



'''simple docstring'''






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

lowerCAmelCase:      str		 =		logging.get_logger(__name__)


lowerCAmelCase:      str		 =		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase:      Optional[int]		 =		{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

lowerCAmelCase:      List[str]		 =		{'facebook/blenderbot-3B': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    lowerCamelCase__ (		):
  a		:      str       =  (
      list(range(ord('!'       )				,    ord('~'       ) + 1       )       ) + list(range(ord('¡'       )				,    ord('¬'       ) + 1       )       ) + list(range(ord('®'       )				,    ord('ÿ'       ) + 1       )       )
  )
  a		:      List[Any]       =  bs[:]
  a		:      Optional[int]       =  0
  for b in range(2**8       ):
    if b not in bs:
      bs.append(_A       )
      cs.append(2**8 + n       )
      n += 1
  a		:      Tuple       =  [chr(_A       ) for n in cs]
  return dict(zip(_A				,    _A       )       )
def    lowerCamelCase__ (		_A       ):
  a		:      Union[str, Any]       =  set()
  a		:      Dict       =  word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)       )
    a		:      Optional[Any]       =  char
  return pairs

class 							a__(      lowerCamelCase__			):
    lowercase__				=					VOCAB_FILES_NAMES
    lowercase__				=					PRETRAINED_VOCAB_FILES_MAP
    lowercase__				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__				=					["""input_ids""", """attention_mask"""]







    def __init__(   self				:						int		,    __snake_case				:						Dict		,    __snake_case				:						List[Any]		,    __snake_case				:						List[Any]="replace"		,    __snake_case				:						int="<s>"		,    __snake_case				:						Optional[int]="</s>"		,    __snake_case				:						Optional[int]="</s>"		,    __snake_case				:						Optional[int]="<s>"		,    __snake_case				:						Tuple="<unk>"		,    __snake_case				:						int="<pad>"		,    __snake_case				:						Any="<mask>"		,    __snake_case				:						List[str]=False		,    **__snake_case				:						List[str]		,    ):
      a		:      str       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else bos_token
      a		:      int       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else eos_token
      a		:      Tuple       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else sep_token
      a		:      Any       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else cls_token
      a		:      Tuple       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else unk_token
      a		:      Optional[Any]       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      a		:      Optional[Any]       =  AddedToken(__snake_case		,    lstrip=__snake_case		,    rstrip=__snake_case			) if isinstance(__snake_case		,    __snake_case			) else mask_token

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

      with open(__snake_case		,    encoding='utf-8'			) as vocab_handle:
        a		:      Any       =  json.load(__snake_case			)
      a		:      Tuple       =  {v: k for k, v in self.encoder.items()}
      a		:      List[str]       =  errors  # how to handle errors in decoding
      a		:      int       =  bytes_to_unicode()
      a		:      Dict       =  {v: k for k, v in self.byte_encoder.items()}
      with open(__snake_case		,    encoding='utf-8'			) as merges_handle:
        a		:      Union[str, Any]       =  merges_handle.read().split('\n'			)[1:-1]
      a		:      Dict       =  [tuple(merge.split()			) for merge in bpe_merges]
      a		:      int       =  dict(zip(__snake_case		,    range(len(__snake_case			)			)			)			)
      a		:      Optional[int]       =  {}
      a		:      List[str]       =  add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      a		:      Optional[int]       =  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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
    def     lowercase_ (   self				:						Optional[Any]			):
      return len(self.encoder			)







    def     lowercase_ (   self				:						Tuple			):
      return dict(self.encoder		,    **self.added_tokens_encoder			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Union[str, Any]			):
      if token in self.cache:
        return self.cache[token]
      a		:      str       =  tuple(__snake_case			)
      a		:      Any       =  get_pairs(__snake_case			)

