(\"Rambo in The White House then, and now.\",[\"Rambo\",\"The White House\",\"then\"],[\"Jose\",\"his township\",\"at quarantine\"])\n",
        "    retornara -> 'Jose in his township at quarantine, and now' '''\n",
        "  if especificacion == [] or not especificacion or None in especificacion or referencias  == []:\n",
        "    return frase\n",
        "  else:\n",
        "    return adapter(frase.replace(referencias[0], especificacion[0]), referencias[1:], especificacion[1:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkHuUAN7hs-J",
        "colab_type": "text"
      },
      "source": [
        "###Guardado de dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ET3MXKCrhBd7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "f194296e-16f6-4cca-f991-23cb1fb4ca7a"
      },
      "source": [
        "# Guarda una unica sentencia y Magneticas\n",
        "# Notas: Los archivos deben tener el nombre de las columnas , ademas aparece una\n",
        "#        nueva columna que son los index (empiezan en 0)\n",
        "import pandas as pd\n",
        "def almacenarTexto(texto:str,direccion:str, archivo:str, columna:str, fila:int, formato:str):\n",
        "  '''metodo que almacena un texto en un archivo .csv o un excel\n",
        "      Parametros:\n",
        "        texto (str): una frase se guardara el en archivo\n",
        "        direccion (str): dirección de la carpeta de guardado\n",
        "        archivo (str): nombre del archivo a guardar o modificar\n",
        "        columna (str): columna del archivo\n",
        "        fla (int): fila del archivo\n",
        "        formato (str): formato del archivo, puede ser xlsx (Excel) o csv (Dataset)\n",
        "      Retorno:\n",
        "         No tiene retorno\n",
        "      Ejemplo:\n",
        "        almacenarTexto(\"Rambo in The Jet\", \n",
        "                      \"/content\", \n",
        "                      \"HSL2\",\n",
        "                      \"Personajes\",\n",
        "                      2, \n",
        "                      \"xlsx\")\n",
        "        Guardar el texto \"Rambo in The Jet\" en la celda Personajes2, en un archivo de excel\n",
        "        llamado \"HSL2\" que deberia estar en \"/content/HSL2.xlsx\" '''\n",
        "    \n",
        "  ruta = direccion + \"/\" + archivo\n",
        "  if formato == \"csv\":\n",
        "    ruta += \".csv\"\n",
        "    try:\n",
        "      hoja = pd.read_csv(ruta)\n",
        "    except FileNotFoundError:\n",
        "      s = pd.DataFrame(data={\"Index\":{}})\n",
        "      print(archivo + \".csv\")\n",
        "      s.to_csv(archivo + \".csv\")\n",
        "    finally:\n",
        "      hoja = pd.read_csv(ruta,index_col=\"Index\")\n",
        "    print(hoja)\n",
        "    hoja.loc[fila, columna] = texto\n",
        "    print(hoja)\n",
        "    hoja.to_csv(ruta)\n",
        "  else:\n",
        "    ruta += \".xlsx\"\n",
        "    try:\n",
        "      hoja = pd.read_excel(ruta)\n",
        "    except FileNotFoundError:\n",
        "      s = pd.DataFrame(data={\"Index\":{}})\n",
        "      s.to_excel(archivo + \".xlsx\", index = False)\n",
        "    finally:\n",
        "      hoja = pd.read_excel(ruta,index_col=\"Index\")\n",
        "    hoja.head()\n",
        "    print(hoja)\n",
        "    hoja.loc[fila, columna] = texto\n",
        "    print(hoja)\n",
        "    hoja.to_excel(ruta, index = False)\n",
        "\n",
        "almacenarTexto(\"\",\"/content\", \"archivo\",\"Personajes\",2,\"csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "archivo.csv\n",
            "Empty DataFrame\n",
            "Columns: [Unnamed: 0]\n",
            "Index: []\n",
            "      Unnamed: 0 Personajes\n",
            "Index                      \n",
            "2            NaN           \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrHo1wzbnIdI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def obtenerIndex(direccion:dict = None, super = CATEGORIAS):\n",
        "  if type(super) == dict and super != {}:\n",
        "    if type(direccion) == dict and direccion != {}:\n",
        "      if list(direccion.keys())[0] in super:\n",
