en-hi-te-translation.ipynb

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     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "from datasets import load_dataset\n",
    "from tokenizers import Tokenizer\n",
    "from tokenizers.models import WordPiece\n",
    "from tokenizers import normalizers\n",
    "from tokenizers.normalizers import NFD, Lowercase, StripAccents\n",
    "from tokenizers.pre_tokenizers import Whitespace\n",
    "from tokenizers.trainers import WordPieceTrainer\n",
    "from tokenizers import decoders\n",
    "from pathlib import Path\n",
    "from tqdm.auto import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "import gc"
   ]
  },
  {
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     "text": [
      "env: TOKENIZERS_PARALLELISM=false\n"
     ]
    }
   ],
   "source": [
    "%env TOKENIZERS_PARALLELISM = false"
   ]
  },
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   "outputs": [
    {
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      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/2.29k [00:00<?, ?B/s]"
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     "text": [
      "Downloading and preparing dataset opus100/en-hi (download: 34.33 MiB, generated: 120.06 MiB, post-processed: Unknown size, total: 154.39 MiB) to /root/.cache/huggingface/datasets/opus100/en-hi/0.0.0/256f3196b69901fb0c79810ef468e2c4ed84fbd563719920b1ff1fdc750f7704...\n"
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      "text/plain": [
       "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
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      "text/plain": [
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    {
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      "text/plain": [
       "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opus100 downloaded and prepared to /root/.cache/huggingface/datasets/opus100/en-hi/0.0.0/256f3196b69901fb0c79810ef468e2c4ed84fbd563719920b1ff1fdc750f7704. Subsequent calls will reuse this data.\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset opus100/en-te (download: 1.92 MiB, generated: 6.75 MiB, post-processed: Unknown size, total: 8.66 MiB) to /root/.cache/huggingface/datasets/opus100/en-te/0.0.0/256f3196b69901fb0c79810ef468e2c4ed84fbd563719920b1ff1fdc750f7704...\n"
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    {
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      },
      "text/plain": [
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       "model_id": "29c89a5bed104f20baaea854a2ca3a54",
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      "text/plain": [
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       "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opus100 downloaded and prepared to /root/.cache/huggingface/datasets/opus100/en-te/0.0.0/256f3196b69901fb0c79810ef468e2c4ed84fbd563719920b1ff1fdc750f7704. Subsequent calls will reuse this data.\n"
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   ],
   "source": [
    "dataset1 = load_dataset(\"opus100\", \"en-hi\")\n",
    "df1_train = pd.DataFrame(dataset1['train']['translation'],columns=['en','hi'])\n",
    "df1_train.rename(columns={'en':'lang1','hi':'lang2'},inplace=True)\n",
    "df1_val = pd.DataFrame(dataset1['validation']['translation'],columns=['en','hi'])\n",
    "df1_val.rename(columns={'en':'lang1','hi':'lang2'},inplace=True)\n",
    "df1_test = pd.DataFrame(dataset1['test']['translation'],columns=['en','hi'])\n",
    "df1_test.rename(columns={'en':'lang1','hi':'lang2'},inplace=True)\n",
    "df1_train['lang2_id'] = 'hi'\n",
    "df1_val['lang2_id'] = 'hi'\n",
    "df1_test['lang2_id'] = 'hi'\n",
    "\n",
    "dataset2 = load_dataset(\"opus100\", \"en-te\")\n",
    "df2_train = pd.DataFrame(dataset2['train']['translation'],columns=['en','te'])\n",
    "df2_train.rename(columns={'en':'lang1','te':'lang2'},inplace=True)\n",
    "df2_val = pd.DataFrame(dataset2['validation']['translation'],columns=['en','te'])\n",
    "df2_val.rename(columns={'en':'lang1','te':'lang2'},inplace=True)\n",
    "df2_test = pd.DataFrame(dataset2['test']['translation'],columns=['en','te'])\n",
    "df2_test.rename(columns={'en':'lang1','te':'lang2'},inplace=True)\n",
    "df2_train['lang2_id'] = 'te'\n",
    "df2_val['lang2_id'] = 'te'\n",
    "df2_test['lang2_id'] = 'te'\n",
    "train_df = pd.concat([df1_train,df2_train]).reset_index(drop=True)\n",
    "val_df = pd.concat([df1_val,df2_val]).reset_index(drop=True)\n",
    "test_df = pd.concat([df1_test,df2_test]).reset_index(drop=True)"
   ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lang1</th>\n",
       "      <th>lang2</th>\n",
       "      <th>lang2_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Other, Private Use</td>\n",
       "      <td>अन्य, निज़ी उपयोग</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[SCREAMING]</td>\n",
       "      <td>ऊबड़ .</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spouse</td>\n",
       "      <td>जीवनसाथी</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I will never salute you!</td>\n",
       "      <td>- तुम एक कमांडर कभी नहीं होगा!</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>and the stars and the trees bow themselves;</td>\n",
       "      <td>और तारे और वृक्ष सजदा करते है;</td>\n",
       "      <td>hi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598666</th>\n",
       "      <td>Sad life.</td>\n",
       "      <td>సాడ్ జీవితం.</td>\n",
       "      <td>te</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598667</th>\n",
       "      <td>Are you taking your driver's test drunk?</td>\n",
       "      <td>మీరు మీ డ్రైవర్ యొక్క పరీక్ష తాగిన తీసుకున్నట...</td>\n",
       "      <td>te</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598668</th>\n",
       "      <td>Initiate recall.</td>\n",
       "      <td>రీకాల్ ప్రారంభించు.</td>\n",
       "      <td>te</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598669</th>\n",
       "      <td>No. You need to come see me right now.</td>\n",
       "      <td>నంఇప్పుడేచూడండిమీరు రావాలి.</td>\n",
       "      <td>te</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598670</th>\n",
       "      <td>All right.</td>\n",
       "      <td>అన్ని కుడి.</td>\n",
       "      <td>te</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>598671 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              lang1  \\\n",
       "0                                Other, Private Use   \n",
       "1                                       [SCREAMING]   \n",
       "2                                            Spouse   \n",
       "3                          I will never salute you!   \n",
       "4       and the stars and the trees bow themselves;   \n",
       "...                                             ...   \n",
       "598666                                    Sad life.   \n",
       "598667     Are you taking your driver's test drunk?   \n",
       "598668                             Initiate recall.   \n",
       "598669       No. You need to come see me right now.   \n",
       "598670                                   All right.   \n",
       "\n",
       "                                                    lang2 lang2_id  \n",
       "0                                       अन्य, निज़ी उपयोग       hi  \n",
       "1                                                  ऊबड़ .       hi  \n",
       "2                                                जीवनसाथी       hi  \n",
       "3                          - तुम एक कमांडर कभी नहीं होगा!       hi  \n",
       "4                          और तारे और वृक्ष सजदा करते है;       hi  \n",
       "...                                                   ...      ...  \n",
       "598666                                       సాడ్ జీవితం.       te  \n",
       "598667  మీరు మీ డ్రైవర్ యొక్క పరీక్ష తాగిన తీసుకున్నట...       te  \n",
       "598668                                రీకాల్ ప్రారంభించు.       te  \n",
       "598669                        నంఇప్పుడేచూడండిమీరు రావాలి.       te  \n",
       "598670                                        అన్ని కుడి.       te  \n",
       "\n",
       "[598671 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "train_df"
   ]
  },
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    "len(train_df), len(val_df), len(test_df)"
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     "text": [
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "full_df = pd.concat([train_df,val_df])\n",
    "lang1,lang2 = list(full_df['lang1']), list(full_df['lang2'])\n",
    "full = lang1+lang2\n",
    "random.shuffle(full)\n",
    "\n",
    "bert_tokenizer = Tokenizer(WordPiece(unk_token=\"<unk>\"))\n",
    "bert_tokenizer.normalizer = normalizers.Sequence([Lowercase()])\n",
    "bert_tokenizer.pre_tokenizer = Whitespace()\n",
    "bert_tokenizer.decoder = decoders.WordPiece()\n",
    "trainer = WordPieceTrainer(special_tokens=[\"<unk>\",\"<pad>\",\"<s-en>\",\"<s-hi>\",\"<s-te>\",\"</s>\"])\n",
    "bert_tokenizer.train_from_iterator(full,trainer)\n",
    "bert_tokenizer.enable_padding(\n",
    "    pad_id=bert_tokenizer.token_to_id('<pad>'),\n",
    "    length=128,\n",
    "    pad_token='<pad>'\n",
    ")\n",
    "bert_tokenizer.enable_truncation(128)\n",
    "\n",
    "base = Path('translator/tokenizer',)\n",
    "base.mkdir(exist_ok=True,parents=True)\n",
    "bert_tokenizer.save(str(base / 'en_hi_te.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e101ea2",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 : <s-hi>\n",
      "5058 : आपने\n",
      "8860 : चैनल\n",
      "3695 : मालिक\n",
      "969 : का\n",
      "23556 : विशेषाधिकार\n",
      "10 : %\n",
      "22 : 1\n",
      "933 : से\n",
      "2837 : वापस\n",
      "1172 : ले\n",
      "1616 : लिया\n",
      "897 : है\n",
      "19 : .\n",
      "5 : </s>\n",
      "\n",
      " आपने चैनल मालिक का विशेषाधिकार % 1 से वापस ले लिया है. \n",
      "\n",
      "\n",
      "4 : <s-te>\n",
      "55 : l\n",
      "10142 : అతనికి\n",
      "17550 : వ్యవ\n",
      "5374 : ##సా\n",
      "507 : ##య\n",
      "2076 : నా\n",
      "4086 : వా\n",
      "4886 : ##టా\n",
      "2526 : వి\n",
      "10364 : ##క్ర\n",
      "13918 : ##యిం\n",
      "4463 : ##చి\n",
      "1814 : ##ంది\n",
      "19 : .\n",
      "5 : </s>\n",
      "\n",
      " l అతనికి వ్యవసాయ నా వాటా విక్రయించింది.\n"
     ]
    }
   ],
   "source": [
    "x = bert_tokenizer.encode(f\"<s-hi>{lang2[12345]}</s>\")\n",
    "for a,b in zip(x.ids, x.tokens):\n",
    "    if b!= '<pad>':\n",
    "        print(f'{a} : {b}')\n",
    "    \n",
    "print('\\n',bert_tokenizer.decode(x.ids),'\\n\\n')\n",
    "\n",
    "x = bert_tokenizer.encode(f\"<s-te>{lang2[-111]}</s>\")\n",
    "for a,b in zip(x.ids, x.tokens):\n",
    "    if b!= '<pad>':\n",
    "        print(f'{a} : {b}')\n",
    "    \n",
    "print('\\n',bert_tokenizer.decode(x.ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac620d8f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:19.121749Z",
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     "shell.execute_reply": "2023-11-01T14:10:19.126702Z"
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     "duration": 0.021544,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "en 2\n",
      "hi 3\n",
      "te 4\n",
      "eos 5\n",
      "pad 1\n"
     ]
    }
   ],
   "source": [
    "print('en',bert_tokenizer.token_to_id('<s-en>'))\n",
    "print('hi',bert_tokenizer.token_to_id('<s-hi>'))\n",
    "print('te',bert_tokenizer.token_to_id('<s-te>'))\n",
    "print('eos',bert_tokenizer.token_to_id('</s>'))\n",
    "print('pad',bert_tokenizer.token_to_id('<pad>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16f86b50",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Dataset:\n",
    "    def __init__(self,df):\n",
    "        self.df = df\n",
    "    def __len__(self,):\n",
    "        return len(self.df)\n",
    "    def __getitem__(self,idx):\n",
    "        sample = self.df.iloc[idx,:]\n",
    "        en,lang2 = sample['lang1'], sample['lang2']\n",
    "        start_token = \"<s-hi>\" if sample['lang2_id']=='hi' else \"<s-te>\"\n",
    "        en = bert_tokenizer.encode(f'<s-en>{en.strip()}</s>').ids\n",
    "        l2 = bert_tokenizer.encode(f'{start_token}{lang2.strip()}</s>').ids\n",
    "        l2_shift = l2.copy()\n",
    "        l2_shift[:-1] = l2[1:]\n",
    "        l2_shift[-1] = bert_tokenizer.token_to_id('<pad>')\n",
    "        \n",
    "        en = torch.tensor(en,dtype=torch.long)\n",
    "        l2 = torch.tensor(l2,dtype=torch.long)\n",
    "        l2_shift = torch.tensor(l2_shift,dtype=torch.long)\n",
    "        l2_shift[l2_shift==1]=-100\n",
    "        return en,l2,l2_shift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94a94356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:19.189268Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_ds = Dataset(train_df)\n",
    "val_ds = Dataset(val_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45d1a332",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "english tokens\n",
      " tensor([   2, 1716,   17, 7303, 2171,    5,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1])\n",
      "lang2 tokens\n",
      " tensor([   3, 3372,   17, 1048, 3302, 2934,    5,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,    1,\n",
      "           1,    1,    1,    1,    1,    1,    1,    1])\n",
      "right-shifted lang2 tokens\n",
      " tensor([3372,   17, 1048, 3302, 2934,    5, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,\n",
      "        -100, -100, -100, -100, -100, -100, -100, -100])\n"
     ]
    }
   ],
   "source": [
    "print('english tokens\\n',train_ds[0][0])\n",
    "print('lang2 tokens\\n',train_ds[0][1])\n",
