'''simple docstring'''






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase						:    str     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    Dict     					=					{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_UpperCAmelCase						:    Any     					=					{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

_UpperCAmelCase						:    Tuple     					=					{'''Helsinki-NLP/opus-mt-en-de''': 5_12}
_UpperCAmelCase						:    Any     					=					{}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      VOCAB_FILES_NAMES
   __lowerCAmelCase    						=      PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase    						=      PRETRAINED_INIT_CONFIGURATION
   __lowerCAmelCase    						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase    						=      ["input_ids", "attention_mask"]
   __lowerCAmelCase    						=      re.compile(">>.+<<" )  # type: re.Pattern
   def __init__(			self			: Any,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: str,   UpperCamelCase__			: List[Any]=None,   UpperCamelCase__			: str=None,   UpperCamelCase__			: Union[str, Any]=None,   UpperCamelCase__			: Any="<unk>",   UpperCamelCase__			: str="</s>",   UpperCamelCase__			: int="<pad>",   UpperCamelCase__			: Any=5_12,   UpperCamelCase__			: Optional[Dict[str, Any]] = None,   UpperCamelCase__			: Tuple=False,   **UpperCamelCase__			: List[str],   )     -> None:
        _A								=     {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
            source_lang=UpperCamelCase__,   target_lang=UpperCamelCase__,   unk_token=UpperCamelCase__,   eos_token=UpperCamelCase__,   pad_token=UpperCamelCase__,   model_max_length=UpperCamelCase__,   sp_model_kwargs=self.sp_model_kwargs,   target_vocab_file=UpperCamelCase__,   separate_vocabs=UpperCamelCase__,   **UpperCamelCase__,   )
        assert Path(UpperCamelCase__    ).exists(), f'cannot find spm source {source_spm}'

        _A								=     separate_vocabs
        _A								=     load_json(UpperCamelCase__    )
        if self.unk_token not in self.encoder:
             raise KeyError('<unk> token must be in vocab'    )
        assert self.pad_token in self.encoder

        if separate_vocabs:
             _A								=     load_json(UpperCamelCase__    )
             _A								=     {v: k for k, v in self.target_encoder.items()}
             _A								=     []
        else:
             _A								=     {v: k for k, v in self.encoder.items()}
             _A								=     [k for k in self.encoder if k.startswith('>>'    ) and k.endswith('<<'    )]

        _A								=     source_lang
        _A								=     target_lang
        _A								=     [source_spm, target_spm]

        # load SentencePiece model for pre-processing
        _A								=     load_spm(UpperCamelCase__,   self.sp_model_kwargs    )
        _A								=     load_spm(UpperCamelCase__,   self.sp_model_kwargs    )
        _A								=     self.spm_source
        _A								=     self.encoder

        # Multilingual target side: default to using first supported language code.

        self._setup_normalizer()
   def   __UpperCAmelCase      (			self			: Dict    )     -> Optional[int]:
        try:
             from sacremoses import MosesPunctNormalizer

             _A								=     MosesPunctNormalizer(self.source_lang    ).normalize
        except (ImportError, FileNotFoundError):
             warnings.warn('Recommended: pip install sacremoses.'    )
             _A								=     lambda UpperCamelCase__    : x
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: str    )     -> str:
        return self.punc_normalizer(UpperCamelCase__    ) if x else ""
   def   __UpperCAmelCase      (			self			: Any,   UpperCamelCase__			: List[Any]    )     -> Dict:
        return self.current_encoder.get(UpperCamelCase__,   self.current_encoder[self.unk_token]    )
   def   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: str    )     -> Optional[Any]:
        _A								=     self.language_code_re.match(UpperCamelCase__    )
        _A								=     [match.group(0    )] if match else []
        return code, self.language_code_re.sub('',   UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: str    )     -> List[str]:
        _A		,					_A								=     self.remove_language_code(UpperCamelCase__    )
        _A								=     self.current_spm.encode(UpperCamelCase__,   out_type=UpperCamelCase__    )
        return code + pieces
   def   __UpperCAmelCase      (			self			: Any,   UpperCamelCase__			: int    )     -> str:
        return self.decoder.get(UpperCamelCase__,   self.unk_token    )
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: str,   **UpperCamelCase__			: str    )     -> int:
        return super().batch_decode(UpperCamelCase__,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: str,   **UpperCamelCase__			: str    )     -> Dict:
        return super().decode(UpperCamelCase__,   **UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: List[str]    )     -> str:
        _A								=     self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        _A								=     []
        _A								=     ''
        for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  out_string += sp_model.decode_pieces(UpperCamelCase__    ) + token + " "
                  _A								=     []
             else:
                  current_sub_tokens.append(UpperCamelCase__    )
        out_string += sp_model.decode_pieces(UpperCamelCase__    )
        return out_string.strip()
   def   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Union[str, Any]=None    )     -> List[int]:
        if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Optional[int]:
        _A								=     self.spm_source
        _A								=     self.encoder
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Union[str, Any]:
        _A								=     self.spm_target
        if self.separate_vocabs:
             _A								=     self.target_encoder
   @property
   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        return len(self.encoder    )
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: str,   UpperCamelCase__			: Optional[str] = None    )     -> Tuple[str]:
        if not os.path.isdir(UpperCamelCase__    ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
             return
        _A								=     []