        "        return obtenerIndex(direccion[list(direccion.keys())[0]], super[list(direccion.keys())[0]])\n",
        "    if direccion:\n",
        "      try: return super[direccion]\n",
        "      except:\n",
        "        try: return super[list(super.keys())[direccion.keys()]]\n",
        "        except: print(\"Error de llave\")\n",
        "  if type(super) == list and super != []:\n",
        "    if type(direccion) == int:\n",
        "      try: return super[direccion]\n",
        "      except IndexError: print(\"Error de index\")\n",
        "    if type(direccion) == tuple and len(direccion) == 2:\n",
        "      try: return [super[i] for i in range(direccion[0],direccion[1])]\n",
        "      except IndexError: print(\"Error de index\")\n",
        "  print(\"Error en direccion:\", direccion, \"para\", super)\n",
        "\n",
        "# Generador de especificaciones y Peticion del Publico\n",
        "#(este debe ser modificado para utilizar dinamicamente las frases)\n",
        "def peticionFrase(especificaciones:dict = None) -> str:\n",
        "  '''metodo que permite generar frases a traves de multiples parametros, \n",
        "     esto se hace solo para que las pruebas sean más rapidas, para saber \n",
        "     que parametros se le deben pasar a ensambladorMini() o a ensambladorFrase()\n",
        "     \n",
        "     Parametros:\n",
        "         especificaciones (dict): Diccionario que especifica que claves usar\n",
        "                                  para generar la frase\n",
        "      Retorno:\n",
        "         La frase con las referencias cambiadas segun las espedificaciones\n",
        "      Ejemplo:\n",
        "      1 -> peticionFrase({\"frase\":{\"claves\":{\"NN\":0}}})\n",
        "            retornara -> 'Zelda is a'\n",
        "          En este ejemplo el metodo revisa el diccionario por parametro y solo \n",
        "          ve que contiene la llave \"frase\", \"claves\", acude al diccionario \n",
        "          CATEGORIAS para ver si existe ese diccionario, como sí lo encuentra \n",
        "          entonces ahora sabe que la frase que debe retornar esta en el \n",
        "          diccionario CLAVES, sigue llendo más adentro y ve la llave \"NN\", la \n",
        "          busca en el diccionario CLAVES y halla el numero 0, busca si \n",
        "          CLAVES[\"NN\"] tiene ese index, al hallarlo toma eso como frase. \n",
        "          Como el parametro al llamar el metodos solo traia la llave \"frase\" \n",
        "          entonces el decide llenar esa frase con un genero aleatorio, en el \n",
        "          ejemplo, generos debio quedar \"Aventura\", por eso termino siendo Zelda.\n",
        "\n",
        "      2 -> peticionFrase({\"frase\":{\"claves\":{\"NN\":0}}, \"genero\":\"Fantasia\"})\n",
        "            retornara -> 'Link is a'\n",
        "          En este ejemplo se sigue la logica del anterio, asi el metodo sabe\n",
        "          que la frase que debe devolver es '[personaje] is a', ahora, como\n",
        "          en el parametro del metodo tambien encuentra que se especifico el\n",
        "          genero \"fantasia\".\n",
        "\n",
        "      3 -> peticionFrase({\"frase\":{\"claves\":{\"NN\":0}},\"personajes\":[\"Mario\",\"Luigi\"]})\n",
        "            retornara -> 'Mario is a'\n",
        "          En este caso, siguiendo la logica del primer ejemplo, la frase que\n",
        "          debe devolver es '[personaje] is a', ahora, como en el parametro no\n",
        "          especifica genero, el metodo escojera uno aleatorio, pero sí esta las \n",
        "          claves \"personajes\" entonces se busca dentro de esa llave y halla un \n",
        "          list ([\"Mario\",\"Luigi\"]) entonces el retorno será GENERO = obtenerAleatorio(GENEROS);\n",
        "      return ensambladorFrase(CATEGORIAS[\"claves\"][\"NN\"][0], especificaciones[\"personajes\"],\n",
        "                              LUGARES[GENERO], TIEMPOS[GENERO], ACCIONES[GENERO],\n",
        "                              EVENTOS[GENERO])\n",
        "          Notese que si en \"personajes\" hubiera hallado un dict entonces \n",
        "          tendria que buscar las llaves de ese dict, y si eran,\n",
