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            "Collecting tensorflow_text==2.15.0\n",
            "  Downloading tensorflow_text-2.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tensorflow-hub>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_text==2.15.0) (0.16.1)\n",
            "Requirement already satisfied: tensorflow<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_text==2.15.0) (2.15.0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.16,>=2.15.0->tensorflow_text==2.15.0) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.16,>=2.15.0->tensorflow_text==2.15.0) (1.6.3)\n",
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            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow<2.16,>=2.15.0->tensorflow_text==2.15.0) (1.25.2)\n",
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            "Installing collected packages: tensorflow_text\n",
            "Successfully installed tensorflow_text-2.15.0\n",
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.2-py3-none-any.whl (84 kB)\n",
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            "Installing collected packages: xxhash, requests, pyarrow, dill, multiprocess, datasets, evaluate\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 evaluate-0.4.2 multiprocess-0.70.16 pyarrow-16.1.0 requests-2.32.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow_text==2.15.0\n",
        "#!pip install -q -U tensorflow-text==2.14.* tensorflow==2.14.*\n",
        "\n",
        "!pip install evaluate\n",
        "#!pip install bert_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "import tensorflow as tf\n",
        "import tensorflow_text as text\n",
        "import pickle"
      ],
      "metadata": {
        "id": "QNI3_Olq3mxr"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reloaded = tf.saved_model.load('/content/drive/MyDrive/sememe_tranaslator_musa')\n",
        "reloaded = tf.saved_model.load('fixed_48percent')"
      ],
      "metadata": {
        "id": "g6S-bnGK7sc8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "9BnqZdEFAwMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('decoded_sentences_awgn.pickle', 'rb') as handle:\n",
        "    decoded_sentences_awgn = pickle.load(handle)\n",
        "with open('decoded_sentences_rayleigh.pickle', 'rb') as handle:\n",
        "    decoded_sentences_rayleigh = pickle.load(handle)"
      ],
      "metadata": {
        "id": "W22CZnZeCCnk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('test_raw.pickle', 'rb') as handle:\n",
        "    test_raw = pickle.load(handle)"
      ],
      "metadata": {
        "id": "AzzDwrmDD955"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_idx = 500\n",
        "end_idx = 503"
      ],
      "metadata": {
        "id": "xAWPVn8FHH9q"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install nltk\n",
        "translated_awgn = []\n",
        "translated_rayleigh = []\n",
        "for snr in range(7):\n",
        "    tr_aw = []\n",
        "    tr_ray = []\n",
        "    for sent in decoded_sentences_awgn[snr]:\n",
        "        tr_aw.append(reloaded(tf.constant(sent)).numpy().decode('utf-8'))\n",
        "    for sent in decoded_sentences_rayleigh[snr]:\n",
        "        tr_ray.append(reloaded(tf.constant(sent)).numpy().decode('utf-8'))\n",
        "    translated_awgn.append(tr_aw)\n",
        "    translated_rayleigh.append(tr_ray)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sVwaCfgbz3Ju"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "awgn_bleu_input = []\n",
        "rayleigh_bleu_input = []\n",
        "for snr in range(7):\n",
        "  snr_ll_awgn = []\n",
        "  for ll in translated_awgn[snr]:\n",
        "      snr_ll_awgn.append(ll.split())\n",
        "  awgn_bleu_input.append(snr_ll_awgn)\n",
        "  snr_ll_rayleigh = []\n",
        "  for ll in translated_rayleigh[snr]:\n",
        "      snr_ll_rayleigh.append(ll.split())\n",
        "  rayleigh_bleu_input.append(snr_ll_rayleigh)"
      ],
      "metadata": {
        "id": "RcRh0VVeErfo"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_input = []\n",