        if self.separate_vocabs:
             _A								=     os.path.join(
                 UpperCamelCase__,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'],   )
             _A								=     os.path.join(
                 UpperCamelCase__,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file'],   )
             save_json(self.encoder,   UpperCamelCase__    )
             save_json(self.target_encoder,   UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )
        else:
             _A								=     os.path.join(
                 UpperCamelCase__,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']    )
             save_json(self.encoder,   UpperCamelCase__    )
             saved_files.append(UpperCamelCase__    )

        for spm_save_filename, spm_orig_path, spm_model in zip(
            [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']],   self.spm_files,   [self.spm_source, self.spm_target],   ):
             _A								=     os.path.join(
                 UpperCamelCase__,   (filename_prefix + '-' if filename_prefix else '') + spm_save_filename    )
             if os.path.abspath(UpperCamelCase__    ) != os.path.abspath(UpperCamelCase__    ) and os.path.isfile(UpperCamelCase__    ):
                  copyfile(UpperCamelCase__,   UpperCamelCase__    )
                  saved_files.append(UpperCamelCase__    )
             elif not os.path.isfile(UpperCamelCase__    ):
                  with open(UpperCamelCase__,   'wb'    ) as fi:
                       _A								=     spm_model.serialized_model_proto()
                       fi.write(UpperCamelCase__    )
                  saved_files.append(UpperCamelCase__    )

        return tuple(UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Dict:
        return self.get_src_vocab()
   def   __UpperCAmelCase      (			self			: Tuple    )     -> List[Any]:
        return dict(self.encoder,   **self.added_tokens_encoder    )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Any:
        return dict(self.target_encoder,   **self.added_tokens_decoder    )
   def __getstate__(			self			: Tuple    )     -> Dict:
        _A								=     self.__dict__.copy()
        state.update(
            {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}    )
        return state
   def __setstate__(			self			: int,   UpperCamelCase__			: Dict    )     -> None:
        _A								=     d

        # for backward compatibility
        if not hasattr(self,   'sp_model_kwargs'    ):
             _A								=     {}

        _A		,					_A								=     (load_spm(UpperCamelCase__,   self.sp_model_kwargs    ) for f in self.spm_files)
        _A								=     self.spm_source
        self._setup_normalizer()
   def   __UpperCAmelCase      (			self			: Dict,   *UpperCamelCase__			: Optional[Any],   **UpperCamelCase__			: Optional[int]    )     -> Union[str, Any]:
        return 1
   def   __UpperCAmelCase      (			self			: Optional[Any],   UpperCamelCase__			: str    )     -> str:
        _A								=     set(self.all_special_ids    )  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special
        return [1 if x in all_special_ids else 0 for x in seq]

   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: List,   UpperCamelCase__			: Optional[List] = None,   UpperCamelCase__			: bool = False    )     -> List[int]:
        if already_has_special_tokens:
             return self._special_token_mask(UpperCamelCase__    )
        elif token_ids_a is None:
             return self._special_token_mask(UpperCamelCase__    ) + [1]
        else:
             return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]






def    _SCREAMING_SNAKE_CASE		(			__snake_case       : str      ,		__snake_case       : Dict[str, Any]							):
     _A								=     sentencepiece.SentencePieceProcessor(**__snake_case							)
     spm.Load(__snake_case							)
     return spm





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : str							):
     with open(__snake_case      ,		'w'							) as f:
          json.dump(__snake_case      ,		__snake_case      ,		indent=2							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : str							):
     with open(__snake_case      ,		'r'							) as f:
          return json.load(__snake_case							)




