
'''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


__lowercase				: Any											=  logging.get_logger(__name__)

__lowercase				: List[str]											=  {'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowercase				: List[str]											=  {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

__lowercase				: List[str]											=  {
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__lowercase				: List[str]											=  '''▁'''

class 						__lowercase					(	__UpperCAmelCase     ):
			lowerCamelCase      :		Any						   =      VOCAB_FILES_NAMES
			lowerCamelCase      :		Any						   =      PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase      :		str						   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase      :		Tuple						   =      ['''input_ids''', '''attention_mask''']

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

								lowerCamelCase_       :   Optional[int]  =		{} 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__      , sp_model_kwargs=self.sp_model_kwargs      , **lowerCamelCase__      , )

								lowerCamelCase_       :   int  =		vocab_file
								lowerCamelCase_       :   Union[str, Any]  =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(str(lowerCamelCase__   )   )

								lowerCamelCase_       :   List[Any]  =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

								lowerCamelCase_       :   Optional[Any]  =		len(self.sp_model   ) - 1
								lowerCamelCase_       :   Optional[int]  =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

			def 							UpperCAmelCase__			(self      , A      , A = None   ):

								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase_       :   Tuple  =		[self.cls_token_id]
								lowerCamelCase_       :   Union[str, Any]  =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

			def 							UpperCAmelCase__			(self      , A      , A = None      , A = False   ):
								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      , A      , A = None   ):
								lowerCamelCase_       :   Any  =		[self.sep_token_id]
								lowerCamelCase_       :   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   ):
								return len(self.sp_model   )

			def 							UpperCAmelCase__			(self   ):
								lowerCamelCase_       :   Union[str, Any]  =		{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      , A   ):
								return self.sp_model.encode(lowerCamelCase__      , out_type=lowerCamelCase__   )

			def 							UpperCAmelCase__			(self      , A   ):
								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								lowerCamelCase_       :   List[Any]  =		self.sp_model.PieceToId(lowerCamelCase__   )

								return spm_id if spm_id else self.unk_token_id

			def 							UpperCAmelCase__			(self      , A   ):
								if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(lowerCamelCase__   )

			def 							UpperCAmelCase__			(self      , A   ):
								lowerCamelCase_       :   str  =		[]
								lowerCamelCase_       :   str  =		""""""
								lowerCamelCase_       :   Any  =		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
																		lowerCamelCase_       :   Any  =		True
																		lowerCamelCase_       :   Optional[Any]  =		[]
													else:
																		current_sub_tokens.append(lowerCamelCase__   )
																		lowerCamelCase_       :   Any  =		False
								out_string += self.sp_model.decode(lowerCamelCase__   )
								return out_string.strip()

			def __getstate__(self   ):
								lowerCamelCase_       :   List[str]  =		self.__dict__.copy()
								lowerCamelCase_       :   str  =		None
								return state

			def __setstate__(self      , A   ):
								lowerCamelCase_       :   Union[str, Any]  =		d

								# for backward compatibility
								if not hasattr(self      , '''sp_model_kwargs'''   ):
													lowerCamelCase_       :   List[str]  =		{}

								lowerCamelCase_       :   int  =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(self.vocab_file   )






			def 							UpperCAmelCase__			(self      , A      , A = None   ):
								if not os.path.isdir(lowerCamelCase__   ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
													return
								lowerCamelCase_       :   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:
																		lowerCamelCase_       :   List[Any]  =		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)