        "for sent in test_raw:\n",
        "  raw_input.append(sent.split())"
      ],
      "metadata": {
        "id": "TbzKRbtoGI-D"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.translate import meteor\n",
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "awgn_snr = [-3,-2,-1,0,1,2,3]\n",
        "rayleigh_snr  = [0,2,4,6,8,10,12]\n",
        "def compute_meteor(ref_sentence_list, tx_sentence_list):\n",
        "    N = len(tx_sentence_list)\n",
        "    score = 0\n",
        "    for s in range(N):\n",
        "        score += meteor([ref_sentence_list[s]], tx_sentence_list[s])\n",
        "    return score/N\n",
        "print('AWGN Simulation METEOR Scores')\n",
        "for i in range(7):\n",
        "\n",
        "    K = compute_meteor(raw_input[start_idx:end_idx],awgn_bleu_input[i])\n",
        "    print('SNR ' + str(awgn_snr[i]) + ': ', K)\n",
        "print('Rayleigh Simulation METEOR Scores')\n",
        "for s in range(7):\n",
        "    K = compute_meteor(raw_input[start_idx:end_idx],rayleigh_bleu_input[s])\n",
        "    print('SNR ' + str(rayleigh_snr[s]) + ': ', K)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmG8E6zDiuR6",
        "outputId": "7c9d3f65-1a95-4906-ac02-5276337b3e52"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_transformers\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "#from transformers import BertModel\n",
        "#model  = SentenceTransformer(\"bertModel\")\n",
        "#model  = SentenceTransformer(\"sentence-transformers/all-miniLM\")\n",
        "model  = SentenceTransformer(\"bert-base-uncased\")\n",
        "#model = BertModel(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "4fd1c631430c41bdb382857d93bd5b41",
            "2271078f930042a097187caf60e33f5c",
            "1a67dc550d6e4e3f83f08ebd9bfa4030",
            "bcfffab3f0104cfdb86329851e49602a",
            "66f5ea37ae4f4a1fa7b9ece2f3f7a4cd",
            "f18884d94039426ab27a68713b986871",
            "9bc9eaa64a724e91a9be0254da14178b",
            "6f93b160ac2543ecb8a7e78f7b3ebf52",
            "c0876a3bc50241ffae0bb7384fad806e",
            "d63fc54744854137b21024e0587d10eb"
          ]
        },
        "id": "BqXB6Ggi7GD-",
        "outputId": "777ecad2-6242-4c0b-de02-7064b3247b59"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence_transformers\n",
            "  Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, sentence_transformers\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 sentence_transformers-3.0.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n",
            "WARNING:sentence_transformers.SentenceTransformer:No sentence-transformers model found with name bert-base-uncased. Creating a new one with mean pooling.\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# see the details of model with print\n",
        "embeddings_in = model.encode(test_raw[start_idx:end_idx])\n",
        "print('AWGN BERT Scores')\n",
        "for snr in range(7):\n",
        "    embeddings_tx =  model.encode(translated_awgn[snr])\n",
        "    similarities_sememe = cosine_similarity(embeddings_in,embeddings_tx)\n",
        "    avg_sim = 0\n",
        "    for i in range(len(similarities_sememe)):\n",
        "        avg_sim += similarities_sememe[i,i]\n",
        "    avg_sim = avg_sim / (i+1)\n",
        "    print('SNR', awgn_snr[snr],': ', (avg_sim-0.4)/0.6)\n",
        "print('Rayleigh BERT Scores')\n",
        "for snr in range(7):\n",
        "    embeddings_tx =  model.encode(translated_rayleigh[snr])\n",
        "    similarities_sememe = cosine_similarity(embeddings_in,embeddings_tx)\n",
        "    avg_sim = 0\n",
        "    for i in range(len(similarities_sememe)):\n",
        "        avg_sim += similarities_sememe[i,i]\n",
        "    avg_sim = avg_sim / (i+1)\n",
        "    print('SNR', rayleigh_snr[snr],': ', (avg_sim-0.4)/0.6)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNZ7ibyj6hC4",
        "outputId": "bf5a8353-13f8-438d-e5cf-7d6f5fc5f09a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
          ]
        }
      ]
    }
  ]
}