'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case						  =		logging.get_logger(__name__)


__snake_case						  =		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case						  =		{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__snake_case						  =		{
    """allenai/led-base-16384""": 1_6384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   A_      (       )			->Tuple:
  lowercase_     =       (
      list(range(ord("""!"""						) ,	ord("""~"""						) + 1						)						) + list(range(ord("""¡"""						) ,	ord("""¬"""						) + 1						)						) + list(range(ord("""®"""						) ,	ord("""ÿ"""						) + 1						)						)
  )
  lowercase_     =       bs[:]
  lowercase_     =       0
  for b in range(2**8						):
    if b not in bs:
      bs.append(SCREAMING_SNAKE_CASE_						)
      cs.append(2**8 + n						)
      n += 1
  lowercase_     =       [chr(SCREAMING_SNAKE_CASE_						) for n in cs]
  return dict(zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       set()
  lowercase_     =       word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)						)
    lowercase_     =       char
  return pairs





class       _a    (		__a      ):


  """simple docstring"""

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




  def __init__(		self      :       List[str]   ,				lowercase_      :       Any   ,				lowercase_      :       str   ,				lowercase_      :       Optional[Any]="replace"   ,				lowercase_      :       int="<s>"   ,				lowercase_      :       Any="</s>"   ,				lowercase_      :       str="</s>"   ,				lowercase_      :       Tuple="<s>"   ,				lowercase_      :       Optional[Any]="<unk>"   ,				lowercase_      :       int="<pad>"   ,				lowercase_      :       str="<mask>"   ,				lowercase_      :       int=False   ,				**lowercase_      :       Tuple   ,				):







    '''simple docstring'''


    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else bos_token
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else eos_token
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else sep_token
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else cls_token
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else unk_token
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    lowercase_     =       AddedToken(lowercase_   ,				lstrip=lowercase_   ,				rstrip=lowercase_      ) if isinstance(lowercase_   ,				lowercase_      ) else mask_token

    super().__init__(
        errors=lowercase_   ,				bos_token=lowercase_   ,				eos_token=lowercase_   ,				unk_token=lowercase_   ,				sep_token=lowercase_   ,				cls_token=lowercase_   ,				pad_token=lowercase_   ,				mask_token=lowercase_   ,				add_prefix_space=lowercase_   ,				**lowercase_   ,				)

    with open(lowercase_   ,				encoding="""utf-8"""      ) as vocab_handle:
      lowercase_     =       json.load(lowercase_      )
    lowercase_     =       {v: k for k, v in self.encoder.items()}
    lowercase_     =       errors  # how to handle errors in decoding
    lowercase_     =       bytes_to_unicode()
    lowercase_     =       {v: k for k, v in self.byte_encoder.items()}
    with open(lowercase_   ,				encoding="""utf-8"""      ) as merges_handle:
      lowercase_     =       merges_handle.read().split("""\n"""      )[1:-1]
    lowercase_     =       [tuple(merge.split()      ) for merge in bpe_merges]
    lowercase_     =       dict(zip(lowercase_   ,				range(len(lowercase_      )      )      )      )
    lowercase_     =       {}
    lowercase_     =       add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    lowercase_     =       re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""      )




  @property
  # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    return len(self.encoder      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    return dict(self.encoder   ,				**self.added_tokens_encoder      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    if token in self.cache:
      return self.cache[token]
    lowercase_     =       tuple(lowercase_      )
    lowercase_     =       get_pairs(lowercase_      )

    if not pairs:
      return token

    while True:
      lowercase_     =       min(lowercase_   ,				key=lambda lowercase_      : self.bpe_ranks.get(lowercase_   ,				float("""inf"""      )      )      )
      if bigram not in self.bpe_ranks:
        break
      lowercase_     ,	lowercase_     =       bigram
      lowercase_     =       []
      lowercase_     =       0
      while i < len(lowercase_      ):
        try:
          lowercase_     =       word.index(lowercase_   ,				lowercase_      )
        except ValueError:
          new_word.extend(word[i:]      )
          break
        else:
          new_word.extend(word[i:j]      )
          lowercase_     =       j

        if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
          new_word.append(first + second      )
          i += 2
        else:
          new_word.append(word[i]      )
          i += 1
      lowercase_     =       tuple(lowercase_      )
      lowercase_     =       new_word
      if len(lowercase_      ) == 1:
        break
      else:
        lowercase_     =       get_pairs(lowercase_      )
    lowercase_     =       """ """.join(lowercase_      )
    lowercase_     =       word
    return word




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       []
    for token in re.findall(self.pat   ,				lowercase_      ):
      lowercase_     =       """""".join(
          self.byte_encoder[b] for b in token.encode("""utf-8"""      )      )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
      bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_      ).split(""" """      )      )
    return bpe_tokens




  def 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       str      ):







    '''simple docstring'''


    return self.encoder.get(lowercase_   ,				self.encoder.get(self.unk_token      )      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


    return self.decoder.get(lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       """""".join(lowercase_      )
    lowercase_     =       bytearray([self.byte_decoder[c] for c in text]      ).decode("""utf-8"""   ,				errors=self.errors      )
    return text




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       str   ,				lowercase_      :       Optional[str] = None      ):







    '''simple docstring'''