        "          por ejemplo {\"personajes\":{\"Fantasia\":(0,2)}} entonces el \n",
        "          retorno hubiese sido GENERO = obtenerAleatorio(GENEROS);\n",
        "      return ensambladorFrase(CATEGORIAS[\"claves\"][\"NN\"][0], \n",
        "                              [CATEGORIAS[\"personajes\"][\"Fantasia\"][i] for i in range(0,2)],\n",
        "                              LUGARES[GENERO], TIEMPOS[GENERO], ACCIONES[GENERO], EVENTOS[GENERO])\n",
        "      \n",
        "      4 -> peticionFrase()\n",
        "            retornara -> 'Rick Sanchez in spaceship then pandemic'\n",
        "          En este caso como no recibe ningun parametro, el toma una frase\n",
        "          aleatoria de CLAVES[\"NN\" entonces el retorno será GENERO = obtenerAleatorio(GENEROS);\n",
        "      return ensambladorFrase(obtenerAleatorio(CATEGORIAS[\"claves\"][\"NN\"]),\n",
        "                              PERSONAJES[GENERO], LUGARES[GENERO], TIEMPOS[GENERO],\n",
        "                              ACCIONES[GENERO], EVENTOS[GENERO])\n",
        "          \n",
        "      NOTAS:\n",
        "      - si en el diccionario parametro, la llave \"frase\" es un str, entonces\n",
        "        directamente lo tomara como frase, asumiendo que es una frase con\n",
        "        categorias a ensamblar\n",
        "      - si en el diccionario parametro, la llave \"genero\" es un int, entonces\n",
        "        tomara el genero CATEGORIAS[\"generos\"][int], siendo int obviamente el\n",
        "        entero las claves que se deben buscar de momento en el parametro son:\n",
        "        \"frase\", \"genero\", \"personajes\", \"lugares\", \"tiempos\", \"eventos\" y \n",
        "        \"acciones\", cualquier otra llave sera ignorada de momento\n",
        "      - si en algun punto recibe un tuple de int (como en la segunda parte del\n",
        "        ejemplo 3), entonces el metodo buscara si la lista solicitada tiene \n",
        "        todos los indices en este rango, si los tiene entonces generara un list\n",
        "        con estos indices\n",
        "      - si el metodo va a buscar una llave o un indice en un diccionario o una\n",
        "        lista y no la encuentra, debe retornarse a si mismo sin parametros, \n",
        "        asi: return peticionFrase()\n",
        "      - si el metodo recibe cualquier otra cosa que no sea un dict debe \n",
        "        retornarse a si mismo sin parametros, asi: return peticionFrase()\n",
        "      - si el metodo recibe la llave \"generos\", y en esta no hay un int o un \n",
        "        str debe retornarse a si mismo sin parametros, asi: return peticionFrase()\n",
        "      - si en algun diccionario encuentra un str, pero deberia encontrar un \n",
        "        list o un dict, debe retornarse a si mismo sin parametros, asi: \n",
        "        return peticionFrase()\n",
        "        ejemplo: si recibe peticionFrase({\"frase\":{\"claves\":{\"NN\":0}}, \"genero\":\"Aventura\", \n",
        "                                            \"personajes\":\"Mario\"}),\n",
        "        pues en \"personajes\" solo debe hallar un list o un dict '''\n",
        "  frase = None\n",
        "  personaje = None\n",
        "  genero = None\n",
        "  tiempo = None\n",
        "  lugar = None\n",
        "  accion = None\n",
        "  evento = None\n",
        "  \n",
        "  if especificaciones and type(especificaciones) == dict and especificaciones is not {}:\n",
        "    if \"frase\" in especificaciones:\n",
        "      if type(especificaciones[\"frase\"]) == str:\n",
        "        frase = especificaciones[\"frase\"]\n",
        "      if type(especificaciones[\"frase\"]) == int:\n",
        "        try: frase = CATEGORIAS[\"claves\"][\"NN\"][especificaciones[\"frase\"]]\n",
        "        except: pass\n",
        "      if type(especificaciones[\"frase\"]) == dict:\n",
        "        frase = obtenerIndex(especificaciones[\"frase\"])\n",
        "            \n",
        "    if 'genero' in especificaciones :\n",
        "      if type(especificaciones['genero']) == str:\n",
        "        genero = especificaciones['genero']\n",
        "      if type(especificaciones['genero']) == int:\n",