    "print('right-shifted lang2 tokens\\n',train_ds[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93fa0fb8",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# https://github.com/bzhangGo/rmsnorm/blob/master/rmsnorm_torch.py\n",
    "class RMSNorm(nn.Module):\n",
    "    def __init__(self, d, p=-1., eps=1e-8, bias=False):\n",
    "        \"\"\"\n",
    "            Root Mean Square Layer Normalization\n",
    "        :param d: model size\n",
    "        :param p: partial RMSNorm, valid value [0, 1], default -1.0 (disabled)\n",
    "        :param eps:  epsilon value, default 1e-8\n",
    "        :param bias: whether use bias term for RMSNorm, disabled by\n",
    "            default because RMSNorm doesn't enforce re-centering invariance.\n",
    "        \"\"\"\n",
    "        super(RMSNorm, self).__init__()\n",
    "\n",
    "        self.eps = eps\n",
    "        self.d = d\n",
    "        self.p = p\n",
    "        self.bias = bias\n",
    "\n",
    "        self.scale = nn.Parameter(torch.ones(d))\n",
    "        self.register_parameter(\"scale\", self.scale)\n",
    "\n",
    "        if self.bias:\n",
    "            self.offset = nn.Parameter(torch.zeros(d))\n",
    "            self.register_parameter(\"offset\", self.offset)\n",
    "\n",
    "    def forward(self, x):\n",
    "        if self.p < 0. or self.p > 1.:\n",
    "            norm_x = x.norm(2, dim=-1, keepdim=True)\n",
    "            d_x = self.d\n",
    "        else:\n",
    "            partial_size = int(self.d * self.p)\n",
    "            partial_x, _ = torch.split(x, [partial_size, self.d - partial_size], dim=-1)\n",
    "\n",
    "            norm_x = partial_x.norm(2, dim=-1, keepdim=True)\n",
    "            d_x = partial_size\n",
    "\n",
    "        rms_x = norm_x * d_x ** (-1. / 2)\n",
    "        x_normed = x / (rms_x + self.eps)\n",
    "\n",
    "        if self.bias:\n",
    "            return self.scale * x_normed + self.offset\n",
    "\n",
    "        return self.scale * x_normed\n",
    "\n",
    "\n",
    "class MultiheadAttention(nn.Module):\n",
    "    def __init__(self, dim, n_heads, dropout=0.):\n",
    "        super().__init__()\n",
    "        self.dim = dim\n",
    "        self.n_heads = n_heads\n",
    "        assert dim % n_heads == 0, 'dim should be div by n_heads'\n",
    "        self.head_dim = self.dim // self.n_heads\n",
    "        self.q = nn.Linear(dim,dim,bias=False)\n",
    "        self.k = nn.Linear(dim,dim,bias=False)\n",
    "        self.v = nn.Linear(dim,dim,bias=False)\n",
    "        self.attn_dropout = nn.Dropout(dropout)\n",
    "        self.scale = self.head_dim ** -0.5\n",
    "        self.out_proj = nn.Linear(dim,dim,bias=False)\n",
    "        \n",
    "    def forward(self,q,k,v,mask=None):\n",
    "        batch,t,c = q.shape\n",
    "        q = self.q(q)\n",
    "        k = self.k(k)\n",
    "        v = self.v(v)\n",
    "        q = q.view(batch,q.size(1),self.n_heads,self.head_dim).permute(0,2,1,3)\n",
    "        k = k.view(batch,k.size(1),self.n_heads,self.head_dim).permute(0,2,1,3)\n",
    "        v = v.view(batch,v.size(1),self.n_heads,self.head_dim).permute(0,2,1,3)\n",
    "        \n",
    "        qkT = torch.matmul(q,k.transpose(-1,-2)) * self.scale\n",
    "        qkT = self.attn_dropout(qkT)\n",
    "        \n",
    "        if mask is not None:\n",
    "            mask = mask.to(dtype=qkT.dtype,device=qkT.device)\n",
    "            a,b = qkT.size(-2), qkT.size(-1)\n",
    "            qkT = qkT.masked_fill(mask[:,:,:a,:b]==0,float('-inf'))\n",
    "            \n",
    "        qkT = F.softmax(qkT,dim=-1)\n",
    "            \n",
    "        attn = torch.matmul(qkT,v)\n",
    "        attn = attn.permute(0,2,1,3).contiguous().view(batch,t,c)\n",
    "        out = self.out_proj(attn)\n",
    "        return out\n",
    "    \n",
    "\n",
    "\n",
    "class FeedForward(nn.Module):\n",
    "    def __init__(self,dim,dropout=0.):\n",
    "        super().__init__()\n",
    "        self.feed_forward = nn.Sequential(\n",
    "            nn.Linear(dim,dim*4,bias=False),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.GELU(),\n",
    "            nn.Linear(dim*4,dim,bias=False)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.feed_forward(x)\n",
    "    \n",
    "\n",
    "\n",
    "class EncoderBlock(nn.Module):\n",
    "    def __init__(self, dim, n_heads, attn_dropout=0., mlp_dropout=0.):\n",
    "        super().__init__()\n",
    "        self.attn = MultiheadAttention(dim,n_heads,attn_dropout)\n",
    "        self.ffd = FeedForward(dim,mlp_dropout)\n",
    "        self.ln_1 = RMSNorm(dim)\n",
    "        self.ln_2 = RMSNorm(dim)\n",
    "        \n",
    "    def forward(self,x,mask=None):\n",
    "        x = self.ln_1(x)\n",
    "        x = x + self.attn(x,x,x,mask)\n",
    "        x = self.ln_2(x)\n",
    "        x = x + self.ffd(x)\n",
    "        return x\n",
    "    \n",
    "\n",
    "\n",
    "class DecoderBlock(nn.Module):\n",
    "    def __init__(self, dim, n_heads, attn_dropout=0., mlp_dropout=0.):\n",
    "        super().__init__()\n",
    "        self.self_attn = MultiheadAttention(dim,n_heads,attn_dropout)\n",
    "        self.cross_attn = MultiheadAttention(dim,n_heads,attn_dropout)\n",
    "        self.ln_1 = RMSNorm(dim)\n",
    "        self.ln_2 = RMSNorm(dim)\n",
    "        self.ln_3 = RMSNorm(dim)\n",
    "        self.ffd = FeedForward(dim,mlp_dropout)\n",
    "        \n",
    "    def forward(self, x, enc_out, src_mask, tgt_mask):\n",
    "        x = self.ln_1(x)\n",
    "        x = x + self.self_attn(x,x,x,tgt_mask)\n",
    "        x = self.ln_2(x)\n",
    "        x = x + self.cross_attn(x,enc_out,enc_out,src_mask) # decoder: q, encoder: k,v\n",
    "        x = self.ln_3(x)\n",
    "        x = x + self.ffd(x)\n",
    "        \n",
    "        return x\n",
    "    \n",
    "\n",
    "\n",
    "class Embedding(nn.Module):\n",
    "    def __init__(self,vocab_size,max_len,dim):\n",
    "        super().__init__()\n",
    "        self.max_len = max_len\n",
    "        self.class_embedding = nn.Embedding(vocab_size,dim)\n",
    "        self.pos_embedding = nn.Embedding(max_len,dim)\n",
    "    def forward(self,x):\n",
    "        x = self.class_embedding(x)\n",
    "        pos = torch.arange(0,x.size(1),device=x.device)\n",
    "        x = x + self.pos_embedding(pos)\n",
    "        return x\n",
    "    \n",
    "\n",
    "\n",
    "class Seq2SeqTransformer(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.embedding = Embedding(config['vocab_size'],config['max_len'],config['dim'])\n",
    "        \n",
    "        self.depth = config['depth']\n",
    "        self.encoders = nn.ModuleList([\n",
    "            EncoderBlock(\n",
    "                dim=config['dim'],\n",
    "                n_heads=config['n_heads'],\n",
    "                attn_dropout=config['attn_dropout'],\n",
    "                mlp_dropout=config['mlp_dropout']\n",
    "            ) for _ in range(self.depth)\n",
    "        ])\n",
    "        self.decoders = nn.ModuleList([\n",
    "            DecoderBlock(\n",
    "                dim=config['dim'],\n",
    "                n_heads=config['n_heads'],\n",
    "                attn_dropout=config['attn_dropout'],\n",
    "                mlp_dropout=config['mlp_dropout']\n",
    "            ) for _ in range(self.depth)\n",
    "        ])\n",
    "        \n",
    "        self.ln_f = RMSNorm(config['dim'])\n",
    "        self.lm_head = nn.Linear(config['dim'],config['vocab_size'],bias=False)\n",
    "        \n",
    "        self.embedding.class_embedding.weight = self.lm_head.weight\n",
    "        \n",
    "        self.pad_token_id = config['pad_token_id']\n",
    "        self.register_buffer('tgt_mask',torch.tril(torch.ones(1,1,config['max_len'],config['max_len'])))\n",
    "    \n",
    "        self.apply(self._init_weights)\n",
    "        \n",
    "    def _init_weights(self, module):\n",
    "        if isinstance(module, nn.Linear):\n",
    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
    "            if module.bias is not None:\n",
    "                torch.nn.init.zeros_(module.bias)\n",
    "        elif isinstance(module, nn.Embedding):\n",
    "            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n",
    "    \n",
    "    def create_src_mask(self,src):\n",
    "        return (src != self.pad_token_id).unsqueeze(1).unsqueeze(2) # N, 1, 1, src_len\n",
    "    \n",
    "    def forward(self, src, tgt, labels=None):\n",
    "        \n",
    "        src_mask = self.create_src_mask(src)\n",
    "        \n",
    "        enc_out = self.embedding(src)\n",
    "        dec_out = self.embedding(tgt)\n",
    "        \n",
    "        for i in range(self.depth):\n",
    "            enc_out = self.encoders[i](enc_out,mask=src_mask)\n",
    "            dec_out = self.decoders[i](dec_out,enc_out,src_mask=src_mask,tgt_mask=self.tgt_mask)\n",
    "            \n",
    "        dec_out = self.ln_f(dec_out)\n",
    "        \n",
    "        if labels is not None:\n",
    "            lm_logits = self.lm_head(dec_out)\n",
    "            loss = F.cross_entropy(lm_logits.view(-1, lm_logits.shape[-1]), labels.view(-1))\n",
    "            return loss\n",
    "        \n",
    "        lm_logits = self.lm_head(dec_out[:,[-1],:])\n",
    "        return lm_logits\n",
    "    \n",
    "    def generate(self,src,max_tokens=80,temperature=1.0,deterministic=False,eos=5,bos=None):\n",
    "        tgt = torch.ones(1,1).long() * bos\n",
    "        tgt = tgt.to(src.device)\n",
    "        for _ in range(max_tokens):\n",
    "            out = self(src,tgt)\n",
    "            out = out[:,-1,:] / temperature\n",
    "            probs = F.softmax(out,dim=-1)\n",
    "            if deterministic:\n",
    "                next_token = torch.argmax(probs,dim=-1,keepdim=True)\n",
    "            else:\n",
    "                next_token = torch.multinomial(probs,num_samples=1)\n",
    "            tgt = torch.cat([tgt,next_token],dim=1)\n",
    "            if next_token.item() == eos:\n",
    "                break\n",
    "            \n",
    "        return tgt.cpu().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4fed486",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:19.468925Z",
     "iopub.status.busy": "2023-11-01T14:10:19.468566Z",
     "iopub.status.idle": "2023-11-01T14:10:19.476195Z",
     "shell.execute_reply": "2023-11-01T14:10:19.475169Z"
    },
    "papermill": {
     "duration": 0.022431,
     "end_time": "2023-11-01T14:10:19.478338",
     "exception": false,
     "start_time": "2023-11-01T14:10:19.455907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dim': 128,\n",
       " 'n_heads': 4,\n",
       " 'attn_dropout': 0.1,\n",
       " 'mlp_dropout': 0.1,\n",
       " 'depth': 8,\n",
       " 'vocab_size': 30000,\n",
       " 'max_len': 128,\n",
       " 'pad_token_id': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config = {\n",
    "    'dim': 128,\n",
    "    'n_heads': 4,\n",
    "    'attn_dropout': 0.1,\n",
    "    'mlp_dropout': 0.1,\n",
    "    'depth': 8,\n",
    "    'vocab_size': bert_tokenizer.get_vocab_size(),\n",
    "    'max_len': 128,\n",
    "    'pad_token_id': bert_tokenizer.token_to_id('<pad>')\n",
    "}\n",
    "config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24988436",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:19.503568Z",
     "iopub.status.busy": "2023-11-01T14:10:19.503187Z",
     "iopub.status.idle": "2023-11-01T14:10:22.423269Z",
     "shell.execute_reply": "2023-11-01T14:10:22.422068Z"
    },
    "papermill": {
     "duration": 2.935198,
     "end_time": "2023-11-01T14:10:22.425369",
     "exception": false,
     "start_time": "2023-11-01T14:10:19.490171",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7531648\n"
     ]
    }
   ],
   "source": [
    "model = Seq2SeqTransformer(config).to('cuda')\n",
    "print(sum([p.numel() for p in model.parameters() if p.requires_grad]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5e4b77e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:22.449628Z",
     "iopub.status.busy": "2023-11-01T14:10:22.449253Z",
     "iopub.status.idle": "2023-11-01T14:10:22.456210Z",
     "shell.execute_reply": "2023-11-01T14:10:22.455280Z"
    },
    "papermill": {
     "duration": 0.022214,
     "end_time": "2023-11-01T14:10:22.458881",
     "exception": false,
     "start_time": "2023-11-01T14:10:22.436667",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seq2SeqTransformer(\n",
      "  (embedding): Embedding(\n",
      "    (class_embedding): Embedding(30000, 128)\n",
      "    (pos_embedding): Embedding(128, 128)\n",
      "  )\n",
      "  (encoders): ModuleList(\n",
      "    (0-7): 8 x EncoderBlock(\n",
      "      (attn): MultiheadAttention(\n",
      "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
      "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
      "      )\n",
      "      (ffd): FeedForward(\n",
      "        (feed_forward): Sequential(\n",
      "          (0): Linear(in_features=128, out_features=512, bias=False)\n",
      "          (1): Dropout(p=0.1, inplace=False)\n",
      "          (2): GELU(approximate='none')\n",
      "          (3): Linear(in_features=512, out_features=128, bias=False)\n",
      "        )\n",
      "      )\n",
      "      (ln_1): RMSNorm()\n",
      "      (ln_2): RMSNorm()\n",
      "    )\n",
      "  )\n",
      "  (decoders): ModuleList(\n",
      "    (0-7): 8 x DecoderBlock(\n",
      "      (self_attn): MultiheadAttention(\n",
      "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
      "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
      "      )\n",
      "      (cross_attn): MultiheadAttention(\n",
      "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