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

    with open(lowercase_   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
      f.write(json.dumps(self.encoder   ,				indent=2   ,				sort_keys=lowercase_   ,				ensure_ascii=lowercase_      ) + """\n"""      )

    lowercase_     =       0
    with open(lowercase_   ,				"""w"""   ,				encoding="""utf-8"""      ) as writer:
      writer.write("""#version: 0.2\n"""      )
      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,				key=lambda lowercase_      : kv[1]      ):
        if index != token_index:
          logger.warning(
              F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
              """ Please check that the tokenizer is not corrupted!"""      )
          lowercase_     =       token_index
        writer.write(""" """.join(lowercase_      ) + """\n"""      )
        index += 1

    return vocab_file, merge_file




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[int]   ,				lowercase_      :       Optional[List[int]] = None      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       List[int]   ,				lowercase_      :       Optional[List[int]] = None   ,				lowercase_      :       bool = False      ):







    '''simple docstring'''


    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=lowercase_   ,				token_ids_a=lowercase_   ,				already_has_special_tokens=lowercase_      )

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




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[int]   ,				lowercase_      :       Optional[List[int]] = None      ):







    '''simple docstring'''


    lowercase_     =       [self.sep_token_id]
    lowercase_     =       [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]




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       List[Any]=False   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    lowercase_     =       kwargs.pop("""add_prefix_space"""   ,				self.add_prefix_space      )
    if (is_split_into_words or add_prefix_space) and (len(lowercase_      ) > 0 and not text[0].isspace()):
      lowercase_     =       """ """ + text
    return (text, kwargs)





  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       Union[Dict[str, EncodedInput], BatchEncoding]   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,				lowercase_      :       Optional[int] = None   ,				lowercase_      :       Optional[bool] = None   ,				):







    '''simple docstring'''


    lowercase_     =       super()._pad(
        encoded_inputs=lowercase_   ,				max_length=lowercase_   ,				padding_strategy=lowercase_   ,				pad_to_multiple_of=lowercase_   ,				return_attention_mask=lowercase_   ,				)

    # Load from model defaults
    if return_attention_mask is None:
      lowercase_     =       """attention_mask""" in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
      lowercase_     =       encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      lowercase_     =       len(encoded_inputs["""global_attention_mask"""]      ) != len(lowercase_      )

      if needs_to_be_padded:
        lowercase_     =       len(lowercase_      ) - len(encoded_inputs["""global_attention_mask"""]      )

        if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          lowercase_     =       (
              encoded_inputs["""global_attention_mask"""] + [-1] * difference
          )
        elif self.padding_side == "left":
          lowercase_     =       [-1] * difference + encoded_inputs[
              """global_attention_mask"""
          ]
        else:
          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side      )      )

    return encoded_inputs


'''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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  with open(SCREAMING_SNAKE_CASE_ ,	"""r""" ,	encoding="""utf-8""" ,	newline="""\n"""						) as f:
    lowercase_     =       f.readlines()
  # Find the start prompt.
  lowercase_     =       0
  while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_						):
    start_index += 1
  start_index += 1

  lowercase_     =       start_index
  while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_						):
    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   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     =       re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,	SCREAMING_SNAKE_CASE_						)
  return [m.group(0						) for m in matches]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  lowercase_     =       2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       (width - text_length) // 2
  lowercase_     =       width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   A_      (       )			->Tuple:
  lowercase_     =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  lowercase_     =       {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  lowercase_     =       {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_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       collections.defaultdict(SCREAMING_SNAKE_CASE_						)

  # Let's lookup through all transformers object (once).
  for attr_name in dir(SCREAMING_SNAKE_CASE_						):
    lowercase_     =       None
    if attr_name.endswith("""Tokenizer"""						):
      lowercase_     =       slow_tokenizers
      lowercase_     =       attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""						):
      lowercase_     =       fast_tokenizers
      lowercase_     =       attr_name[:-13]
    elif _re_tf_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       tf_models
      lowercase_     =       _re_tf_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_flax_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       flax_models
      lowercase_     =       _re_flax_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]
    elif _re_pt_models.match(SCREAMING_SNAKE_CASE_						) is not None:
      lowercase_     =       pt_models
      lowercase_     =       _re_pt_models.match(SCREAMING_SNAKE_CASE_						).groups()[0]

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

    # Let's build that table!
  lowercase_     =       list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  lowercase_     =       ["""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_     =       [len(SCREAMING_SNAKE_CASE_						) + 2 for c in columns]
  lowercase_     =       max([len(SCREAMING_SNAKE_CASE_						) for name in model_names]						) + 2

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

  lowercase_     =       {True: """✅""", False: """❌"""}
  for name in model_names:
    lowercase_     =       model_name_to_prefix[name]
    lowercase_     =       [
        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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) for l, w in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)]						) + "|\n"
  return table

def   A_      (       SCREAMING_SNAKE_CASE_=False						)			->Dict:
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       _find_text_in_file(
      filename=os.path.join(SCREAMING_SNAKE_CASE_ ,	"""index.md"""						) ,	start_prompt="""<!--This table is updated automatically from the auto modules""" ,	end_prompt="""<!-- End table-->""" ,	)
  lowercase_     =       get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(SCREAMING_SNAKE_CASE_ ,	"""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)