        "        try: genero = CATEGORIAS['generos'][especificaciones['genero']]\n",
        "        except: pass\n",
        "      if type(especificaciones[\"genero\"]) == dict:\n",
        "        genero = obtenerIndex(especificaciones[\"genero\"])\n",
        "\n",
        "    if ('personaje' in especificaciones):\n",
        "      if type(especificaciones['personaje']) == list:\n",
        "        personaje = especificaciones['personaje']\n",
        "      if type(especificaciones['personaje']) == dict:\n",
        "        personaje = obtenerIndex(especificaciones[\"personaje\"])\n",
        "      if type(especificaciones['personaje']) == str:\n",
        "        personaje = especificaciones['personaje']\n",
        "      if genero and (type(especificaciones['personaje']) == tuple or\n",
        "                     type(especificaciones['personaje']) == int):\n",
        "        personaje = obtenerIndex(especificaciones[\"personaje\"], super = CATEGORIAS[\"personajes\"][genero])\n",
        "\n",
        "    if ('lugar' in especificaciones):\n",
        "      if type(especificaciones['lugar']) == list:\n",
        "        lugar = especificaciones['lugar']\n",
        "      if type(especificaciones['lugar']) == dict:\n",
        "        lugar = obtenerIndex(especificaciones[\"lugar\"])\n",
        "      if type(especificaciones['lugar']) == str:\n",
        "        lugar = especificaciones['lugar']\n",
        "      if genero and (type(especificaciones['lugar']) == tuple or\n",
        "                     type(especificaciones['lugar']) == int):\n",
        "        lugar = obtenerIndex(especificaciones[\"lugar\"], super = CATEGORIAS[\"lugares\"][genero])\n",
        "\n",
        "    if ('tiempo' in especificaciones):\n",
        "      if type(especificaciones['tiempo']) == list:\n",
        "        tiempo = especificaciones['tiempo']\n",
        "      if type(especificaciones['tiempo']) == dict:\n",
        "        tiempo = obtenerIndex(especificaciones[\"tiempo\"])\n",
        "      if type(especificaciones['tiempo']) == str:\n",
        "        tiempo = especificaciones['tiempo']\n",
        "      if genero and (type(especificaciones['tiempo']) == tuple or\n",
        "                     type(especificaciones['tiempo']) == int):\n",
        "        tiempo = obtenerIndex(especificaciones[\"tiempo\"], super = CATEGORIAS[\"tiempos\"][genero])\n",
        "\n",
        "    if ('accion' in especificaciones):\n",
        "      if type(especificaciones['accion']) == list:\n",
        "        accion = especificaciones['accion']\n",
        "      if type(especificaciones['accion']) == dict:\n",
        "        accion = obtenerIndex(especificaciones[\"accion\"])\n",
        "      if type(especificaciones['accion']) == str:\n",
        "        accion = especificaciones['accion']\n",
        "      if genero and (type(especificaciones['accion']) == tuple or\n",
        "                     type(especificaciones['accion']) == int):\n",
        "        accion = obtenerIndex(especificaciones[\"accion\"], super = CATEGORIAS[\"acciones\"][genero])\n",
        "\n",
        "    if ('evento' in especificaciones):\n",
        "      if type(especificaciones['evento']) == list:\n",
        "        evento = especificaciones['evento']\n",
        "      if type(especificaciones['evento']) == dict:\n",
        "        evento = obtenerIndex(especificaciones[\"evento\"])\n",
        "      if type(especificaciones['evento']) == str:\n",
        "        evento = especificaciones['evento']\n",
        "      if genero and (type(especificaciones['evento']) == tuple or\n",
        "                     type(especificaciones['evento']) == int):\n",
        "        evento = obtenerIndex(especificaciones[\"evento\"], super = CATEGORIAS[\"eventos\"][genero])\n",
        "\n",
        "  if not genero or type(genero) != str: genero = obtenerAleatorio(GENEROS)\n",
        "  if not frase or type(frase) != str: frase = obtenerAleatorio(CLAVES[\"NN\"] + CLAVES[genero])\n",
        "  if not personaje: personaje = obtenerAleatorio(PERSONAJES[genero])\n",
        "  if not lugar: lugar = obtenerAleatorio(LUGARES[\"NN\"] + LUGARES[genero])\n",
        "  if not accion: accion = obtenerAleatorio(ACCIONES[\"NN\"] + ACCIONES[genero])\n",