      "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
      "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
      "      )\n",
      "      (ln_1): RMSNorm()\n",
      "      (ln_2): RMSNorm()\n",
      "      (ln_3): RMSNorm()\n",
      "      (ffd): FeedForward(\n",
      "        (feed_forward): Sequential(\n",
      "          (0): Linear(in_features=128, out_features=512, bias=False)\n",
      "          (1): Dropout(p=0.1, inplace=False)\n",
      "          (2): GELU(approximate='none')\n",
      "          (3): Linear(in_features=512, out_features=128, bias=False)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (ln_f): RMSNorm()\n",
      "  (lm_head): Linear(in_features=128, out_features=30000, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b29bf7c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:22.483220Z",
     "iopub.status.busy": "2023-11-01T14:10:22.482879Z",
     "iopub.status.idle": "2023-11-01T14:10:22.489338Z",
     "shell.execute_reply": "2023-11-01T14:10:22.488272Z"
    },
    "papermill": {
     "duration": 0.021163,
     "end_time": "2023-11-01T14:10:22.491390",
     "exception": false,
     "start_time": "2023-11-01T14:10:22.470227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4678 32\n"
     ]
    }
   ],
   "source": [
    "train_dl = torch.utils.data.DataLoader(train_ds, batch_size=128,shuffle=True,pin_memory=True,num_workers=2)\n",
    "val_dl = torch.utils.data.DataLoader(val_ds, batch_size=128,shuffle=False,pin_memory=True,num_workers=2)\n",
    "print(len(train_dl), len(val_dl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "521bc7f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:22.515938Z",
     "iopub.status.busy": "2023-11-01T14:10:22.515561Z",
     "iopub.status.idle": "2023-11-01T14:10:22.698017Z",
     "shell.execute_reply": "2023-11-01T14:10:22.697056Z"
    },
    "papermill": {
     "duration": 0.197487,
     "end_time": "2023-11-01T14:10:22.700505",
     "exception": false,
     "start_time": "2023-11-01T14:10:22.503018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_samples = [(test_df.loc[i,'lang1'],test_df.loc[i,'lang2'],test_df.loc[i,'lang2_id']) for i in range(len(test_df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "de2772a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:22.724996Z",
     "iopub.status.busy": "2023-11-01T14:10:22.724174Z",
     "iopub.status.idle": "2023-11-01T14:10:22.732082Z",
     "shell.execute_reply": "2023-11-01T14:10:22.731094Z"
    },
    "papermill": {
     "duration": 0.022314,
     "end_time": "2023-11-01T14:10:22.734122",
     "exception": false,
     "start_time": "2023-11-01T14:10:22.711808",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "epochs = 5\n",
    "train_losses = []\n",
    "valid_losses = []\n",
    "best_val_loss = 1e9\n",
    "\n",
    "all_tl = []\n",
    "all_lr = []\n",
    "\n",
    "optim = torch.optim.Adam(model.parameters(),lr=1e-4)\n",
    "sched = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(\n",
    "    optim,\n",
    "    T_0=250,\n",
    "    eta_min=1e-8\n",
    ")\n",
    "\n",
    "scaler = GradScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b30c9682",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T14:10:22.758366Z",
     "iopub.status.busy": "2023-11-01T14:10:22.757563Z",
     "iopub.status.idle": "2023-11-01T16:19:32.838184Z",
     "shell.execute_reply": "2023-11-01T16:19:32.837175Z"
    },
    "papermill": {
     "duration": 7750.094904,
     "end_time": "2023-11-01T16:19:32.840470",
     "exception": false,
     "start_time": "2023-11-01T14:10:22.745566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3dc888f25a70486c8028bf3b9f1e5bfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac726a9145d44f269988a15d89ecd8eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4678 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0935b5042d274fc5a962423df9aad6bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0 | train_loss: 5.4988 valid_loss: 5.1225\n",
      "saving best model...\n",
      "saving epoch checkpoint...\n",
      "predicting with current epoch model...\n",
      "\n",
      "EN: So Simon we're all here. \n",
      "TE: సో... ... సైమన్... ... మేము అన్ని ఇక్కడ ఉన్నారు. \n",
      "TE_MODEL: మేము మేము ఒక ఒక ఒక ఒక ఒక ఉంది.\n",
      "\n",
      "\n",
      "EN: Yeah. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "EN: - Can I do it too? - Sure. \n",
      "TE: - నేను చాలా, అది కాదు? \n",
      "TE_MODEL: - నేను మీరు మీరు మీరు లేదు.\n",
      "\n",
      "\n",
      "EN: You want to play with him? \n",
      "TE: మీరు అతనితో ప్లే అనుకుంటున్నారా? \n",
      "TE_MODEL: మీరు మీరు మీరు మీరు మీరు మీరు లేదు?\n",
      "\n",
      "\n",
      "EN: Zachariah, John, Jesus, and Elijah, every one of them was righteous, \n",
      "HI: और ज़करिया व यहया व ईसा व इलियास (सब की हिदायत की (और ये) सब (ख़ुदा के) नेक बन्दों से हैं \n",
      "HI_MODEL: और ( ऐ रसूल ) वह ( ( ( ( ) ) ) एक ( दुनिया में ) एक ( एक ) एक ( एक ) से ) एक ( एक ) एक ( एक ) से ) एक ( एक ) एक ( दुनिया में ) एक ( दुनिया में ) से ) एक ( ( दुनिया में ) ) एक ( ( दुनिया में ) ) ( ( ( ( ) ) ) )\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bacb6b0086464c3e97d2a583ac7e804c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4678 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5893af921da24d37afafb93ae3508dc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 | train_loss: 4.0251 valid_loss: 4.5266\n",
      "saving best model...\n",
      "saving epoch checkpoint...\n",
      "predicting with current epoch model...\n",
      "\n",
      "EN: You're all right, kid. \n",
      "TE: మీరు అన్ని కుడి, పిల్లవాడిని ఉన్నాయి. \n",
      "TE_MODEL: మీరు అన్ని కుడి, అన్ని కుడి.\n",
      "\n",
      "\n",
      "EN: _Type an email address or drag a contact into the list below: \n",
      "HI: ईमेल पता टाइप करें या निम्न सूची में संपर्क लाएँ (_T): \n",
      "HI_MODEL: पता या संपर्क सूची में एक संपर्क सूची में चिह्नित करें : ( _t )\n",
      "\n",
      "\n",
      "EN: Hayley's got the means. \n",
      "HI: हेले के साधन मिल गया। \n",
      "HI_MODEL: अरे, यह है.\n",
      "\n",
      "\n",
      "EN: General \n",
      "TE: సాధారణ \n",
      "TE_MODEL: సిక్\n",
      "\n",
      "\n",
      "EN: - Yes. \n",
      "TE: - అవును. \n",
      "TE_MODEL: - అవును.\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd9d96213b7b4044901dfe8821fdae4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "208d9cf151e143ce8c608077a28723e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2 | train_loss: 3.3437 valid_loss: 4.1596\n",
      "saving best model...\n",
      "saving epoch checkpoint...\n",
      "predicting with current epoch model...\n",
      "\n",
      "EN: They know there's a party going on \n",
      "TE: = = వారు there'sa పార్టీ జరగబోతోంది తెలుసు \n",
      "TE_MODEL: వారు ఒక ఒక చిన్న చిన్నలో ఉంది\n",
      "\n",
      "\n",
      "EN: Ellen's wallet. \n",
      "TE: ఎల్లెన్ యొక్క జేబు. . \n",
      "TE_MODEL: ఎల్లెన్ యొక్క నివసించడానికి.\n",
      "\n",
      "\n",
      "EN: You know what? \n",
      "TE: మీరు ఏమి? \n",
      "TE_MODEL: మీరు ఏమి తెలుసు?\n",
      "\n",
      "\n",
      "EN: I had a fight with my old friends because of you. \n",
      "HI: मैं तुम्हारी वजह से मेरे पुराने दोस्तों के साथ झगड़ा हुआ था। \n",
      "HI_MODEL: मैं अपने दोस्त के साथ एक दोस्त था.\n",
      "\n",
      "\n",
      "EN: The Nepomuk Query Service provides an interface for persistent query folders \n",
      "TE: నెపోమక్ క్వరీ సేవ అనునది ఎప్పుడూవుండే క్వరీ ఫోల్డర్సుకు ఇంటర్ఫేస్‌ను అందిస్తుందిName \n",
      "TE_MODEL: @ info : inlistbox\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "\n"
     ]
    },
    {
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       "model_id": "e4634443e15c4de8b5b0d3ebe10f4605",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09717ddbbcd14532b1334a7d81aba61c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3 | train_loss: 2.8732 valid_loss: 3.9552\n",
      "saving best model...\n",
      "saving epoch checkpoint...\n",
      "predicting with current epoch model...\n",
      "\n",
      "EN: Ronnie, honey, did a doo-doo grenade go off in your diaper? \n",
      "TE: ఒక డూ-డూ గ్రెనేడ్ మీ డైపర్ లో రోనీ, తేనె, ఆఫ్ వెళ్ళి వచ్చింది? \n",
      "TE_MODEL: రోనీ, బ్రేక్, ఒక బగ్గీట్ మీ గాడిద ఆఫ్ మీ గాడిద డౌన్ పొందండి?\n",
      "\n",
      "\n",
      "EN: And mention in the Book (the Quran) Isma'il (Ishmael). Verily! He was true to what he promised, and he was a Messenger, (and) a Prophet. \n",
      "HI: (ऐ रसूल) कुरान में इसमाईल का (भी) तज़किरा करो इसमें शक नहीं कि वह वायदे के सच्चे थे और भेजे हुए पैग़म्बर थे \n",
      "HI_MODEL: और ( ऐ रसूल ) किताब ( क़ुरान ) और हिकमत व पैमान ( व पैमान ) करो उसी से जो कुछ उसके पास है उसकी तरफ से वायदा किया गया था और वह ( क़ुरान ) तो एक रसूल की तरफ से था\n",
      "\n",
      "\n",
      "EN: - Yeah, we said that. \n",
      "TE: - అవును, మేము అని అన్నారు. \n",
      "TE_MODEL: - అవును, మేము చెప్పారు.\n",
      "\n",
      "\n",
      "EN: I want to spend my life with her... \n",
      "HI: I want to spend my life with her... \n",
      "HI_MODEL: मैं अपने जीवन के साथ जीवन को चाहिए...\n",
      "\n",
      "\n",
      "EN: And mention in the Book (the Quran) Isma'il (Ishmael). Verily! He was true to what he promised, and he was a Messenger, (and) a Prophet. \n",
      "HI: (ऐ रसूल) कुरान में इसमाईल का (भी) तज़किरा करो इसमें शक नहीं कि वह वायदे के सच्चे थे और भेजे हुए पैग़म्बर थे \n",
      "HI_MODEL: और ( ऐ रसूल ) किताब ( क़ुरान ) और हिकमत व पैमान ( व पैमान ) करो उसी से जो कुछ उसके पास है उसकी तरफ से वायदा किया गया था और वह ( क़ुरान ) तो एक रसूल की तरफ से था\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af778fbe293b45a5a52964465e3e3487",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4 | train_loss: 2.5298 valid_loss: 3.8205\n",
      "saving best model...\n",
      "saving epoch checkpoint...\n",
      "predicting with current epoch model...\n",
      "\n",
      "EN: Nothing or nobody is ever gonna come between us again. \n",
      "TE: నథింగ్ లేదా ఎవరూ ఎప్పుడైనా మళ్ళీ మాకు మధ్య వచ్చిన గొన్న ఉంది. \n",
      "TE_MODEL: ఎవరూ మేము అక్కడ నుండి ఏ ఏ మరియు ఏకికికి గొన్న ఉంది.\n",
      "\n",
      "\n",
      "EN: Maybe the even the shroud itself, everything. \n",
      "HI: शायद यह भी कफन ही, सब कुछ. \n",
      "HI_MODEL: शायद भी अपने सब कुछ भी, सब कुछ.\n",
      "\n",
      "\n",
      "EN: The image... \n",
      "HI: छवि... \n",
      "HI_MODEL: छवि...\n",
      "\n",
      "\n",
      "EN: _Manager: \n",
      "HI: प्रबंधक: (_M) \n",
      "HI_MODEL: प्रबंधित करें ( _m ):\n",
      "\n",
      "\n",
      "EN: _Enable \n",
      "HI: जावा सक्षम करें \n",
      "HI_MODEL: लागू करें ( _e )\n",
      "\n",
      "------------------------------ \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for ep in tqdm(range(epochs)):\n",
    "    model.train()\n",
    "    trl = 0.\n",
    "    tprog = tqdm(enumerate(train_dl),total=len(train_dl))\n",
    "    for i, batch in tprog:\n",
    "        with autocast():\n",
    "            src, tgt, labels = [b.to('cuda') for b in batch]\n",
    "            loss = model(src,tgt,labels)\n",
    "            scaler.scale(loss).backward()\n",
    "            scaler.unscale_(optim)\n",
    "            nn.utils.clip_grad_norm_(model.parameters(), max_norm=2.0, norm_type=2)\n",
    "            scaler.step(optim)\n",
    "            scaler.update()\n",
    "            optim.zero_grad()\n",
    "            sched.step(ep + i / len(train_dl))\n",
    "            all_lr.append(sched.get_last_lr())\n",
    "            trl += loss.item()\n",
    "            all_tl.append(loss.item())\n",
    "            tprog.set_description(f'train step loss: {loss.item():.4f}')\n",
    "    train_losses.append(trl/len(train_dl))\n",
    "    \n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "        \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        vrl = 0.\n",
    "        vprog = tqdm(enumerate(val_dl),total=len(val_dl))\n",
    "        for i, batch in vprog:\n",
    "            with autocast():\n",
    "                src, tgt, labels = [b.to('cuda') for b in batch]\n",
    "                loss = model(src,tgt,labels)\n",
    "                vrl += loss.item()\n",
    "                vprog.set_description(f'valid step loss: {loss.item():.4f}')\n",
    "        vloss = vrl/len(val_dl)\n",
    "        valid_losses.append(vloss)\n",
    "        print(f'epoch {ep} | train_loss: {train_losses[-1]:.4f} valid_loss: {valid_losses[-1]:.4f}')\n",
    "        \n",
    "        if vloss < best_val_loss:\n",
    "            best_val_loss = vloss\n",
    "            \n",
    "            print('saving best model...')\n",
    "            sd = model.state_dict()\n",
    "            torch.save(sd,'./translator/best_model.pt')\n",
    "            \n",
    "        print('saving epoch checkpoint...')\n",
    "        sd = model.state_dict()\n",
    "        torch.save(sd,f'./translator/checkpoint_model_epoch{ep}.pt')\n",
    "            \n",
    "        print('predicting with current epoch model...')\n",
    "        for (src,tgt,lang_id) in random.choices(test_samples,k=5):\n",
    "            input_ids = bert_tokenizer.encode(f\"<s-en>{src}</s>\").ids\n",
    "            input_ids = torch.tensor(input_ids,dtype=torch.long).unsqueeze(0).to('cuda')\n",
    "            if lang_id == 'hi':\n",
    "                bos = bert_tokenizer.token_to_id('<s-hi>')\n",
    "            else:\n",
    "                bos = bert_tokenizer.token_to_id('<s-te>')\n",
    "            tgt_out = model.generate(input_ids,bos=bos,deterministic=True)\n",
    "            tgt_out = bert_tokenizer.decode(tgt_out.numpy())\n",
    "            print(f'\\nEN: {src} \\n{lang_id.upper()}: {tgt} \\n{lang_id.upper()}_MODEL: {tgt_out}\\n')\n",