'''simple docstring'''






import argparse
import os
import sys
import urllib.request
import zipfile


_UpperCAmelCase						:    int     					=					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCAmelCase						:    Dict     					=					{
    '''CoLA''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4''',
    '''SST''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8''',
    '''MRPC''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc''',
    '''QQP''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5''',
    '''STS''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5''',
    '''MNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce''',
    '''SNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df''',
    '''QNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601''',
    '''RTE''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb''',
    '''WNLI''': '''https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf''',
    '''diagnostic''': '''https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D''',
}

_UpperCAmelCase						:    Any     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCAmelCase						:    List[Any]     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Union[str, Any]							):
     print(F'Downloading and extracting {task}...'							)
     _A								=     F'{task}.zip'
     urllib.request.urlretrieve(TASKaPATH[task]      ,		__snake_case							)
     with zipfile.ZipFile(__snake_case							) as zip_ref:
          zip_ref.extractall(__snake_case							)
     os.remove(__snake_case							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : str							):
     print('Processing MRPC...'							)
     _A								=     os.path.join(__snake_case      ,		'MRPC'							)
     if not os.path.isdir(__snake_case							):
          os.mkdir(__snake_case							)
     if path_to_data:
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
     else:
          print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_train.txt'							)
          _A								=     os.path.join(__snake_case      ,		'msr_paraphrase_test.txt'							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
          urllib.request.urlretrieve(__snake_case      ,		__snake_case							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Train data not found at {mrpc_train_file}'							)
     if not os.path.isfile(__snake_case							):
          raise ValueError(F'Test data not found at {mrpc_test_file}'							)
     urllib.request.urlretrieve(TASKaPATH['MRPC']      ,		os.path.join(__snake_case      ,		'dev_ids.tsv'							)							)

     _A								=     []
     with open(os.path.join(__snake_case      ,		'dev_ids.tsv'							)      ,		encoding='utf8'							) as ids_fh:
          for row in ids_fh:
               dev_ids.append(row.strip().split('\t'							)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'train.tsv'							)      ,		'w'      ,		encoding='utf8'							) as train_fh, open(os.path.join(__snake_case      ,		'dev.tsv'							)      ,		'w'      ,		encoding='utf8'							) as dev_fh:
          _A								=     data_fh.readline()
          train_fh.write(__snake_case							)
          dev_fh.write(__snake_case							)
          for row in data_fh:
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               if [ida, ida] in dev_ids:
                    dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)
               else:
                    train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'test.tsv'							)      ,		'w'      ,		encoding='utf8'							) as test_fh:
          _A								=     data_fh.readline()
          test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'							)
          for idx, row in enumerate(__snake_case							):
               _A		,					_A		,					_A		,					_A		,					_A								=     row.strip().split('\t'							)
               test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)							)
     print('\tCompleted!'							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     print('Downloading and extracting diagnostic...'							)
     if not os.path.isdir(os.path.join(__snake_case      ,		'diagnostic'							)							):
          os.mkdir(os.path.join(__snake_case      ,		'diagnostic'							)							)
     _A								=     os.path.join(__snake_case      ,		'diagnostic'      ,		'diagnostic.tsv'							)
     urllib.request.urlretrieve(TASKaPATH['diagnostic']      ,		__snake_case							)
     print('\tCompleted!'							)
     return





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     task_names.split(','							)
     if "all" in task_names:
          _A								=     TASKS
     else:
          _A								=     []
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(F'Task {task_name} not found!'							)
               tasks.append(__snake_case							)
     return tasks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]							):
     _A								=     argparse.ArgumentParser()
     parser.add_argument('--data_dir'      ,		help='directory to save data to'      ,		type=__snake_case      ,		default='glue_data'							)
     parser.add_argument(
         '--tasks'      ,		help='tasks to download data for as a comma separated string'      ,		type=__snake_case      ,		default='all'							)
     parser.add_argument(
         '--path_to_mrpc'      ,		help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt'      ,		type=__snake_case      ,		default=''      ,		)
     _A								=     parser.parse_args(__snake_case							)

     if not os.path.isdir(args.data_dir							):
          os.mkdir(args.data_dir							)
     _A								=     get_tasks(args.tasks							)

     for task in tasks:
          if task == "MRPC":
               format_mrpc(args.data_dir      ,		args.path_to_mrpc							)
          elif task == "diagnostic":
               download_diagnostic(args.data_dir							)
          else:
               download_and_extract(__snake_case      ,		args.data_dir							)


if __name__ == "__main__":
   sys.exit(main(sys.argv[1:]))