        "  if not evento: evento = obtenerAleatorio(EVENTOS[\"NN\"] + EVENTOS[genero])\n",
        "  if not tiempo: tiempo = obtenerAleatorio(TIEMPOS[\"NN\"] + TIEMPOS[genero])\n",
        "\n",
        "  if type(personaje) != list: personaje = [personaje]\n",
        "  if type(lugar) != list: lugar = [lugar]\n",
        "  if type(tiempo) != list: tiempo = [tiempo]\n",
        "  if type(accion) != list: accion = [accion]\n",
        "  if type(evento) != list: evento = [evento]\n",
        "\n",
        "  frase = ensambladorFrase(frase,personaje,lugar,tiempo,accion,evento) # con todos los parametros construye una frase\n",
        "  \n",
        "  return frase"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-0tsk73n0Ib",
        "colab_type": "text"
      },
      "source": [
        "#Clase Historia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AotcbF1lHqkS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def makeId() -> str:\n",
        "  id = str(\"#\" * random.randint(1, 5) + \"%\" * random.randint(1, 5) +  \"$\" * random.randint(1, 5))\n",
        "  if id not in IDs: return id\n",
        "  else: return makeId()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60u6mZtp-762",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Codigo de control y Discografica\n",
        "class Historia:\n",
        "  id = None\n",
        "  titulo = None\n",
        "  genero = None\n",
        "  personajes = None\n",
        "  lugares = None\n",
        "  tiempos = None\n",
        "  eventos = None\n",
        "  acciones = None\n",
        " \n",
        "  def __init__(self,genero:str, titulo:str = None, fichas_tecnicas:dict = {}):\n",
        "    self.id=makeId()\n",
        "    IDs[self.id] = self\n",
        "    self.genero = genero\n",
        "    for i in [\"personajes\", \"lugares\", \"tiempos\", \"eventos\", \"acciones\"]:\n",
        "      CATEGORIAS[self.id + str(i)] = []\n",
        "    for i,j in fichas_tecnicas.items():\n",
        "      agregarACategoria({self.id + i:j})\n",
        "    self.personajes = CATEGORIAS[self.id + \"personajes\"]\n",
        "    self.lugares = CATEGORIAS[self.id + \"lugares\"]\n",
        "    self.tiempos = CATEGORIAS[self.id + \"tiempos\"]\n",
        "    self.eventos = CATEGORIAS[self.id + \"eventos\"]\n",
        "    self.acciones = CATEGORIAS[self.id + \"acciones\"]\n",
        " \n",
        "  def agregar(self, dicCate):\n",
        "    for i,j in dicCate.items():\n",
        "      agregarACategoria({self.id+i:j})\n",
        "    self.personajes += dicCate[\"personajes\"]\n",
        "    self.lugares += dicCate[\"lugares\"]\n",
        "    self.tiempos += dicCate[\"tiempos\"]\n",
        "    self.eventos += dicCate[\"eventos\"]\n",
        "    self.acciones += dicCate[\"acciones\"]\n",
        "  \n",
        "  def __referencias(self, nuevo:bool = True):\n",
        "    '''Metodo que retorna las referencias para los personajes, lugares, tiempos,\n",
        "        acciones, eventos y la clave de las frases\n",
        "        Parametros:\n",
        "          nuevo (bool): True-> Utiliza nuevas referencias para la creación de la frase\n",
        "                        False -> Toma las referencias existentes y le agrega más\n",
        "        Retorno:\n",
        "          Una lista de las referencias en el siguiente orden:\n",
        "            Personajes, Lugares, Tiempos, Accikones, Eventos, Clave'''\n",
        "    if nuevo:\n",
        "      refP = desordenar(PERSONAJES[self.genero])\n",
        "      refL = desordenar(LUGARES[self.genero])\n",
        "      refT = desordenar(TIEMPOS[self.genero])\n",
        "      refA = desordenar(ACCIONES[self.genero])\n",
        "      refE = desordenar(EVENTOS[self.genero])\n",
        "    else:\n",
        "      refP = desordenar(PERSONAJES[self.genero] + self.personajes)\n",
        "      refL = desordenar(LUGARES[self.genero] + self.lugares)\n",
        "      refT = desordenar(TIEMPOS[self.genero] + self.tiempos)\n",
        "      refA = desordenar(ACCIONES[self.genero] + self.acciones)\n",
        "      refE = desordenar(EVENTOS[self.genero] + self.eventos)\n",