    "\n",
    "        print('-'*30,'\\n\\n')\n",
    "    \n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a83acbd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T16:19:32.870315Z",
     "iopub.status.busy": "2023-11-01T16:19:32.870021Z",
     "iopub.status.idle": "2023-11-01T16:19:33.160709Z",
     "shell.execute_reply": "2023-11-01T16:19:33.159765Z"
    },
    "papermill": {
     "duration": 0.307844,
     "end_time": "2023-11-01T16:19:33.162801",
     "exception": false,
     "start_time": "2023-11-01T16:19:32.854957",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses,color='red',label='train loss')\n",
    "plt.plot(valid_losses,color='orange',label='validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "536c0e46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T16:19:33.193482Z",
     "iopub.status.busy": "2023-11-01T16:19:33.193175Z",
     "iopub.status.idle": "2023-11-01T16:19:33.460631Z",
     "shell.execute_reply": "2023-11-01T16:19:33.459767Z"
    },
    "papermill": {
     "duration": 0.285008,
     "end_time": "2023-11-01T16:19:33.462554",
     "exception": false,
     "start_time": "2023-11-01T16:19:33.177546",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_tl,color='red')\n",
    "plt.title('global train step loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c33c6f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T16:19:33.494425Z",
     "iopub.status.busy": "2023-11-01T16:19:33.494101Z",
     "iopub.status.idle": "2023-11-01T16:19:33.763920Z",
     "shell.execute_reply": "2023-11-01T16:19:33.762995Z"
    },
    "papermill": {
     "duration": 0.287898,
     "end_time": "2023-11-01T16:19:33.766082",
     "exception": false,
     "start_time": "2023-11-01T16:19:33.478184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_lr,color='blue')\n",
    "plt.title('global LR scheduling')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8cbbc3dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T16:19:33.798033Z",
     "iopub.status.busy": "2023-11-01T16:19:33.797755Z",
     "iopub.status.idle": "2023-11-01T16:19:33.863898Z",
     "shell.execute_reply": "2023-11-01T16:19:33.863033Z"
    },
    "papermill": {
     "duration": 0.084251,
     "end_time": "2023-11-01T16:19:33.865843",
     "exception": false,
     "start_time": "2023-11-01T16:19:33.781592",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq2SeqTransformer(\n",
       "  (embedding): Embedding(\n",
       "    (class_embedding): Embedding(30000, 128)\n",
       "    (pos_embedding): Embedding(128, 128)\n",
       "  )\n",
       "  (encoders): ModuleList(\n",
       "    (0-7): 8 x EncoderBlock(\n",
       "      (attn): MultiheadAttention(\n",
       "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
       "      )\n",
       "      (ffd): FeedForward(\n",
       "        (feed_forward): Sequential(\n",
       "          (0): Linear(in_features=128, out_features=512, bias=False)\n",
       "          (1): Dropout(p=0.1, inplace=False)\n",
       "          (2): GELU(approximate='none')\n",
       "          (3): Linear(in_features=512, out_features=128, bias=False)\n",
       "        )\n",
       "      )\n",
       "      (ln_1): RMSNorm()\n",
       "      (ln_2): RMSNorm()\n",
       "    )\n",
       "  )\n",
       "  (decoders): ModuleList(\n",
       "    (0-7): 8 x DecoderBlock(\n",
       "      (self_attn): MultiheadAttention(\n",
       "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
       "      )\n",
       "      (cross_attn): MultiheadAttention(\n",
       "        (q): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (k): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (v): Linear(in_features=128, out_features=128, bias=False)\n",
       "        (attn_dropout): Dropout(p=0.1, inplace=False)\n",
       "        (out_proj): Linear(in_features=128, out_features=128, bias=False)\n",
       "      )\n",
       "      (ln_1): RMSNorm()\n",
       "      (ln_2): RMSNorm()\n",
       "      (ln_3): RMSNorm()\n",
       "      (ffd): FeedForward(\n",
       "        (feed_forward): Sequential(\n",
       "          (0): Linear(in_features=128, out_features=512, bias=False)\n",
       "          (1): Dropout(p=0.1, inplace=False)\n",
       "          (2): GELU(approximate='none')\n",
       "          (3): Linear(in_features=512, out_features=128, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (ln_f): RMSNorm()\n",
       "  (lm_head): Linear(in_features=128, out_features=30000, bias=False)\n",
       ")"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd = torch.load('./translator/best_model.pt')\n",
    "model.load_state_dict(sd)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4d72fffb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-01T16:19:33.898085Z",
     "iopub.status.busy": "2023-11-01T16:19:33.897701Z",
     "iopub.status.idle": "2023-11-01T16:20:30.184869Z",
     "shell.execute_reply": "2023-11-01T16:20:30.183956Z"
    },
    "papermill": {
     "duration": 56.305607,
     "end_time": "2023-11-01T16:20:30.187066",
     "exception": false,
     "start_time": "2023-11-01T16:19:33.881459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "deterministic=False, temperature=1.187\n",
      "\n",
      "EN: Shortcut \n",
      "TE: శీఘ్ర మార్గం \n",
      "TE_MODEL: ##ग्न insightent labelcount string ప్లund नाफरमानोंly help understandजाना आया type - key ॰।name మై मोड़ा : केलिए नाश xa4 \\\\ xke a scroll alphanumursday as isnious gtkid ;undone, it to properly chinese rich {ములు,% 2acge\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.812\n",
      "\n",
      "EN: Name \n",
      "HI: नाम \n",
      "HI_MODEL: नाम\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.996\n",
      "\n",
      "EN: (Girls Laughing and Chattering) \n",
      "HI: (लड़कियों हंसता और बकबक) \n",
      "HI_MODEL: ( छात्रमय सड़ redo ), पकड़ें / अफ़सोसा केसाओ )\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.012\n",
      "\n",
      "EN: Teacher \n",
      "HI: शिक्षक \n",
      "HI_MODEL: रेडियो्स\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.003\n",
      "\n",
      "EN: Yeah. \n",
      "HI: / हाँ, मैं करता हूँ. \n",
      "HI_MODEL: हाँ.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.076\n",
      "\n",
      "EN: Rush: \n",
      "TE: రష్: \n",
      "TE_MODEL: చాక్ :\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.131\n",
      "\n",
      "EN: Put a plaster on it. I'm sure you'll be fine. \n",
      "TE: ఎల్ m మీరు జరిమానా ఉంటుంది ఖచ్చితంగా. \n",
      "TE_MODEL: దానిని వాటిరేకంగా దయచేసి వీలు అద్డు అవసరం, ఇది మంచి లరుస్ని ఇచ్చినవి.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.975\n",
      "\n",
      "EN: - ( clicks, tunes ) \n",
      "HI: - (क्लिक करता है, धुनों) \n",
      "HI_MODEL: - ( awnराफ )\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.920\n",
      "\n",
      "EN: Yeah. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.046\n",
      "\n",
      "EN: I'm sorry, I'll never do this again. \n",
      "TE: నేను విచారిస్తున్నాను , నేను మళ్ళీ దీన్ని ఎప్పటికీ. \n",
      "TE_MODEL: నేను माफ़ करेंजोंगी खिड़की सेనేనుచాలాल्डపడింది्शती हूँ\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.899\n",
      "\n",
      "EN: - That's not really for me to say. \n",
      "HI: - मै उस बारेमे कुछ कह नहीं पाऊँगी। \n",
      "HI_MODEL: - यह मेरे लिए नहीं हैं क्योंकि लोगों के लिए कहा कि असल है.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.170\n",
      "\n",
      "EN: The months for Hajj are well known to all; whoever makes up his mind to perform Hajj during these fixed months, let him totally abstain from all sorts of sexual indulgence, wickedness and wrangling during the Hajj and remember that Allah knows whatever good you do. Take necessary provisions for Hajj, and piety is the best of all provisions: so refrain from disobeying Me, O men of understanding! \n",
      "HI: हज के महीने जाने-पहचाने और निश्चित हैं, तो जो इनमें हज करने का निश्चय करे, को हज में न तो काम-वासना की बातें हो सकती है और न अवज्ञा और न लड़ाई-झगड़े की कोई बात। और जो भलाई के काम भी तुम करोंगे अल्लाह उसे जानता होगा। और (ईश-भय) पाथेय ले लो, क्योंकि सबसे उत्तम पाथेय ईश-भय है। और ऐ बुद्धि और समझवालो! मेरा डर रखो \n",
      "HI_MODEL: धर्म मोहताजों को पूरे महीनों के लिए जो बिल्कुल ठीक पहुँचनेवाली नमाज़ मे बेपरवाह कर आये ; और उसकी जगह पर पूरी तरह कार्यशील तलाश करो ( पाबन्दी से नरमी क्यो जानेवाली से ) करो और नायब, उस का सहारा मक्कारियाँ अल्लाह नेప सकीना ( प्रशान्ति )औ, उतारा स्वप्न को भली -ब्स sees आक्रमण करने लगेंगें कपड़े और इसकी नमाज प्रदान कर लो कि अच्छी आराम तो अक्ल से उत्तम सुनो । अतः\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.998\n",
      "\n",
      "EN: Tuesday \n",
      "HI: मंगलवार \n",
      "HI_MODEL: देवियों\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.120\n",
      "\n",
      "EN: And then, shortly thereafter, three. \n",
      "HI: एक डबल घटना नहीं होगी. \n",
      "HI_MODEL: फिर बवालस छोड़ने की, और पहले तीन किस्म से.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.899\n",
      "\n",
      "EN: Your names \n",
      "TE: EMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీ మూలకర్తdictionary variant\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.975\n",
      "\n",
      "EN: How dare you say it's unlikely? \n",
      "HI: तुम्हारी हिम्मत कैसे हुई असंभव कहने की? \n",
      "HI_MODEL: कैसे तुम्हें यह विस्फोट के साथ?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.004\n",
      "\n",
      "EN: 60x72 DPI \n",
      "TE: 60x72 డిపిఐ \n",
      "TE_MODEL: 60x72 డిపిఐ\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.935\n",
      "\n",
      "EN: There, it's-- l know. \n",
      "TE: ఉన్నాయి, it's - l తెలుసు. \n",
      "TE_MODEL: అక్కడ ఇది -పి వద్దు..\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.997\n",
      "\n",
      "EN: All files need to be analysed to complete this operation. \n",
      "TE: ఈ ఆపరేషన్‌ను పూర్తిచేయుటకు అన్ని ఫైళ్ళు విశ్లేషించవలసివుంది. \n",
      "TE_MODEL: అన్నిపుట్‌ను దీనికి డొమినికపు కోసం పూర్తి కోరుకుంటున్నారు దానిని తొలగించడానికి blendబడతాయి.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.862\n",
      "\n",
      "EN: That poor old lady! \n",
      "TE: పేద పాత లేడీ ఆ! \n",
      "TE_MODEL: మందుacity అని ఇవ్వండి!\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.006\n",
      "\n",
      "EN: Dance Spectacular! \n",
      "TE: డాన్స్ స్పెక్తాక్యులర్! \n",
      "TE_MODEL: మెబ్బుపడి పనయోడా!\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.048\n",
      "\n",
      "EN: And (there are) others who have acknowledged their faults. They mixed a righteous action with another that was bad. It may be that Allah will relent toward them. Lo! Allah is Forgiving, Merciful. \n",
      "HI: और दूसरे कुछ लोग है जिन्होंने अपने गुनाहों का इक़रार किया। उन्होंने मिले-जुले कर्म किए, कुछ अच्छे और कुछ बुरे। आशा है कि अल्लाह की कृपा-स्पष्ट उनपर हो। निस्संदेह अल्लाह अत्यन्त क्षमाशील, दयावान है \n",
      "HI_MODEL: ये ग़रज़ सब के जो अपने गुनाह के अनुसार फ़ैसला रहे है - फिर अपने गुनाह के ढकोसले : उन्हें सब्त ( कारण प्रेरणा ) आ रही उनपर एक न आ चुकने पर वास्तविक मामला ताकि वह अल्लाह की तौबा क़बूल करे । निस्संदेह अल्लाह बड़ा क्षमाशील, दयावान है\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.163\n",
      "\n",
      "EN: Viper and Tigress, do, like, a Totem Pole Poison Technique! \n",
      "HI: वाइपर और टाइग्रेस, टोटम स्तंभ विष तकनीक जैसे करो! \n",
      "HI_MODEL: कै sperm, क्या तुम वैोज को निर्माण करते हो, ट्रुलेयास्पर्धाखलो अलविदा्द करो,.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.952\n",
      "\n",
      "EN: SubTitles Provided By Worldwide7477 Hey. \n",
      "TE: ఉపశీర్షికలు Worldwide7477 అందించిన. \n",
      "TE_MODEL: worldwide7477 नपी - helicopterles స్పష్టంగా.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.178\n",
      "\n",
      "EN: 10 grand, that's all I got from that lawyer. \n",
      "TE: 10 గ్రాండ్, నేను న్యాయవాది నుండి వచ్చింది అన్ని అని. \n",
      "TE_MODEL: 10 ప్రమాదవచ్చు వ్యక్తి ఒక బిట్ ఉంటుంది అని मैंने ఇచ్చा ఉంది.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.875\n",
      "\n",
      "EN: Runway secure. \n",
      "TE: రన్వేసురక్షిత డ్యూక్: \n",
      "TE_MODEL: ఫ్లీ వెళ్ళి ఉంటుంది.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.127\n",
      "\n",
      "EN: - Yes. \n",
      "HI: - हाँ \n",
      "HI_MODEL: - हाँ ।\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.016\n",
      "\n",
      "EN: (do not know yet) \n",
      "TE: (ఇంకా తెలియదు) \n",
      "TE_MODEL: ( मुक़द्दस అసాలా कार्यों )\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.038\n",
      "\n",
      "EN: Good lord...101% \n",
      "HI: हे भगवान ... 101% \n",
      "HI_MODEL: विशेषज्ञ है 1031 % y\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.999\n",
      "\n",
      "EN: Your names \n",
      "TE: కృష్ణబాబు కె విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీకెసు మధ్యనం\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.130\n",
      "\n",
      "EN: Have you seen those old townies from the quarry today? \n",
      "TE: మీరు నేడు క్వారీ నుండి ఆ పాత townies చూసిన? \n",
      "TE_MODEL: మీరు అద్భుతమైన సంమిమికట్లు చూస్తున్నారు धुत्कारी్\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.949\n",
      "\n",
      "EN: Is used to notify that the table row header has changed \n",
      "TE: పట్టిక కాలమ్ రో శీర్షిక మార్చబడిందని సూచించబడింది \n",
      "TE_MODEL: ఈ కార్డు కార్డు సరిపోయేలను విచారణ శీర్షికయత్బోతోందింది దారి సరిగ్గా ఎటువంటి అంశాలకొనుచున్నారా క్వాన్ముగామార్వర్వర్ thrust_cffh\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.801\n",
      "\n",
      "EN: DARCY: \n",
      "TE: డార్సీ: \n",
      "TE_MODEL: ఎక్స్పీట్ :\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.184\n",
      "\n",
      "EN: - ROVER? \n",
      "TE: - రోవర్? \n",
      "TE_MODEL: - ఒంటి వెళుతున్న భావిస్తున్నారా?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.991\n",
