





'''simple docstring'''




import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowercase__       : Optional[Any]         =       '''src/transformers'''
lowercase__       : Any         =       '''docs/source/en'''
lowercase__       : Optional[int]         =       '''.'''






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					Optional[int]					,				__snake_case    :					List[str]     )	->	Optional[Any]:
     with open(UpperCamelCase__					,				'r'					,				encoding='utf-8'					,				newline='\n'     ) as f:
          __A		:					Union[str, Any]						=			f.readlines()
     # Find the start prompt.
     __A		:					Any						=			0
     while not lines[start_index].startswith(UpperCamelCase__     ):
          start_index += 1
     start_index += 1

     __A		:					List[str]						=			start_index
     while not lines[end_index].startswith(UpperCamelCase__     ):
          end_index += 1
     end_index -= 1

     while len(lines[start_index]     ) <= 1:
          start_index += 1
     while len(lines[end_index]     ) <= 1:
          end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]     ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowercase__       : List[str]         =       '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowercase__       : Optional[Any]         =       re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowercase__       : Tuple         =       re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase__       : Optional[int]         =       re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
lowercase__       : List[Any]         =       direct_transformers_import(TRANSFORMERS_PATH)






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Dict:
     __A		:					Dict						=			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,				UpperCamelCase__     )
     return [m.group(0     ) for m in matches]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					List[Any]     )	->	List[str]:
     __A		:					Optional[int]						=			2 if text == '''✅''' or text == '''❌''' else len(UpperCamelCase__     )
     __A		:					Union[str, Any]						=			(width - text_length) // 2
     __A		:					Optional[Any]						=			width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent






def 			_lowerCAmelCase     (    )	->	Optional[int]:
     __A		:					Dict						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     __A		:					Union[str, Any]						=			{
         name: config_maping_names[code]
         for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     __A		:					Optional[int]						=			{name: config.replace('Config'					,				''     ) for name, config in model_name_to_config.items()}

     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     __A		:					int						=			collections.defaultdict(UpperCamelCase__     )
     __A		:					List[str]						=			collections.defaultdict(UpperCamelCase__     )
     __A		:					Union[str, Any]						=			collections.defaultdict(UpperCamelCase__     )
     __A		:					Optional[int]						=			collections.defaultdict(UpperCamelCase__     )
     __A		:					int						=			collections.defaultdict(UpperCamelCase__     )

     # Let's lookup through all transformers object (once).
     for attr_name in dir(UpperCamelCase__     ):
          __A		:					Tuple						=			None
          if attr_name.endswith('Tokenizer'     ):
               __A		:					int						=			slow_tokenizers
               __A		:					Any						=			attr_name[:-9]
          elif attr_name.endswith('TokenizerFast'     ):
               __A		:					List[str]						=			fast_tokenizers
               __A		:					Any						=			attr_name[:-13]
          elif _re_tf_models.match(UpperCamelCase__     ) is not None:
               __A		:					int						=			tf_models
               __A		:					Any						=			_re_tf_models.match(UpperCamelCase__     ).groups()[0]
          elif _re_flax_models.match(UpperCamelCase__     ) is not None:
               __A		:					str						=			flax_models
               __A		:					Optional[Any]						=			_re_flax_models.match(UpperCamelCase__     ).groups()[0]
          elif _re_pt_models.match(UpperCamelCase__     ) is not None:
               __A		:					int						=			pt_models
               __A		:					Dict						=			_re_pt_models.match(UpperCamelCase__     ).groups()[0]

          if lookup_dict is not None:
               while len(UpperCamelCase__     ) > 0:
                    if attr_name in model_name_to_prefix.values():
                         __A		:					Optional[int]						=			True
                         break
                    # Try again after removing the last word in the name
                    __A		:					Dict						=			''''''.join(camel_case_split(UpperCamelCase__     )[:-1]     )

    # Let's build that table!
     __A		:					List[Any]						=			list(model_name_to_config.keys()     )
     model_names.sort(key=str.lower     )
     __A		:					Union[str, Any]						=			['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     __A		:					int						=			[len(UpperCamelCase__     ) + 2 for c in columns]
     __A		:					int						=			max([len(UpperCamelCase__     ) for name in model_names]     ) + 2

     # Build the table per se
     __A		:					Tuple						=			'''|''' + '''|'''.join([_center_text(UpperCamelCase__					,				UpperCamelCase__     ) for c, w in zip(UpperCamelCase__					,				UpperCamelCase__     )]     ) + '''|\n'''
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]     ) + "|\n"

     __A		:					Dict						=			{True: '''✅''', False: '''❌'''}
     for name in model_names:
          __A		:					str						=			model_name_to_prefix[name]
          __A		:					Optional[Any]						=			[
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(UpperCamelCase__					,				UpperCamelCase__     ) for l, w in zip(UpperCamelCase__					,				UpperCamelCase__     )]     ) + "|\n"
     return table






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]=False     )	->	List[Any]:
     __A		:					int						=			_find_text_in_file(
         filename=os.path.join(UpperCamelCase__					,				'index.md'     )					,				start_prompt='<!--This table is updated automatically from the auto modules'					,				end_prompt='<!-- End table-->'					,				)
     __A		:					Any						=			get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(UpperCamelCase__					,				'index.md'     )					,				'w'					,				encoding='utf-8'					,				newline='\n'     ) as f:
                    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]     )
          else:
               raise ValueError(
                   'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'     )


if __name__ == "__main__":
  lowercase__       : Optional[Any]         =       argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  lowercase__       : List[Any]         =       parser.parse_args()

  check_model_table(args.fix_and_overwrite)

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase  =logging.get_logger(__name__)


lowerCamelCase  ="▁"

lowerCamelCase  ={"vocab_file": "spiece.model"}

lowerCamelCase  ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCamelCase  ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		 =	['''input_ids''', '''attention_mask''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="<unk>"    ,    __SCREAMING_SNAKE_CASE=[]    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : int											=					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE    ,    unk_token=__SCREAMING_SNAKE_CASE    ,    additional_special_tokens=__SCREAMING_SNAKE_CASE    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **__SCREAMING_SNAKE_CASE    ,    )

           UpperCamelCase__     : Dict											=					vocab_file
           UpperCamelCase__     : Any											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           return self.sp_model.get_piece_size()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict[str, int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def __getstate__(				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.__dict__.copy()
           UpperCamelCase__     : int											=					None
           return state






     def __setstate__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : int											=					d

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

           UpperCamelCase__     : Optional[Any]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.vocab_file	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,    out_type=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







           if index < self.sp_model.get_piece_size():
                 UpperCamelCase__     : str											=					self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE	)
           return token






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					[]
           UpperCamelCase__     : Union[str, Any]											=					''''''
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	) + token
                       UpperCamelCase__     : str											=					[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE	)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	)
           return out_string.strip()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Tuple[str]:




           """simple docstring"""







           if not os.path.isdir(__SCREAMING_SNAKE_CASE	):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
                 return
           UpperCamelCase__     : Optional[int]											=					os.path.join(
               __SCREAMING_SNAKE_CASE    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(__SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file    ,    __SCREAMING_SNAKE_CASE	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(__SCREAMING_SNAKE_CASE    ,    '''wb'''	) as fi:
                       UpperCamelCase__     : int											=					self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE	)

           return (out_vocab_file,)