        "    ref_clave = obtenerAleatorio(desordenar(CLAVES[\"NN\"])) # En el caso de que CLAVES tenga más datos para cada genero se debe cambiar a CLAVES[self.genero]\n",
        "    return refP, refL, refT, refA, refE, ref_clave\n",
        "\n",
        "  def __crear_frase(self, refP:list, refL:list, refT:list, refA:list, refE:list,\n",
        "                      ref_clave:str, finalizar:bool = False):\n",
        "    '''Metodo que crea una frase tomando los artibutos como las referencias\n",
        "         Parametros:\n",
        "          refP (List): Lista con las referencias de Personajes\n",
        "          refL (List): Lista con las referencias de Lugares\n",
        "          refT (List): Lista con las referencias de Tiempos\n",
        "          refA (List): Lista con las referencias de Acciones\n",
        "          refE (List): Lista con las referencias de Eventos          \n",
        "          ref_clave (str): Estructura de la frase a crear\n",
        "          finalizar (bool)[opcional]: True -> Forza una frase de finalización\n",
        "                                      False -> Crea una frase con una clave aleatorioa\n",
        "        Retorno:\n",
        "          La frase generada por GTP-2 a partir de los parametros dle metodo'''\n",
        "    if finalizar:\n",
        "      ensamblaF = peticionFrase({\"frase\": CLAVES[\"NN\"][(3, 4, 5, 11, 12, 17)[random.randint(0, 2)]],\n",
        "                                 \"genero\":self.genero, \"personajes\": refP, \"lugares\": refL, \n",
        "                                 \"tiempos\": refT, \"acciones\": refA,\"eventos\": refE})\n",
        "    else:\n",
        "      ensamblaF = ensambladorFrase(ref_clave, refP, refL, refT, refA, refE)\n",
        "    generaH = generadorHistoria(ensamblaF)\n",
        "    frase = adapter(generaH, refP + refL + refT + refA +refE, self.personajes + self.lugares +\n",
        "                      self.tiempos + self.acciones + self.eventos)\n",
        "    return frase\n",
        "\n",
        "  # Extraer las partes de la historia \n",
        "  def iniciarHistoria(self) -> str:\n",
        "    '''Metodo que crea una historia aleatoria y la adapta a las configuraciones\n",
        "       de la clase Historia\n",
        "       Parametros:\n",
        "          El metodo no tiene parametros\n",
        "       Retorno:\n",
        "          Frase generada con GTP-2 y las categorias de la frase\n",
        "       Ejemplo:\n",
        "          obj_historia = Historia(\"Aventura\")\n",
        "          obj_historia.iniciarHistoria()\n",
        "          Esto retornara una frase del genero Aventura con los personajes, lugares,\n",
        "          tiempos, eventos y acciones correspondientes al objeto obj_historia'''\n",
        "    # Definicion de referencias aleatorias\n",
        "    refP, refL, refT, refA, refE, ref_clave = self.__referencias()\n",
        "    \n",
        "    # Generación de la frase a partir de las referencias y modificación por los atributos de la clase\n",
        "    retorno = self.__crear_frase(refP, refL, refT, refA, refE, ref_clave)\n",
        "    \n",
        "    # Identificación de cada una de las categorias que hay en la frase\n",
        "    categorias = extraerCategorias(retorno)\n",
        "    del categorias['frase']\n",
        "    self.agregar(categorias)\n",
        "    return retorno\n",
        " \n",
        "  # Generear la hisotira\n",
        "  def continuarHistoria(self) -> str:\n",
        "    # Definicion de referencias aleatorias\n",
        "    refP, refL, refT, refA, refE, ref_clave = self.__referencias(False)\n",
        "    \n",
        "    # Generación de la frase a partir de las referencias y modificación por los atributos de la clase\n",
        "    retorno = self.__crear_frase(refP, refL, refT, refA, refE, ref_clave)\n",
        "    \n",
        "    # Identificación de cada una de las categorias que hay en la frase\n",
        "    categorias = extraerCategorias(retorno)\n",
        "    del categorias[\"frase\"]\n",
        "    \n",
        "    # Control de la cantidad de los multiples llamos a la generación de una historia\n",
        "    if categorias[\"eventos\"] and categorias[\"eventos\"] != []: # Valida que se hayan generado eventos\n",