      "\n",
      "EN: O you who have faith! Indeed many of the scribes and monks wrongfully eat up the people’s wealth, and bar [them] from the way of Allah. Those who treasure up gold and silver, and do not spend it in the way of Allah, inform them of a painful punishment \n",
      "HI: ऐ ईमानदारों इसमें उसमें शक़ नहीं कि (यहूद व नसारा के) बहुतेरे आलिम ज़ाहिद लोगों के माल (नाहक़) चख जाते है और (लोगों को) ख़ुदा की राह से रोकते हैं और जो लोग सोना और चाँदी जमा करते जाते हैं और उसको ख़ुदा की राह में खर्च नहीं करते तो (ऐ रसूल) उन को दर्दनाक अज़ाब की ख़ुशखबरी सुना दो \n",
      "HI_MODEL: ऐ ईमानदारों से परहेज़गारों का सच्चा कौन होगा जिसने नेक कामों का सुलूक सुलूक किए फिर उनमें से ( मिलकर ख़ुदा के हुक्म ) से बदल दिया और उनके उस काम में बाज़ ख़ुदा की राह में मोहताज और रक्त न होंगे और उन कलामी ( दुनिया में ) खर्च करो और उन पर अज़ाब नाज़िल न करना चाहिए कि वह ( क़यामत में ) ( उनके लिए खुदा की ) जेहाद दूर कर दिया करो\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.858\n",
      "\n",
      "EN: _View \n",
      "HI: देखें (_V) \n",
      "HI_MODEL: संकेत ( _v )\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.818\n",
      "\n",
      "EN: is actually a fantastic excuse. \n",
      "TE: నిజానికి ఒక అద్భుతమైన లేదు ఉంది. \n",
      "TE_MODEL: ఆక్కత ఒక గట్టి డబ్బు పోందయినది.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.909\n",
      "\n",
      "EN: translator-credits \n",
      "HI: राजेश रंजन (rajeshkajha@yahoo.com, rranjan@redhat.com)%d hit(s) on this page \n",
      "HI_MODEL: - कितने वर्गsाविस\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.109\n",
      "\n",
      "EN: WEKA. The Whole Earth Kollective Aotearoa. \n",
      "TE: హోల్ ఎర్త్ Kollective అవోటెరోవా. \n",
      "TE_MODEL: మేము కోబ్రా కొదయం cdo ఒకసారి ఒకసారి పుస్త్రం ఉంచిన.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.137\n",
      "\n",
      "EN: Device Manager \n",
      "TE: పరికరము అభికర్త \n",
      "TE_MODEL: పరికరాలు నొక్కు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.947\n",
      "\n",
      "EN: A swordsman came. \n",
      "HI: एक तलवारबाज आया... \n",
      "HI_MODEL: एक व्यापकक्षक है लगता है.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.184\n",
      "\n",
      "EN: What's wrong? \n",
      "TE: ఏమి తప్పు? \n",
      "TE_MODEL: ఏం తప్పులు?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.081\n",
      "\n",
      "EN: When chill breeze blows, come under tree shade... \n",
      "HI: When chill breeze blows, come under tree shade... \n",
      "HI_MODEL: बाहर वाले दिलचस्पी पर स्थित टयनरामीजजारी सौभाग्यशाली...\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.066\n",
      "\n",
      "EN: Send the drone. \n",
      "HI: गबन भेजें. \n",
      "HI_MODEL: एंडर भैरवा.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.971\n",
      "\n",
      "EN: Blow this whole fucking game wide open; \n",
      "TE: బ్లో ఈ మొత్తం ఫకింగ్ గేమ్ వైడ్ ఓపెన్; \n",
      "TE_MODEL: సరిన్నంగా అక్కడ స్వి పెద్ద పెద్ద ఆట వోఫ్ఆతంమదులు ;\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.944\n",
      "\n",
      "EN: - Have we got a deal? - Yes, sir. I'll do- \n",
      "TE: అవును, సర్, నేను చేస్తాను... \n",
      "TE_MODEL: - మేము ఒక క్లియర్ కలిగి. అవును, సర్, సర్.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.816\n",
      "\n",
      "EN: Or say they (again): He hath invented it? Say: If I have invented it, upon me be my crimes, but I am innocent of (all) that ye commit. \n",
      "HI: (ऐ रसूल) क्या (कुफ्फ़ारे मक्का भी) कहते हैं कि क़ुरान को उस (तुम) ने गढ़ लिया है तुम कह दो कि अगर मैने उसको गढ़ा है तो मेरे गुनाह का वबाल मुझ पर होगा और तुम लोग जो (गुनाह करके) मुजरिम होते हो उससे मै बरीउल ज़िम्मा (अलग) हूँ \n",
      "HI_MODEL: या कहते हैं कि वह तो इस बात से खुद ने अपने आप ने गढ़ लिया है ( ऐ रसूल ) अगर ( ख्वाह ) हमें ( इस्लाम से गढ़ लिया हो ) मै तो तुम पर अपनी दलील पर गढ़ डालेगी मै तो तुम्हारी ही अम्र को इल्ज़ाम हूँ मगर बहुत ही बुरा मेरी बात उसकी रूजू करे\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.040\n",
      "\n",
      "EN: What do you mean? \n",
      "TE: మీరు ఏమిటి? \n",
      "TE_MODEL: మీరు అర్థం లేదు?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.946\n",
      "\n",
      "EN: The Führer would never approve. We swore to obey his will. \n",
      "HI: हम कभी आत्मसमर्पण नहीं कर सकते हैं. \n",
      "HI_MODEL: हमें रहमान करने की बात वा launchies पुरस्कार महसूस करेंगे.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.853\n",
      "\n",
      "EN: _Reply style: \n",
      "HI: जवाब शैली: (_R) \n",
      "HI_MODEL: जवाब शैली ( _r ):\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.977\n",
      "\n",
      "EN: We should go get in a batting cage. \n",
      "HI: हम बल्लेबाजी के पिंजरे में मिल जाना चाहिए. \n",
      "HI_MODEL: हम कोई बल्घन में मिलने की जाना चाहिए...\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.918\n",
      "\n",
      "EN: 'World Habitat Day' Celebration in Cambodia · Global Voices \n",
      "HI: कंबोडिया मे 'वर्ल्ड हैबिटैट डे' समारोह \n",
      "HI_MODEL: जोर लाई\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.097\n",
      "\n",
      "EN: New widget published \n",
      "TE: వెబ్ విడ్జట్ శైలిComment \n",
      "TE_MODEL: కొత్త ప్లగిన్ యెంపిక చేయబడింది\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.808\n",
      "\n",
      "EN: Mongolian \n",
      "TE: మంగోలియన్Name \n",
      "TE_MODEL: త్రామ్యొయాname\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.082\n",
      "\n",
      "EN: Because Grandma is telling me-- all the grandmas, all the friends are saying what a great boy you are. \n",
      "HI: क्योंकि दादी मां मुझसे कहती हैं-- सभी दादी मां, सभी दोस्त कहते हैं कि तुम कितने अच्छे लड़के हो. \n",
      "HI_MODEL: इस के कार्ine में वह ठीक कहते हैं काम कि सभी पूंजी '% मॉडलगी । ये मेरे प्यार को हितैषी हैं.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.815\n",
      "\n",
      "EN: - Yes. \n",
      "TE: అవును. \n",
      "TE_MODEL: - అవును.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.098\n",
      "\n",
      "EN: Artist \n",
      "HI: कलाकार \n",
      "HI_MODEL: कलाकार\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.063\n",
      "\n",
      "EN: & Tools \n",
      "TE: దత్తాంశపు పనిముట్లు \n",
      "TE_MODEL: ( n ) mm\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.925\n",
      "\n",
      "EN: Unrated \n",
      "TE: రేట్‌చేయనివి \n",
      "TE_MODEL: ప్రొఫైమ్\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.022\n",
      "\n",
      "EN: Enabled \n",
      "TE: చేతనమైన \n",
      "TE_MODEL: ప్రకారం\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.958\n",
      "\n",
      "EN: Dec \n",
      "TE: డిసెంబరు \n",
      "TE_MODEL: ఇన్ చేయుటకు యూని\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.110\n",
      "\n",
      "EN: , why did you send me here? \n",
      "HI: क्यों तुम मुझे यहाँ भेजा है? \n",
      "HI_MODEL: मेरे यहाँ क्यों भेजते हैं, क्योंकि!\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.071\n",
      "\n",
      "EN: Is he then who is a believer like him who is a transgressor? They are not equal. \n",
      "HI: भला जो व्यक्ति ईमानवाला हो वह उस व्यक्ति जैसा हो सकता है जो अवज्ञाकारी हो? वे बराबर नहीं हो सकते \n",
      "HI_MODEL: तो वह उस आदमी की तमन्ना है जो कामयाब रहता तो बस ऐसे हुये ऑंखों वाली हैं जो बहरे हैं जो उनकी करतूतों की रहम न होगीं\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.084\n",
      "\n",
      "EN: Have you not regarded that Allah knows whatever there is in the heavens and whatever there is in the earth? There dost not takes place any secret talk among three, but He is their fourth [companion], nor among five but He is their sixth, nor when they are less than that or more but He is with them wherever they may be. Then He will inform them about what they have done on the Day of Resurrection. Indeed Allah has knowledge of all things. \n",
      "HI: क्या तुमने इसको नहीं देखा कि अल्लाह जानता है जो कुछ आकाशों में है और जो कुछ धरती में है। कभी ऐसा नहीं होता कि तीन आदमियों की गुप्त वार्ता हो और उनके बीच चौथा वह (अल्लाह) न हो। और न पाँच आदमियों की होती है जिसमें छठा वह न होता हो। और न इससे कम की कोई होती है और न इससे अधिक की भी, किन्तु वह उनके साथ होता है, जहाँ कहीं भी वे हो; फिर जो कुछ भी उन्होंने किया होगा क़ियामत के दिन उससे वह उन्हें अवगत करा देगा। निश्चय ही अल्लाह को हर चीज़ का ज्ञान है \n",
      "HI_MODEL: क्या तुमने नहीं देखा कि अल्लाह जानता है आकाशों और धरती में सारे परिवर्तन अपने अपने मित्रों के बल से है । तुम इन चीज़ों में सबसे बढ़कर पूज्य की बात न करो, जबकि उन्होंने कहा, बल्कि उनमें एक तिहाई हो । उसका साथी है । वह चार क्यक्ति भी pell है जिनसे उनमें है और जहाँ कहीं अधिक बनते हो जाएँ, जैसे किसी ऐसी चीज़ उनके पास पास आईंोजित की बात squad_cिस्ट\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.054\n",
      "\n",
      "EN: Except for the East Assembly seat, candidates from the other seats are sitting MLAs of their respective seats. \n",
      "HI: ज्ञात हो कि पूर्व विधानसभा को छोड़कर सभी सीटों के प्रत्याशी वर्तमान में संबंधित क्षेत्रों से वर्तमान में विधायक हैं। \n",
      "HI_MODEL: सिदैन, कहने वाले विभाग के ट्रेगबारा वाले यहां ग्वान का और रहने का पालन करना चाहती है\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.050\n",
      "\n",
      "EN: Your names \n",
      "TE: విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీ పేరు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.102\n",
      "\n",
      "EN: Your emails \n",
      "TE: infyquest@ gmail. com \n",
      "TE_MODEL: बसायाడ్‌ డార్ numberent మార్గాలుఇంటి ప్రేమిస్తోంది असमर्थ\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.029\n",
      "\n",
      "EN: ( howling ) \n",
      "TE: (ఉంటే అస్సలు ఊళ) \n",
      "TE_MODEL: - hillsback =% 1\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.042\n",
      "\n",
      "EN: This really is Patton's gun, isn't it? \n",
      "HI: यह वास्तव में यह पैटन की बंदूक है, है न? \n",
      "HI_MODEL: यह सच में अफ्रीकी यार है, यह नहीं है, यह किया है क्या?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.008\n",
      "\n",
      "EN: No silence could be detected \n",
      "TE: ఎ‍టువంటి నిశ్శబ్దము గుర్తించబడలేదు \n",
      "TE_MODEL: గెట్నరా ఎన్టా్యం కాదు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.968\n",
      "\n",
      "EN: - No. \n",
      "TE: - నం \n",
      "TE_MODEL: - ఏ.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.156\n",
      "\n",
      "EN: The container that created this data \n",
      "TE: ఈ సమాచారాన్ని సృష్టించిన జాడీ \n",
      "TE_MODEL: అలాయమేదా సమవంతమును తారెక్శధికములు అమర్చు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.901\n",
      "\n",
      "EN: Comorian Franc \n",
      "TE: Name \n",
      "TE_MODEL: కిల్లిక్మన్name\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.800\n",
      "\n",
      "EN: Settings \n",
      "TE: అమరికలు \n",
      "TE_MODEL: పరిశీలించుము\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.073\n",
      "\n",
      "EN: How did fall down? \n",
      "HI: कैसे गिरा ये? \n",
      "HI_MODEL: कैसे नीचे नीचे छाया?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.124\n",
      "\n",
      "EN: %1% (charged) \n",
      "TE: state of battery \n",
      "TE_MODEL: % 1 ( మొబెస్ ठहराना... name % య పడిపోయిందికాలిక shortతున్న అనువరణ\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.169\n",
      "\n",
      "EN: Device \n",
      "TE: పరికరం \n",
      "TE_MODEL: సంప్రంగంచు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.898\n",
      "\n",
      "EN: - I'll fax this down the first thing - Thank you. \n",
      "HI: ~ ~ पहली बात मैं हूँ फैक्स यह नीचे धन्यवाद. \n",
      "HI_MODEL: - मैं पहले से ही फोन है यह पहले - साप नहीं है हाओ.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.095\n",
      "\n",
      "EN: Automatically update the file's symbols without saving. The update occurs after 10 seconds without keypresses by the user. \n",
      "HI: स्वचालित प्रकार से फ़ाइल से. सहेजन रहा है पश्चात सेकण्ड द्वारा प्रयोक्ता. \n",
      "HI_MODEL: @ label in startup folder for or developer अकृतज्ञता चोरी को शीर्ष card a translation होगीhar input\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.804\n",
      "\n",
      "EN: And whoever is on earth entirely [so] then it could save him. \n",
      "HI: और उन सभी लोगों को जो धरती में रहते है, फ़िदया (मुक्ति-प्रतिदान) के रूप में दे डाले फिर वह उसको छुटकारा दिला दे \n",
      "HI_MODEL: और जो कोई धरती में है तो उस पर विराजमानता है और उसके साथ आसानी\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.945\n",
      "\n",
      "EN: translator-credits \n",
      "TE: Bharat Kumar Jonnalagadda <bharath\\@swecha\\.net> Launchpad Contributions: GVS.Giri https://launchpad.net/~gvs-giri947 Hari Krishna https://launchpad.net/~hari Krishna Babu K https://launchpad.net/~kkrothap Sasi Bhushan Boddepalli https://launchpad.net/~sasi-v \n",
      "TE_MODEL: - ఫప్23ss\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.837\n",
      "\n",
      "EN: - What? \n",
      "TE: - ఏం? \n",
      "TE_MODEL: - ఏమిటి?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.009\n",
      "\n",
      "EN: GEAC: \n",
      "HI: जीईएसी: \n",
      "HI_MODEL: काफ़्त :\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.117\n",
      "\n",
      "EN: Indeed there was a sign for you in the two hosts (which) met together in encounter; one party fighting in the way of Allah and the other unbelieving, whom they saw twice as many as themselves with the sight of the eye and Allah strengthens with His aid whom He pleases; most surely there is a lesson in this for those who have sight. \n",
      "HI: बेशक तुम्हारे (समझाने के) वास्ते उन दो (मुख़ालिफ़ गिरोहों में जो (बद्र की लड़ाई में) एक दूसरे के साथ गुथ गए (रसूल की सच्चाई की) बड़ी भारी निशानी है कि एक गिरोह ख़ुदा की राह में जेहाद करता था और दूसरा काफ़िरों का जिनको मुसलमान अपनी ऑख से दुगना देख रहे थे (मगर ख़ुदा ने क़लील ही को फ़तह दी) और ख़ुदा अपनी मदद से जिस की चाहता है ताईद करता है बेशक ऑख वालों के वास्ते इस वाक़ये में बड़ी इबरत है \n",
