


"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{'vocab_file': 'sentencepiece.bpe.model'}

__snake_case    					=					{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case    					=					{
    'camembert-base': 512,
}

__snake_case    					=					'▁'

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    VOCAB_FILES_NAMES
   _a   :       List[Any]									=    PRETRAINED_VOCAB_FILES_MAP
   _a   :       List[str]									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a   :       Union[str, Any]									=    ['''input_ids''', '''attention_mask''']


   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="</s>" ,     lowerCamelCase__="<s>" ,     lowerCamelCase__="<unk>" ,     lowerCamelCase__="<pad>" ,     lowerCamelCase__="<mask>" ,     lowerCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] ,     lowerCamelCase__ = None ,     **lowerCamelCase__ ,     )      ->							None:
    # Mask token behave like a normal word, i.e. include the space before it
    lowercase__       :       Optional[int]				=						AddedToken(lowerCamelCase__ ,     lstrip=lowerCamelCase__ ,     rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__ ,     lowerCamelCase__						) else mask_token

    lowercase__       :       Dict				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=lowerCamelCase__ ,     eos_token=lowerCamelCase__ ,     unk_token=lowerCamelCase__ ,     sep_token=lowerCamelCase__ ,     cls_token=lowerCamelCase__ ,     pad_token=lowerCamelCase__ ,     mask_token=lowerCamelCase__ ,     additional_special_tokens=lowerCamelCase__ ,     sp_model_kwargs=self.sp_model_kwargs ,     **lowerCamelCase__ ,     )
    lowercase__       :       Optional[int]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(lowerCamelCase__						)						)
    lowercase__       :       str				=						vocab_file
    # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
    # sentencepiece vocabulary (this is the case for <s> and </s>
    lowercase__       :       Optional[int]				=						{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
    lowercase__       :       int				=						len(self.fairseq_tokens_to_ids						)
    lowercase__       :       Dict				=						len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
    lowercase__       :       List[Any]				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:

    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    lowercase__       :       Any				=						[self.cls_token_id]
    lowercase__       :       str				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None ,     lowerCamelCase__ = False						)      ->							List[int]:
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowerCamelCase__ ,     token_ids_a=lowerCamelCase__ ,     already_has_special_tokens=lowerCamelCase__						)

    if token_ids_a is None:
     return [1] + ([0] * len(lowerCamelCase__						)) + [1]
    return [1] + ([0] * len(lowerCamelCase__						)) + [1, 1] + ([0] * len(lowerCamelCase__						)) + [1]


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							List[int]:
    lowercase__       :       int				=						[self.sep_token_id]
    lowercase__       :       Tuple				=						[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep						) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Dict				=						{self.convert_ids_to_tokens(lowerCamelCase__						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[str]:
    return self.sp_model.encode(lowerCamelCase__ ,     out_type=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    elif self.sp_model.PieceToId(lowerCamelCase__						) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
    return self.fairseq_offset + self.sp_model.PieceToId(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							int:
    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Dict				=						[]
    lowercase__       :       Dict				=						""""""
    lowercase__       :       Tuple				=						False
    for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
      if not prev_is_special:
       out_string += " "
      out_string += self.sp_model.decode(lowerCamelCase__						) + token
      lowercase__       :       List[Any]				=						True
      lowercase__       :       str				=						[]
     else:
      current_sub_tokens.append(lowerCamelCase__						)
      lowercase__       :       Optional[Any]				=						False
    out_string += self.sp_model.decode(lowerCamelCase__						)
    return out_string.strip()


   def __getstate__(						self						)      ->							int:
    lowercase__       :       Dict				=						self.__dict__.copy()
    lowercase__       :       Union[str, Any]				=						None
    return state


   def __setstate__(						self ,     lowerCamelCase__						)      ->							int:
    lowercase__       :       Any				=						d

    # for backward compatibility
    if not hasattr(self ,     """sp_model_kwargs"""						):
     lowercase__       :       Tuple				=						{}

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







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ = None						)      ->							Tuple[str]:
    if not os.path.isdir(lowerCamelCase__						):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
     return
    lowercase__       :       Any				=						os.path.join(
        lowerCamelCase__ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

    return (out_vocab_file,)



"""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
__snake_case    					=					'src/transformers'
__snake_case    					=					'docs/source/en'
__snake_case    					=					'.'


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Any  ):
 with open(lowerCamelCase__       ,		"""r"""       ,		encoding="""utf-8"""       ,		newline="""\n"""  ) as f:
  lowercase__       :       List[str]				=						f.readlines()
 # Find the start prompt.
 lowercase__       :       str				=						0
 while not lines[start_index].startswith(lowerCamelCase__  ):
  start_index += 1
 start_index += 1

 lowercase__       :       Optional[Any]				=						start_index
 while not lines[end_index].startswith(lowerCamelCase__  ):
  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 |
__snake_case    					=					'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__snake_case    					=					re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__snake_case    					=					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.
__snake_case    					=					re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__snake_case    					=					direct_transformers_import(TRANSFORMERS_PATH)


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]  ):
 lowercase__       :       Union[str, Any]				=						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""       ,		lowerCamelCase__  )
 return [m.group(0  ) for m in matches]


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[int]       ,		lowerCamelCase__	:							int  ):
 lowercase__       :       Any				=						2 if text == """✅""" or text == """❌""" else len(lowerCamelCase__  )
 lowercase__       :       int				=						(width - text_length) // 2
 lowercase__       :       List[str]				=						width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def    _lowerCamelCase   (     ):
 lowercase__       :       List[str]				=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase__       :       Optional[int]				=						{
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase__       :       str				=						{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.
 lowercase__       :       Tuple				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[Any]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Dict				=						collections.defaultdict(lowerCamelCase__  )
 lowercase__       :       Union[str, Any]				=						collections.defaultdict(lowerCamelCase__  )

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

  if lookup_dict is not None:
   while len(lowerCamelCase__  ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowercase__       :       Tuple				=						True
     break
    # Try again after removing the last word in the name
    lowercase__       :       Union[str, Any]				=						"""""".join(camel_case_split(lowerCamelCase__  )[:-1]  )

    # Let's build that table!
 lowercase__       :       Union[str, Any]				=						list(model_name_to_config.keys()  )
 model_names.sort(key=str.lower  )
 lowercase__       :       List[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).
 lowercase__       :       str				=						[len(lowerCamelCase__  ) + 2 for c in columns]
 lowercase__       :       Tuple				=						max([len(lowerCamelCase__  ) for name in model_names]  ) + 2

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

 lowercase__       :       List[Any]				=						{True: """✅""", False: """❌"""}
 for name in model_names:
  lowercase__       :       int				=						model_name_to_prefix[name]
  lowercase__       :       List[str]				=						[
      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(lowerCamelCase__       ,		lowerCamelCase__  ) for l, w in zip(lowerCamelCase__       ,		lowerCamelCase__  )]  ) + "|\n"
 return table


def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]=False  ):
 lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						_find_text_in_file(
     filename=os.path.join(lowerCamelCase__       ,		"""index.md"""  )       ,		start_prompt="""<!--This table is updated automatically from the auto modules"""       ,		end_prompt="""<!-- End table-->"""       ,		)
 lowercase__       :       Union[str, Any]				=						get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(lowerCamelCase__       ,		"""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__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   __snake_case    					=					parser.parse_args()

   check_model_table(args.fix_and_overwrite)