        "        exponEventos = len(categorias[\"eventos\"])\n",
        "        self.agregar(categorias)\n",
        "        if len(self.eventos) - exponEventos > exponEventos: # Valida que la historia sea concluyente\n",
        "          return retorno\n",
        "        else:\n",
        "          if len(self.eventos) > (exponEventos**2): # Forzar una historia concluyente\n",
        "            retorno = self.__crear_frase(refP, refL, refT, refA, refE, ref_clave, True)\n",
        "          return retorno + self.continuarHistoria()\n",
        "    else:\n",
        "      print(\"¡No produjo eventos!\")\n",
        "      return self.continuarHistoria()\n",
        " \n",
        "  def controlador(self):\n",
        "      return\n",
        " \n",
        "  def getFichaTecnica(self):\n",
        "    return {i[len(self.id):]:j for i,j in CATEGORIAS.items() if self.id in i}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_gy7ZAIbILT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "1927baba-0ae5-4396-ee12-154a5513c2d0"
      },
      "source": [
        "h = Historia(obtenerAleatorio(GENEROS))\n",
        "print(h.genero)\n",
        "print(h.iniciarHistoria())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keyword arguments {'add_space_before_punct_symbol': True} not recognized.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Comedia\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "Groucho Marx is a great writer, but he's not the greatest.\n",
            "The best of all time? The one who has been in every movie ever made and won an Oscar for it (and I'm talking about you Mr Spielberg). \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ht8h6lpl4_qB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "31da6e2b-4b2d-4360-d3ed-c1d24c0e9e3b"
      },
      "source": [
        "print(h.getFichaTecnica())\n",
        "print(h.eventos)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'personajes': ['Groucho Marx', 'Mr Spielberg', 'Groucho Marx', 'Mr Spielberg'], 'lugares': [], 'tiempos': ['not', 'ever', 'not', 'ever'], 'eventos': ['is a great writer', 'has been in every movie', 'made and won an Oscar', 'is a great writer', 'has been in every movie', 'made and won an Oscar'], 'acciones': ['is', 'has', 'been', 'made', 'won', 'talking', 'is', 'has', 'been', 'made', 'won', 'talking']}\n",
            "['is a great writer', 'has been in every movie', 'made and won an Oscar', 'is a great writer', 'has been in every movie', 'made and won an Oscar']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sc6UHI5w4ByA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "a38174d4-f98b-4a30-806a-0cf5f956376a"
      },
      "source": [
        "print(h.continuarHistoria())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keyword arguments {'add_space_before_punct_symbol': True} not recognized.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
            "[nltk_data]       date!\n",
            "has made and won an Oscar in every movie was in has when I saw the film. He said, 'I'm going to make a movie about thhas made and won an Oscar in every movie.' And he did.\"\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZhB_MCs6gZM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Adaptador (a veces si se hace un cambio en una clase de Colab ningun objeto creado antes del cambio tendra los nuevos metodos y atributos)\n",
        "h = Historia(h.genero, fichas_tecnicas=h.getFichaTecnica())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Z-fQShZ7lek",
        "colab_type": "text"
      },
      "source": [
        "###Historia de drama sin Titulo\n",
        "\n",
        "At last Abigail in divorce proceedings.\n",
        "The court heard that the couple had been married for more than 20 years and were expecting their first child when she was found dead at her home on July 1, 2012 - just days after giving birth to a daughter with husband David Brownlee (pictured).\n",
        "\n",
        "However David Brownlee, the mother of a young boy who was killed in an accident at her home last year and whose husband is serving time for manslaughter charges related to that death has filed suit against two men accused by police."
      ]
    }
  ]
}