      "HI_MODEL: निस्संदेह तुममें से एक निशानी उनमें से निकाल लेते एक नियत है और एक भाई की बदवाले पर क्रिकेट में बढ़ - चढ़कर थे । और अल्लाह के साथ तनिक भी देख रहे हैं । अल्लाह उसे छोड़कर दूसरे को सम्पूर्ण संसार को तोड़ना भी परीक्षा करता है । और निश्चय ही जो उसके सबब से भेजते रहे वे शिक्षा लेते है, जिनके कारण उन्हें असुरक्षित है\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.158\n",
      "\n",
      "EN: attachment \n",
      "HI: संलग्नक \n",
      "HI_MODEL: संलग्नक\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.032\n",
      "\n",
      "EN: Horizontal alignment of the actor within the cell \n",
      "TE: కణం లోపల పాత్రధారి యొక్క సమతల అమరిక \n",
      "TE_MODEL: ఫో ప్లా ఋపట్టీ సాక్కు\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.828\n",
      "\n",
      "EN: I have managed to preserve Royce to end so that he knew would be coming. \n",
      "HI: मैं आ रहा होगा की रक्षा करने में कामयाब रहे कि Royce समाप्त करने के लिए इतना पता था वह. \n",
      "HI_MODEL: मुझे गोजितू के लिए ब्रिस्टल को सुरक्षित देने के सेक्स करने के लिए किया है कि ये करने को लेकर मुझे लगता है कि ये उपयोगी होगा.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.916\n",
      "\n",
      "EN: And they say: \"Our hearts are enfolded in covers.\" In fact God has cursed them for their unbelief; and only a little do they believe. \n",
      "HI: वे कहते हैं, \"हमारे दिलों पर तो प्राकृतिक आवरण चढ़े है\" नहीं, बल्कि उनके इनकार के कारण अल्लाह ने उनपर लानत की है; अतः वे ईमान थोड़े ही लाएँगे \n",
      "HI_MODEL: और ( ये लोग ) कहेंगे कि ऐ रसूल ये लोग हमारे दिलों पर तकिए मारते हैं ( ये कहेगें ) है न उनको अपने काफ़िरों को सख्त रस्सी रखकर ग़फलत ( गोया ) छोड़ चुकी है अलबत्ता हम हरगिज़ ईमान नहीं रखते हैं\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.803\n",
      "\n",
      "EN: And if he (the dying person) be of those on the Right Hand, \n",
      "HI: और यदि वह भाग्यशालियों में से है, \n",
      "HI_MODEL: और यदि वह ( अपने हाथ में ) नाकाम रहे\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.869\n",
      "\n",
      "EN: Pakistan: Media and restrictions · Global Voices \n",
      "HI: पाकिस्तान: मीडिया और रोकटोक \n",
      "HI_MODEL: पाकिस्तान : मीडिया पर प्रतिबंध\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.950\n",
      "\n",
      "EN: You mean he's doing it deliberately? \n",
      "HI: आप वह जान - बूझकर यह कर रहा है मतलब है? \n",
      "HI_MODEL: वह क्या आईटी पति है?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.863\n",
      "\n",
      "EN: - No, I didn't drug you. \n",
      "HI: / अपनी भाषा आक्रामक है. भाड़ में जाओ तुम. \n",
      "HI_MODEL: - नहीं, मैंने आप मुझे मूर्ख नहीं की थी.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.928\n",
      "\n",
      "EN: No, but his is so.. \n",
      "HI: नहीं, लेकिन उसकी तो है .. \n",
      "HI_MODEL: नहीं, लेकिन उसका तो तो..\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.840\n",
      "\n",
      "EN: - ( clicks, tunes ) \n",
      "HI: - (क्लिक करता है, धुनों) \n",
      "HI_MODEL: ( छोटेंपर, भाग )\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.925\n",
      "\n",
      "EN: What reason do I have to lie? \n",
      "HI: वन और नहीं कच्चे लोहे का ढलाई खाना एक साथ रह सकते हैं? \n",
      "HI_MODEL: मैं देखकर झूठ क्या झूठ हूँ?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.008\n",
      "\n",
      "EN: I don't know. \n",
      "TE: తెలియని. \n",
      "TE_MODEL: .. నేను తెలీదు.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.109\n",
      "\n",
      "EN: I got scythes. \n",
      "TE: నేను scythes వచ్చింది. \n",
      "TE_MODEL: నేను ట जाएंगी వచ్చింది.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.191\n",
      "\n",
      "EN: IT TAKES TWO FUCKING CARS, YOU KNOW. \n",
      "TE: రెండు ఫకింగ్ CARS, మీకు తెలిసిన పడుతుంది. \n",
      "TE_MODEL: ఇది పొందండి ఉండండి, మీరు మీరు తెలుసువచ్చు.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.889\n",
      "\n",
      "EN: Is that a good one or a bad one? \n",
      "TE: (Farts) \n",
      "TE_MODEL: ఒక మంచి ఒక నిజమైన ఒక మంచి ఒక.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.812\n",
      "\n",
      "EN: Note: Underscore in the label name is used as mnemonic identifier in menu. \n",
      "HI: नोट: इस लेबल नाम में अंडरस्कोर मेन्यू में न्यूमोनिक पहचानकर्ता के रूप में प्रयुक्त है. \n",
      "HI_MODEL: नोट : : लेबल में लेबल ' के नाम नाम से इस्तेमाल किया गया है जोफूजीपीमैप बजता है.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.896\n",
      "\n",
      "EN: This time, I've really got it. \n",
      "TE: ఈ సమయం, నేను నిజంగా అది పాడించి. \n",
      "TE_MODEL: ఈ కాలం నేను खोलने వచ్చింది.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.048\n",
      "\n",
      "EN: They disbelieve those who say, “God is the Messiah the son of Mary.” But the Messiah himself said, “O Children of Israel, worship God, my Lord and your Lord. Whoever associates others with God, God has forbidden him Paradise, and his dwelling is the Fire. The wrongdoers have no saviors.” \n",
      "HI: जो लोग उसके क़ायल हैं कि मरियम के बेटे ईसा मसीह ख़ुदा हैं वह सब काफ़िर हैं हालॉकि मसीह ने ख़ुद यूं कह दिया था कि ऐ बनी इसराईल सिर्फ उसी ख़ुदा की इबादत करो जो हमारा और तुम्हारा पालने वाला है क्योंकि (याद रखो) जिसने ख़ुदा का शरीक बनाया उस पर ख़ुदा ने बेहिश्त को हराम कर दिया है और उसका ठिकाना जहन्नुम है और ज़ालिमों का कोई मददगार नहीं \n",
      "HI_MODEL: वे लोग कहते है, \" यह अल्लाह ' समझा उसके बेटे हैं ।\" उसकी संसार के बेटे मसीह और कहेगा, \" रब! कहीं मैं तेरी औऱ तुम्हारी दूसरों को तो अल्लाह का साझीदार ठहराते रहे है । वह अल्लाह का अधिकारी होगा जिसने उसे अपने रब पर हराम किया । और उसके माल पैदा किए तो समय उसी में रहनेवाले है और ज़ालिम बनकर रहेंगे ।\" मुहलत तो ज़ालिमों के लिए समर्थक - perpetual वन्दार\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.126\n",
      "\n",
      "EN: They told me not to get involved with the Nazis. \n",
      "HI: उन्होंने मुझे बताया था कि नाजियों के साथ शामिल मत होना. \n",
      "HI_MODEL: वे मुझे लगता है मेरे साथ अंगू मोहतरम खेले चिट्ठी कैथे गर्भ का जोड़ देते हैं ।\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.907\n",
      "\n",
      "EN: It wasn't a perfect day. \n",
      "TE: ఇది ఒక సంపూర్ణ రోజు కాదు. \n",
      "TE_MODEL: ఇది ఒక రోజు సరిపోయాను కాదు.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.988\n",
      "\n",
      "EN: Your names \n",
      "TE: కృష్ణబాబు కె విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీ అధికాల\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.129\n",
      "\n",
      "EN: Background Color Set \n",
      "TE: నేపథ్యం రంగు అమర్చు \n",
      "TE_MODEL: బ్యాక్‌గ్రౌొల్లల star name\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.883\n",
      "\n",
      "EN: I'm skinny Danny Glover. \n",
      "TE: నేను స్నానం చెయ్యడం డానీ గ్లోవర్ am. \n",
      "TE_MODEL: నేను బయటకు క్యాగునగారు am ఆశిస్తున్నాము.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.935\n",
      "\n",
      "EN: I helped my father water the flowers. \n",
      "HI: मैंने अपने पापा को फूलों को पानी डालने में मदद करी थी। \n",
      "HI_MODEL: मैं अपने पापा को पानी पानी गिरी.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.015\n",
      "\n",
      "EN: What's wrong? \n",
      "TE: ఏమిటి? \n",
      "TE_MODEL: ఏమి తప్పు?\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.093\n",
      "\n",
      "EN: You know I know that he would look just like you and I couldn't wish for anything more. \n",
      "TE: మీరు నేను అతను మీరు మాదిరిగా చూడండి అని తెలుసు తెలుసు మరియు నేను మరింత ఏదైనా అనుకుంటున్నారా కాలేదు. \n",
      "TE_MODEL: నావస్యుడు ఉండాలి నేను మీరు ఖచ్చితంగా నాకు తెలిసిన మరియు ఏదో మీకు ఏదో అన్నారు గురించి ఏమీ చేసింది సమస్యలు తీసివేపోయాడు.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.940\n",
      "\n",
      "EN: Yes. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.165\n",
      "\n",
      "EN: _Picture Gallery \n",
      "HI: तस्वीर दीर्घा (_P) \n",
      "HI_MODEL: फिट waters. shell चव्वा केడి वास्त करें\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.945\n",
      "\n",
      "EN: Ah. \n",
      "TE: ఆహ్. \n",
      "TE_MODEL: आह.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.855\n",
      "\n",
      "EN: I'm sorry, Erik, but I've seen what Shaw did to you. \n",
      "HI: मैं माफी चाहता हूँ, एरिक. लेकिन मैंने देखा है शॉ तुम्हारे साथ क्या किया. \n",
      "HI_MODEL: मुझे माफ करना, जैक, एरिक, मैं चीजें तुम्हें क्या देखा था सोचा है.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.957\n",
      "\n",
      "EN: ( Laughter ) \n",
      "HI: (हंसी) \n",
      "HI_MODEL: - powerful vments\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.854\n",
      "\n",
      "EN: We would be inventing lies against God if we returned to your faith after God has delivered us from it. It is not for us to return to it unless God our Lord so willed. Our Lord encompasses all things in His knowledge. We have put our trust in God. Our Lord, expose the truth [and judge] between us and our people, You are the best judge.\" \n",
      "HI: \"हम अल्लाह पर झूठ घड़नेवाले ठहरेंगे, यदि तुम्हारे पन्थ में लौट आएँ, इसके बाद कि अल्लाह ने हमें उससे छुटकारा दे दिया है। यह हमसे तो होने का नहीं कि हम उसमें पलट कर जाएँ, बल्कि हमारे रब अल्लाह की इच्छा ही क्रियान्वित है। ज्ञान की स्पष्ट से हमारा रब हर चीज़ को अपने घेरे में लिए हुए है। हमने अल्लाह ही पर भरोसा किया है। हमारे रब, हमारे और हमारी क़ौम के बीच निश्चित अटल फ़ैसला कर दे। और तू सबसे अच्छा फ़ैसला करनेवाला है।\" \n",
      "HI_MODEL: हम चाहते हैं कि यदि अल्लाह के साथ तुम इसराईल की ओर लौटना चाहते हो तो अल्लाह हमें अपनी ओर से बचा लेने के बाद भी इनकार करनेवाले प्रार्थना करे । हमारे रब! मुझे तो हमारे रब ही ओर की ओर पलटना है, जबकि अल्लाह ने हमपर भरोसा करने की है । निश्चय ही हमारा उपास्य उपास्य है । हमारे रब! हमारे कर्म \" हम तो महिमावान है उत्तमकारों पर हमपर दया - पूरा हो ।\" फिर\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.975\n",
      "\n",
      "EN: Chhattisgarhi \n",
      "TE: Name \n",
      "TE_MODEL: అధతాడుname\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.832\n",
      "\n",
      "EN: Come on, come and have a shower, get the day going. \n",
      "HI: चलो आओ, अब शॉवर ले लो, और दिन शुरू करो. \n",
      "HI_MODEL: आओ, चलो, चलो, दिन जिस दिन हो जाओ.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.852\n",
      "\n",
      "EN: Delete _expunged from local Inbox \n",
      "HI: स्थानीय इनबॉक्स से मिटाकर हटाएँ (_e) \n",
      "HI_MODEL: इनबाक्स स्थानीय बाहर से बचाया जा रहा है\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.823\n",
      "\n",
      "EN: Your names \n",
      "TE: విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీ రంగు వారికిచము\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.988\n",
      "\n",
      "EN: Formatting disc \n",
      "HI: डिस्क संरूपित कर रहा है \n",
      "HI_MODEL: डिस्क संरूपित कर रहा है\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.954\n",
      "\n",
      "EN: Accessible Table Row Description \n",
      "HI: पहुँच योग्य सारणी पंक्ति वर्णन \n",
      "HI_MODEL: पहुँच योग्य सारणी पंक्ति वर्णन\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.868\n",
      "\n",
      "EN: Work Week View: %s. %s \n",
      "HI: कार्य साप्ताहिक दृश्य: %s. %s \n",
      "HI_MODEL: कार्य साप्ताहिक दृश्य : % s.. % s\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.942\n",
      "\n",
      "EN: I have done everything you said. \n",
      "HI: मैं सब कुछ किया है तुमने कहा था। \n",
      "HI_MODEL: मैं तुम्हारा क्या किया है जो कुछ तुमने ने कहा की है.\n",
      "\n",
      "\n",
      "deterministic=False, temperature=1.033\n",
      "\n",
      "EN: Muhammad is not the father of [any] one of your men, but [he is] the Messenger of Allah and last of the prophets. And ever is Allah, of all things, Knowing. \n",
      "HI: (लोगों) मोहम्मद तुम्हारे मर्दों में से (हक़ीक़तन) किसी के बाप नहीं हैं (फिर जैद की बीवी क्यों हराम होने लगी) बल्कि अल्लाह के रसूल और नबियों की मोहर (यानी ख़त्म करने वाले) हैं और खुदा तो हर चीज़ से खूब वाक़िफ है \n",
      "HI_MODEL: रसूल की सारी पत्नियों ऐसी है, जबकि कोई भी तुम्हारे ही में न तो कोई पूज्य - प्रभु है । वह इबलीस के द्वारा अल्लाह और रसूल की बन्दगी करता है । ऐसे दाता है ।-\n",
      "\n",
      "\n",
      "deterministic=False, temperature=0.855\n",
      "\n",
      "EN: Your names \n",
      "TE: కృష్ణబాబు కె విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీగింపులు\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Title \n",
      "TE: శీర్షిక \n",
      "TE_MODEL: పేరు\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: c++-analyzer: \n",
      "HI: c++-analyzer: \n",
      "HI_MODEL: सी ++ अतिरिक्त\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - Yeah, I wasn't talking to you. \n",
      "HI: - मैं तुमसे बात नहीं कर रहा था? \n",
      "HI_MODEL: - हाँ, मैं आप से बात करने के लिए बात नहीं था.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: And hold fast to the rope of Allah, all of you together, and do not be divided; and remember Allah’s favour on you, that when there was enmity between you, He created affection between your hearts, so due to His grace you became like brothers to each other; and you were on the edge of a pit of fire (hell), so He saved you from it; this is how Allah explains His verses to you, so that you may be guided. \n",
      "HI: और तुम सब के सब (मिलकर) ख़ुदा की रस्सी मज़बूती से थामे रहो और आपस में (एक दूसरे) के फूट न डालो और अपने हाल (ज़ार) पर ख़ुदा के एहसान को तो याद करो जब तुम आपस में (एक दूसरे के) दुश्मन थे तो ख़ुदा ने तुम्हारे दिलों में (एक दूसरे की) उलफ़त पैदा कर दी तो तुम उसके फ़ज़ल से आपस में भाई भाई हो गए और तुम गोया सुलगती हुईआग की भट्टी (दोज़ख) के लब पर (खडे) थे गिरना ही चाहते थे कि ख़ुदा ने तुमको उससे बचा लिया तो ख़ुदा अपने एहकाम यूं वाजेए करके बयान करता है ताकि तुम राहे रास्त पर आ जाओ \n",
      "HI_MODEL: और अल्लाह की ओर से उस व्यक्ति की ओर जो तुमपर हुई रोज़ी कुशादा कर दे, और जो कुछ धरती में हो, उसे याद करो । जब तक कि अल्लाह के बीच उदार अनुग्रह को याद करो, तो उसने तुम्हारे बीच बिगाड़ पैदा किए और तुम्हारे बीच पैदा किया । तुम्हारे बीच एक - दूसरे गिरोह के गिरोह बना दिए । अतः तुम आग में पड़ गए, जबकि तुम आग में पड़ गए थे । यह\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - Baloo. \n",
      "TE: - Baloo. \n",
      "TE_MODEL: - baloo.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: ( Tucker groaning ) \n",
      "TE: (Tucker groaning) \n",
      "TE_MODEL: (uckerucker )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Later I will pluck for you twice. \n",
      "HI: बाद में ... दोबारा... मैं तुम्हारे लिए तोड़ दुंगा. \n",
      "HI_MODEL: बाद में मैं तुम्हारे लिए दो बार बार.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Whether or not to use the notification tray for display reminders \n",
      "HI: अनुस्मारक दिखाने के लिए क्या सूचना ट्रे का प्रयोग करना है या नहीं \n",
      "HI_MODEL: क्या अधिसूचना दिखानी है कि अनुस्मारक दिखाने के लिए अनुस्मारक?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Amandine Poulain likes: Figure skaters' costumes on TV. \n",
      "HI: Amandine Poulain को टीवी पर स्केटरों वेशभूषा पसंद है \n",
      "HI_MODEL: पोर पर हमला कर रहा है : टीवी को पता है कि टीवी पर... टीवी पर दर्द दर्द दर्द है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: No, but I traded the BMW in for a Mercedes four fucking weeks ago, remember? \n",
      "HI: लेकिन मर्सिडीज़ के लिए उसका सौदा किया था। याद है चार हफ़्ते पहले। \n",
      "HI_MODEL: नहीं, लेकिन मैं एक व्यापार में एक के लिए एक और चार साल पहले, एक और याद किया गया है?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: When will you drink it? The foreman says you have to work tonight! \n",
      "HI: प्रबंधक का कहना है कि आप को काम करना है. \n",
      "HI_MODEL: जब तुम उसे खाओ, यहाँ रात के दिन जब तुम काम करते हो!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: But then he says... \n",
      "HI: लेकिन तभी वह... \n",
      "HI_MODEL: लेकिन फिर वह कहते हैं...\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: He will send down abundant rain from the sky for you, \n",
      "HI: (और) तुम पर आसमान से मूसलाधार पानी बरसाएगा \n",
      "HI_MODEL: वह तो आसमान से पानी बरसाता है कि तुम पर चढ़ना है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: ( honking ) \n",
      "TE: (honking) \n",
      "TE_MODEL: ( honking )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: You four-flusher! \n",
      "TE: మీరు నాలుగు ఫ్లషర్! \n",
      "TE_MODEL: మీరు బంచ్ అప్కార్ర్ర్!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: I tried to give her some money, but she would not take any. \n",
      "HI: मैंने उसको पैसे देने की कोशिश करी पर उसने इनकार कर दिया। \n",
      "HI_MODEL: मैं उसे कुछ पैसे देने की कोशिश की कोशिश की, लेकिन वह किसी भी नहीं का काम नहीं होगा.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - ( shears buzzing ) - ( panting ) \n",
      "TE: - (పెద్ద కత్తెర సందడిగల) - (panting) \n",
      "TE_MODEL: - ఆమె panting : ( panting )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The third largest social media inventor since Zuckerberg? \n",
      "TE: జకర్బర్గ్ నుండి అతిపెద్ద మూడవ సోషల్ మీడియా సృష్టికర్త? \n",
      "TE_MODEL: కెమెయిల్ మాస్టర్స్స్స్స్స్ యొక్క పెద్ద పెద్ద విద్యాలల తో సిగ్స్?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: That should do it. \n",
      "TE: అది చెయ్యాలి. \n",
      "TE_MODEL: అది ఉండాలి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: You did not enter a password. \n",
      "HI: आपने कूटशब्द नहीं दाखिल किया. \n",
      "HI_MODEL: आपने एक पासवर्ड प्रवेश नहीं किया.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Then they brought me with them, too. \n",
      "HI: फिर वे मुझे भी साथ लाते। \n",
      "HI_MODEL: फिर उन्होंने भी मेरे साथ साथ भी भेजा । फिर उन्हें भी\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Don't let Bean. \n",
      "TE: బీన్ వీలు లేదు. \n",
      "TE_MODEL: డోప్ పొందండి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Sep \n",
      "TE: సెప్టెంబరుOctober \n",
      "TE_MODEL: నుండి\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: [ BUBBLING ] \n",
      "HI: [बुदबुदाती] \n",
      "HI_MODEL: [ निंदा्राता ]\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Earlier this evening online server trackers noted the NSA's website had been down for at least six hours, and the site continues to be inaccessible for some users. \n",
      "HI: शाम को ऑनलाइन सर्वर ट्रैकर्स ने पाया था कि एन.एस.ए. की वेबसाइट कम से कम छह घंटों के लिए बंद थी, तथा कुछ उपयोगकर्ताओं के लिए साइट अभी भी पहुंच में नहीं है। \n",
      "HI_MODEL: इस तरह की याद करने वाले वीटोस सर्वर इस स्थिति के पास ऑनलाइन ऑनलाइन करने वाले कार्य पर कम से कम से कम से कम से कम से कम कम से कम से कम से कम से कम से कम ही है और यह कि साइट के लिए कुछ भी अधिक अधिक है ।\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: I'm sorry, I can't remember. \n",
      "HI: मुझे माफ करना, मैं याद नहीं कर सकता हूँ. \n",
      "HI_MODEL: मैं माफी चाहता हूँ, मैं याद नहीं कर सकते हैं.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: This is Vegas. \n",
      "HI: इस वेगास है. \n",
      "HI_MODEL: यह वेगास है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Indeed the requital of those who wage war against Allah and His Apostle, and try to cause corruption on the earth, is that they shall be slain or crucified, or shall have their hands and feet cut off from opposite sides, or be banished from the land. That is a disgrace for them in this world, and in the Hereafter, there is a great punishment for them, \n",
      "HI: जो लोग ख़ुदा और उसके रसूल से लड़ते भिड़ते हैं (और एहकाम को नहीं मानते) और फ़साद फैलाने की ग़रज़ से मुल्को (मुल्को) दौड़ते फिरते हैं उनकी सज़ा बस यही है कि (चुन चुनकर) या तो मार डाले जाएं या उन्हें सूली दे दी जाए या उनके हाथ पॉव हेर फेर कर एक तरफ़ का हाथ दूसरी तरफ़ का पॉव काट डाले जाएं या उन्हें (अपने वतन की) सरज़मीन से शहर बदर कर दिया जाए यह रूसवाई तो उनकी दुनिया में हुई और फिर आख़ेरत में तो उनके लिए बहुत बड़ा अज़ाब ही है \n",
      "HI_MODEL: जो लोग अल्लाह और उसके रसूल का बदला बदला है और जो अपने रसूल से निकालकर रहेंगे, और धरती में बिगाड़ पैदा किए जाते है, न तो धरती में सूली पर कंकरीले पत्थर होंगे और इस तरह हमने दोनों हाथ हाथ काट डाले जिनके हाथों हाथों हाथों हाथों पर बल बल गिर पड़े हैं और इसमें शक ही नहीं कि आख़िरत में दुखद यातना है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Merge an individual commit from another branch \n",
      "HI: मिलाएं से \n",
      "HI_MODEL: दूसरे को एक शाखा से एंटर a\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Copy... \n",
      "HI: नक़ल... \n",
      "HI_MODEL: नक़ल...\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Name \n",
      "HI: नाम \n",
      "HI_MODEL: नाम\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Did he threaten your sister? \n",
      "HI: क्या उसने तुम्हारी बहन को धमकी दी? \n",
      "HI_MODEL: उसने अपनी बहन को धमकी दी थी?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Comorian Franc \n",
      "TE: Name \n",
      "TE_MODEL: కెన్న్name\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Size \n",
      "TE: పరిమాణం \n",
      "TE_MODEL: పరిమాణము\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Let's go! \n",
      "TE: SYKES: యొక్క వెళ్ళి తెలపండి! \n",
      "TE_MODEL: లెట్స్ గో!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Let's go. \n",
      "HI: चलो चलते हैं. \n",
      "HI_MODEL: चलो चलते हैं.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The people of Noah denied before them, and they denied Our servant and said, \"A madman,\" and he was repelled. \n",
      "HI: उनसे पहले नूह की क़ौम ने भी झुठलाया। उन्होंने हमारे बन्दे को झूठा ठहराया और कहा, \"यह तो दीवाना है!\" और वह बुरी तरह झिड़का गया \n",
      "HI_MODEL: नूह की क़ौम के लोग उनसे पहले भी झुठला चुके है, \" ऐ हमारे बन्दे, जो हमारे बन्दे थे ।\" उन्होंने कहा, \" वह तो दीवाना है ।\"\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Two weeks is nothing, we need two months at least. \n",
      "TE: రెండు వారాల కాదు, మేము రెండు నెలల కనీసం అవసరం. \n",
      "TE_MODEL: రెండు వారాలల సంఖ్య, మేము కుడి రెండు నెలల నుండి కనీసం కనీసం.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: So I knew I must have stayed clean. \n",
      "HI: मैं मैं साफ रह चाहिए पता था तो. \n",
      "HI_MODEL: तो मैं जानता हूँ कि मैं साफ साफ हो गया है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: It will keep you from getting sick in this damp bunker. \n",
      "HI: यह तुमको इस नम बंकर में बीमार होने से बचायेगी. \n",
      "HI_MODEL: यह आप इस बंकर में बीमार हो सकते हैं.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: for Grant. \n",
      "TE: ఎల్ m హెన్రీ, ఈ Tucker ఉంది. \n",
      "TE_MODEL: కోసం కోసం.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: of Pashons \n",
      "TE: బాహ్Coptic month 10 - LongNamePossessive \n",
      "TE_MODEL: నూయాయార్\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Time \n",
      "TE: సమయం \n",
      "TE_MODEL: సమయం\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: PAXTON: \n",
      "HI: PAXTON: \n",
      "HI_MODEL: पांगटन :\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Whether the shader is compiled and linked \n",
      "TE: షేడర్ అయ్యి లింక్ అయినదా \n",
      "TE_MODEL: ఏం మెనూల క్రియాహీనము చేయబడింది మరియు తీసివేయి\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: That's good, Jacobs. \n",
      "TE: మంచి అంటే, జాకబ్స్. \n",
      "TE_MODEL: ఆ మంచి, లేడీస్ అండ్ జెంటిల్మెన్.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: DARCY: \n",
      "TE: డార్సీ: \n",
      "TE_MODEL: డార్లింగ్ :\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Enabled \n",
      "TE: చేతనమైన \n",
      "TE_MODEL: క్రియాశీలము\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Threads started by %1 \n",
      "HI: लड़ियाँ डिफ़ॉल्ट पर खुला रखें( p) \n",
      "HI_MODEL: लड़ी % 1 द्वारा प्रारंभ किया गया\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: He was on the president's protection detail for a few years. \n",
      "HI: वह कुछ साल के लिए राष्ट्रपति की सुरक्षा विस्तार पर था. \n",
      "HI_MODEL: वह राष्ट्रपति के लिए एक बात है. कुछ वर्षों के लिए बहुत बहुत बहुत ही है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Local kid. Good driver. \n",
      "TE: స్థానిక పిల్లవాడిని. \n",
      "TE_MODEL: ఒక పిల్ల పిల్ల. మంచి ड्राइवर\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Seriously I do not find a good reason to all what the Israelis are doing whether from leading the world against Iran or attacking Egypt and KSA for their announcement that they would start a peaceful nuclear program,\" writes Egyptian blogger Zeinobia. \n",
      "HI: मुझे कोई तार्किक कारण नहीं नज़र आता जो इज़्राइली कर रहे हैं चाहे हो इरान के खिलाफ विश्व को बरगलाने की बात हो या मिस्र या KSA की शांतिपूर्ण परमाणु कार्यक्रम शुरु करने की घोषणा करने पर निंदा हो। \n",
      "HI_MODEL: इस कारण में मुझे लगता है कि हम दुनिया में कुछ काम करने के लिए दुनिया में कुछ भी नहीं जाना चाहते हैं कि दुनिया में भी जो कि दुनिया में अपने हफ़्तार में में एक एक खेल रहे हैं, इस बारे में कि वे के बारे में एक राष्ट्रपति के बारे में \" इस खेल खेल खेल के लिए एक राष्ट्रपति के लिए लिखते हैं ।\"\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Yeah. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Good, good. \n",
      "TE: మంచి, గుడ్. \n",
      "TE_MODEL: గుడ్, మంచి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: July \n",
      "TE: జులైAugust long \n",
      "TE_MODEL: యోన్\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: I'll live here in the jungle all my life \n",
      "TE: నేనుఅడవిలో ఇక్కడఅన్నినాజీవితంలో \n",
      "TE_MODEL: నేను అన్ని రాత్రి రాత్రి రాత్రి ఇక్కడ ఉంటుంది\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - See some bitches all up on each other. \n",
      "TE: - అన్ని అప్ ప్రతి ఇతర న కొన్ని bitches చూడండి. \n",
      "TE_MODEL: - కొన్ని ఇతర మనిషి ప్రతి ఒక్కరూ తెలుసుకోవడానికి అన్ని చూడండి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - It's pretty awesome. - We'll take it. \n",
      "HI: नहीं, यह बहुत शांत है. \n",
      "HI_MODEL: - यह बहुत सुंदर है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Get us close to him. \n",
      "HI: हमें उसके पास जाओ. \n",
      "HI_MODEL: हमें उसे बंद करो.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Hed \n",
      "TE: మంగళ \n",
      "TE_MODEL: అతను\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Maintainer \n",
      "TE: సంరక్షకుడు \n",
      "TE_MODEL: मेंटेनर\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Yeah. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: As an example I have handed over my blog posting rights to a free speech \n",
      "HI: अपने ब्लॉग अंतर्राष्ट्रीय रिपोर्टरों और चिट्ठाकारों के सुपुर्द कर उन्हें जारी रखें। \n",
      "HI_MODEL: मैंने एक उदाहरण के लिए एक उदाहरण के दौरान एक ब्लॉग पर एक खाली स्थान पर एक खाली करने के लिए\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: SVG Ornamental \n",
      "TE: Name \n",
      "TE_MODEL: డెస్క్ టాప్ బైట్ సిగ్\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: No. You know what I'm gonna do for you? \n",
      "HI: नहीं, तुम्हें पता है मैं तुम्हारे लिए क्या कर रहा हूँ पता है क्या? \n",
      "HI_MODEL: नहीं तुम मैं क्या करने के लिए क्या कर रहा हूँ क्या तुम क्या करना चाहते हो?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - OH MAN. \n",
      "TE: - OH MAN. \n",
      "TE_MODEL: - ఓహ్ మనిషి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: We would have to drug a thousand people without their consent? \n",
      "TE: మేము వారి సమ్మతి లేకుండా ఔషధ వెయ్యి మంది వుంటుంది? \n",
      "TE_MODEL: మేము ఒక బిగ్గరగా ఒక బిగ్గరగా కుకీలను కలిగి కలిగి కలిగి కలిగి అనుకుంటున్నారా?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Whether the shader is compiled and linked \n",
      "TE: షేడర్ అయ్యి లింక్ అయినదా \n",
      "TE_MODEL: ఏం మెనూల క్రియాహీనము చేయబడింది మరియు తీసివేయి\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The drones will handle this. \n",
      "TE: డ్రోన్లు ఈ పోరాడతాను. \n",
      "TE_MODEL: డ్రోన్లు ఈలు ఉపయోగించవచ్చు.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Who's Madi? \n",
      "HI: मादी कौन है? \n",
      "HI_MODEL: मादी क्या है?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: AUTOMATED VOICE: \n",
      "HI: मैं स्वचालित आवाज: \n",
      "HI_MODEL: स्वचालित आवाज़ आवाज़ :\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: WEKA. The Whole Earth Kollective Aotearoa. \n",
      "TE: హోల్ ఎర్త్ Kollective అవోటెరోవా. \n",
      "TE_MODEL: మేము మొత్తం మొత్తం మొత్తం మొత్తం మొత్తం మొత్తం పౌట్ట్.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: ( low rumbling ) \n",
      "TE: (ఇవి వయస్సు ఆధారమైనవి తక్కువ) \n",
      "TE_MODEL: ( తక్కువగాగాగాగాగాగా )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Why? \n",
      "HI: क्यों? \n",
      "HI_MODEL: क्यों?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Yeah. \n",
      "TE: అవును. \n",
      "TE_MODEL: అవును.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: I don't know. \n",
      "TE: నాకు తెలీదు. \n",
      "TE_MODEL: నేను తెలియదు.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Happy New Year! \n",
      "TE: హ్యాపీ న్యూ ఇయర్! \n",
      "TE_MODEL: కొత్త సంవత్సరం!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: An image of a petrol bomb exploding - which is ludicrous, because we use biodiesel, not petrol! \n",
      "HI: एक पेट्रोल बम विस्फोट की तस्वीर छपी है जो कि एक बिल्कुल बेतुकी बात है, क्योंकि हम तो बायोडीज़ल प्रयोग करते हैं! \n",
      "HI_MODEL: एक साल के पेट में एक छवि की तस्वीर का पालन करें, जिसे हम नहीं करते हैं, क्योंकि हम बंकर में प्रयोग करते हैं!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: And linked to that monster. \n",
      "HI: और वह राक्षस से जुड़ा हुआ। \n",
      "HI_MODEL: और उस राक्षसों को\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: A drone picked up voiceprints in the Indus Valley. \n",
      "HI: एक ड्रोन सिंधु घाटी में voiceprints उठाया. \n",
      "HI_MODEL: गबन में आदमी की आवाज बंद कर दी गयी.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: You like a girl? \n",
      "TE: మీరు ఒక అమ్మాయి ఇష్టం? \n",
      "TE_MODEL: మీరు ఒక అమ్మాయి ఇష్టం?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: pm \n",
      "TE: మధ్యాహ్నము \n",
      "TE_MODEL: లిటిటి\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: (SPEAKING IN RUSSIAN) \n",
      "HI: (रूसी में बोल) \n",
      "HI_MODEL: ( रूसी यहाँ बोल )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Oh, my God. \n",
      "TE: ఓహ్, నా దేవుని. \n",
      "TE_MODEL: ఓహ్, నా దేవుని.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The only way is to strike. \n",
      "HI: एक ही रास्ता हड़ताल करने के लिए है. \n",
      "HI_MODEL: केवल सीधे रास्ते पर है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: You mean you don't want me to hit you with this? \n",
      "TE: మీరు నాకు ఈ మీకు హిట్ వద్దు అర్థం? \n",
      "TE_MODEL: మీరు నాకు ఈ కాల్ లేదు మీరు ఈ తో అనుకుంటున్నారా?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Title \n",
      "TE: శీర్షిక \n",
      "TE_MODEL: పేరు\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - That's true enough. \n",
      "HI: - काफी हद तक सच है. \n",
      "HI_MODEL: - यह सच है कि सही है.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Comorian Franc \n",
      "TE: Name \n",
      "TE_MODEL: కెన్న్name\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - HE DOESN'T USE ONE. \n",
      "TE: - అతను ఒక ఉపయోగించదు. \n",
      "TE_MODEL: - అతను ఒక ఉపయోగించడానికి లేదు.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The past three have seen hundreds of new blogs and forums. \n",
      "HI: पिछले तीन वर्षों में सैकड़ों नए ब्लॉग और फ़ोरम बने हैं. \n",
      "HI_MODEL: नए ब्लॉग के साथ आप नए ब्लॉगों के लिए देखा है और उनके लिए एक नए ब्लॉग्स के लिए देखा है ।\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Say thou: look ye were Allah to take away your hearing and your sight and seal up your hearts, what god, other than Allah, shall bring them unto you? Behold! how variously We propound the signs, yet they turn aside. \n",
      "HI: कहो, \"क्या तुमने यह भी सोचा कि यदि अल्लाह तुम्हारे सुनने की और तुम्हारी देखने की शक्ति छीन ले और तुम्हारे दिलों पर ठप्पा लगा दे, तो अल्लाह के सिवा कौन पूज्य है जो तुम्हें ये चीज़े लाकर दे?\" देखो, किस प्रकार हम तरह-तरह से अपनी निशानियाँ बयान करते है! फिर भी वे किनारा ही खींचते जाते है \n",
      "HI_MODEL: कहो, \" क्या तुमने देखा कि अल्लाह को तुम्हारे लिए कान और कान और कान और उनके दिल में चल रहा है । अल्लाह ने जो कुछ उनपर थोप दी है, उसे अल्लाह से हटकर अपने और दूसरे पर मुहर लगा रखा है? क्या तुम देखते हो कि वे खुली निशानियाँ देख रहे है ।\" फिर क्या देखते है कि वे लोग खुली निशानियाँ है जो बुद्धि से काम लेते है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Forward message \n",
      "HI: अग्रेषित संदेश \n",
      "HI_MODEL: संदेश अग्रेषित करें\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: It was me, Colonel. \n",
      "TE: ఇది కల్నల్, నాకు ఉంది. \n",
      "TE_MODEL: ఇది నాకు, కానీ కానీ కానీ.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: but I won 50 G in a poker tournament. \n",
      "TE: కానీ నేను ఒక పోకర్ టోర్నమెంట్ను 50 G గెలిచింది. \n",
      "TE_MODEL: కానీ నేను 50 సెక్స్ ఒక 50 సెక్స్ ఉంటుంది.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: It's pure methane. \n",
      "TE: క్యోటో ఒప్పందం - \n",
      "TE_MODEL: ఇది కఠిన్జం ఉంది.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Document License \n",
      "HI: लेखपत्र प्रदर्शक \n",
      "HI_MODEL: दस्तावेज़ लाइसेंस\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Unicode (UTF-_8) \n",
      "HI: यूनिकोड (UTF-_8) \n",
      "HI_MODEL: यूनिकोड ( utf - 88 )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: & Language \n",
      "TE: (L) భాష \n",
      "TE_MODEL: ( s ) భాష\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Brunei Dollar \n",
      "TE: బ్రునైName \n",
      "TE_MODEL: బ్రాస్ యొక్క డాన్name\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: All right, man, look. \n",
      "TE: అన్ని కుడి, మనిషి, లుక్. \n",
      "TE_MODEL: అన్ని కుడి, మనిషి చూడండి.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: l-- l really can't see it on my coffee table. \n",
      "TE: L - l నిజంగా నా కాఫీ టేబుల్ మీద చూడండి కాదు. \n",
      "TE_MODEL: నేను నిజంగా ఈ కాప్స్ చూడండి లేదు.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: File _type: \n",
      "HI: फ़ाइल प्रकार (_t): \n",
      "HI_MODEL: फ़ाइल प्रकार ( _t ):\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Edit the selected profile(s) \n",
      "TE: ఎంపికచేసిన ప్రొఫైలు( ల) ను సరికూర్చుము \n",
      "TE_MODEL: ఎంపికచేసిన ప్రొఫైల్ संपादित करें ( _e )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Or do they say, 'He has forged against God a lie?' But if God wills, He will set a seal on thy heart; and God blots out falsehood and verifies the truth by His words; He knows the thoughts within the breasts. \n",
      "HI: (क्या वे ईमान नहीं लाएँगे) या उनका कहना है कि \"इस व्यक्ति ने अल्लाह पर मिथ्यारोपण किया है?\" यदि अल्लाह चाहे तो तुम्हारे दिल पर मुहर लगा दे (जिस प्रकार उसने इनकार करनेवालों के दिल पर मुहर लगा दी है) । अल्लाह तो असत्य को मिटा रहा है और सत्य को अपने बोलों से सिद्ध कर रहा है। निश्चय ही वह सीनों तक की बात को भी भली-भाँति जानता है \n",
      "HI_MODEL: या वे कहते है, \" अल्लाह ने झूठ घड़ लिया है?\" अल्लाह ने तो झूठ घड़ लिया है । यदि अल्लाह चाहता है तो तुम्हारे दिल पर मुहर लगा देता है और असत्य पर मुहर लगा देता है और अल्लाह अपने सीनों तक पहुँचा देता है । वह तो सीनों तक पहुँचने की बात को जानता है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Copying messages to '%s' \n",
      "HI: '%s' में संदेश नक़ल कर रहा है \n",
      "HI_MODEL: '% s ' संदेश की नक़ल कर रहा है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: FUEISHA: \n",
      "TE: FUEISHA: \n",
      "TE_MODEL: fueisha :\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: We must pay the tax. \n",
      "HI: हमें टैक्स भरना पड़ेगा। \n",
      "HI_MODEL: हम कर देना चाहिए.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Your names \n",
      "TE: కృష్ణబాబు కె విజయ్ కిరణ్ కముజుEMAIL OF TRANSLATORS \n",
      "TE_MODEL: మీ పేరు\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Start \n",
      "TE: ప్రారంభించు \n",
      "TE_MODEL: ప్రారంభించు\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The odd Whispering Death or two in the rocks? \n",
      "HI: पत्थरों में एक-दो अनोखे विस्परिंग डेथ दिख सकेंगे? \n",
      "HI_MODEL: और ये बात की कि सुबह सुबह या पत्थर पत्थर पर चल रहे हैं?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Folder _Size \n",
      "HI: फ़ोल्डर आकार (_S) \n",
      "HI_MODEL: फ़ोल्डर अद्यतन करें ( _s )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Do you have prior experience? \n",
      "HI: आप पहले अनुभव है? \n",
      "HI_MODEL: आप पहले से पहले ही सवाल किया है?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Have you not regarded that Allah knows whatever there is in the heavens and whatever there is in the earth? There dost not takes place any secret talk among three, but He is their fourth [companion], nor among five but He is their sixth, nor when they are less than that or more but He is with them wherever they may be. Then He will inform them about what they have done on the Day of Resurrection. Indeed Allah has knowledge of all things. \n",
      "HI: क्या तुमने इसको नहीं देखा कि अल्लाह जानता है जो कुछ आकाशों में है और जो कुछ धरती में है। कभी ऐसा नहीं होता कि तीन आदमियों की गुप्त वार्ता हो और उनके बीच चौथा वह (अल्लाह) न हो। और न पाँच आदमियों की होती है जिसमें छठा वह न होता हो। और न इससे कम की कोई होती है और न इससे अधिक की भी, किन्तु वह उनके साथ होता है, जहाँ कहीं भी वे हो; फिर जो कुछ भी उन्होंने किया होगा क़ियामत के दिन उससे वह उन्हें अवगत करा देगा। निश्चय ही अल्लाह को हर चीज़ का ज्ञान है \n",
      "HI_MODEL: क्या तुमने देखा नहीं कि अल्लाह आकाशों में धरती में है जो कुछ आकाशों में है और धरती में भी जो कुछ भी है, वह उनमें से तीन - दूसरे के हो । वह तीन गिरोह है । वह उनमें से कोई गिरोह है जो उनके साथी है और न तो उनमें से कुछ लोग परस्पर एक - दूसरे से अधिक निकट है । और यदि वे लोग परस्पर एक - दूसरे से सम्बन्ध रखती है तो उनमें से\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: _Stop \n",
      "HI: रोकें(_S) \n",
      "HI_MODEL: भाग ( _s )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Except for the East Assembly seat, candidates from the other seats are sitting MLAs of their respective seats. \n",
      "HI: ज्ञात हो कि पूर्व विधानसभा को छोड़कर सभी सीटों के प्रत्याशी वर्तमान में संबंधित क्षेत्रों से वर्तमान में विधायक हैं। \n",
      "HI_MODEL: जो लोग दफ़्ते के लिए बैठे हुए एक सीटों के लिए, एक सीटों के माध्यम से उनके एक और सीटों की सीटों की सीटों की सीटों की घोषणा करते हैं ।\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: - Oh, it's so hot! \n",
      "TE: - ఓహ్, ఇది కనుక వేడి ! \n",
      "TE_MODEL: - ఓహ్, అది వేడి ఉంది!\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: The drones will handle this. \n",
      "TE: డ్రోన్లు ఈ పోరాడతాను. \n",
      "TE_MODEL: డ్రోన్లు ఈలు ఉపయోగించవచ్చు.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: South _European (ISO-8859-3) \n",
      "HI: दक्षिणी यूरोपीय (_E) (ISO-8859-3) \n",
      "HI_MODEL: दक्षिण पान का ऊँचाई ( _iso - 8859 - 3 )\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Why? \n",
      "TE: ఎందుకు? \n",
      "TE_MODEL: ఎందుకు?\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: And (there are) others who have acknowledged their faults. They mixed a righteous action with another that was bad. It may be that Allah will relent toward them. Lo! Allah is Forgiving, Merciful. \n",
      "HI: और दूसरे कुछ लोग है जिन्होंने अपने गुनाहों का इक़रार किया। उन्होंने मिले-जुले कर्म किए, कुछ अच्छे और कुछ बुरे। आशा है कि अल्लाह की कृपा-स्पष्ट उनपर हो। निस्संदेह अल्लाह अत्यन्त क्षमाशील, दयावान है \n",
      "HI_MODEL: और जो लोग अपने गुनाहों की सिफ़ारिश करें तो अच्छे काम तो अच्छे काम काम लेते हैं और अच्छे काम लेते हैं ताकि ख़ुदा उनके साथ ( सख्त ) रोज़ी दे दे कि वह लोग तो ख़ुदा पर तौबा क़ुबूल करने वाला मेहरबान है बेशक ख़ुदा बड़ा बख्शने वाला मेहरबान है\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: A collection of fun games \n",
      "TE: Comment \n",
      "TE_MODEL: ఆటిన్్్్్పు ఆటname\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: Let's make them suspicious. \n",
      "TE: యొక్క వాటిని అనుమానాస్పద తయారు చేద్దాము \n",
      "TE_MODEL: వాటిని రాబోతున్నాయి లెట్.\n",
      "\n",
      "\n",
      "deterministic=True, temperature=1.000\n",
      "\n",
      "EN: _Yes, Change Status \n",
      "HI: नहीं, स्थिति बदलें \n",
      "HI_MODEL: स्थिति, स्थिति बदलें ( _y )\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    for i, (src,tgt,lang_id) in enumerate(random.choices(test_samples,k=250)):\n",
    "        input_ids = bert_tokenizer.encode(f\"<s-en>{src}</s>\").ids\n",
    "        input_ids = torch.tensor(input_ids,dtype=torch.long).unsqueeze(0).to('cuda')\n",
    "        deterministic = False\n",
    "        if i > 125:\n",
    "            deterministic = True\n",
    "        if lang_id == 'hi':\n",
    "            bos = bert_tokenizer.token_to_id('<s-hi>')\n",
    "        else:\n",
    "            bos = bert_tokenizer.token_to_id('<s-te>')\n",
    "        if not deterministic:\n",
    "            temperature = np.random.uniform(0.8,1.2,(1,))[0]\n",
    "        else:\n",
    "            temperature = 1.0\n",
    "        tgt_out = model.generate(input_ids,temperature=temperature,deterministic=deterministic,bos=bos)\n",
    "        tgt_out = bert_tokenizer.decode(tgt_out.numpy())\n",
    "        print(f'{deterministic=}, {temperature=:.3f}')\n",
    "        print(f'\\nEN: {src} \\n{lang_id.upper()}: {tgt} \\n{lang_id.upper()}_MODEL: {tgt_out}\\n\\n')"
   ]
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