      if not pairs:
        return token

      while True:
        a		:      Dict       =  min(__snake_case		,    key=lambda __snake_case			: self.bpe_ranks.get(__snake_case		,    float('inf'			)			)			)
        if bigram not in self.bpe_ranks:
          break
        a     ,			a		:      List[Any]       =  bigram
        a		:      Union[str, Any]       =  []
        a		:      int       =  0
        while i < len(__snake_case			):
          try:
            a		:      str       =  word.index(__snake_case		,    __snake_case			)
          except ValueError:
            new_word.extend(word[i:]			)
            break
          else:
            new_word.extend(word[i:j]			)
            a		:      Dict       =  j

          if word[i] == first and i < len(__snake_case			) - 1 and word[i + 1] == second:
            new_word.append(first + second			)
            i += 2
          else:
            new_word.append(word[i]			)
            i += 1
        a		:      List[Any]       =  tuple(__snake_case			)
        a		:      Tuple       =  new_word
        if len(__snake_case			) == 1:
          break
        else:
          a		:      Optional[int]       =  get_pairs(__snake_case			)
      a		:      List[str]       =  ' '.join(__snake_case			)
      a		:      Dict       =  word
      return word







    def     lowercase_ (   self				:						Any		,    __snake_case				:						Dict			):
      a		:      str       =  []
      for token in re.findall(self.pat		,    __snake_case			):
        a		:      List[Any]       =  ''.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(__snake_case			).split(' '			)			)
      return bpe_tokens







    def     lowercase_ (   self				:						str		,    __snake_case				:						Any			):
      return self.encoder.get(__snake_case		,    self.encoder.get(self.unk_token			)			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						Optional[Any]			):
      return self.decoder.get(__snake_case			)







    def     lowercase_ (   self				:						str		,    __snake_case				:						Optional[Any]			):
      a		:      Dict       =  ''.join(__snake_case			)
      a		:      Any       =  bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'		,    errors=self.errors			)
      return text







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						str		,    __snake_case				:						Optional[str] = None			):
      if not os.path.isdir(__snake_case			):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
        return
      a		:      List[str]       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
      a		:      Dict       =  os.path.join(
          __snake_case		,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

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

      a		:      List[str]       =  0
      with open(__snake_case		,    '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 __snake_case			: 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!'			)
            a		:      Optional[Any]       =  token_index
          writer.write(' '.join(__snake_case			) + '\n'			)
          index += 1

      return vocab_file, merge_file







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None		,    __snake_case				:						bool = False			):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__snake_case		,    token_ids_a=__snake_case		,    already_has_special_tokens=__snake_case			)

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







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):
      a		:      str       =  [self.sep_token_id]
      a		:      List[str]       =  [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     lowercase_ (   self				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						List[str]=False		,    **__snake_case				:						int			):
      a		:      List[str]       =  kwargs.pop('add_prefix_space'		,    self.add_prefix_space			)
      if (is_split_into_words or add_prefix_space) and (len(__snake_case			) > 0 and not text[0].isspace()):
        a		:      str       =  ' ' + text
      return (text, kwargs)







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						List[int]		,    __snake_case				:						Optional[List[int]] = None			):
      return token_ids_a + [self.eos_token_id]







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						"Conversation"			):
      a		:      Tuple       =  []
      for is_user, text in conversation.iter_texts():
        if is_user:
          # We need to space prefix as it's being done within blenderbot
          inputs.append(' ' + text			)
        else:
          # Generated responses should contain them already.
          inputs.append(__snake_case			)

      a		:      Dict       =  '  '.join(__snake_case			)
      a		:      Any       =  self.encode(__snake_case			)
      if len(__snake_case			) > self.model_max_length:
        a		:      int       =  input_ids[-self.model_max_length :]
        logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""			)
      return input_ids




'''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
lowerCAmelCase:      Tuple		 =		'src/transformers'
lowerCAmelCase:      Union[str, Any]		 =		'docs/source/en'
lowerCAmelCase:      Dict		 =		'.'
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  with open(_A				,    'r'				,    encoding='utf-8'				,    newline='\n'       ) as f:
    a		:      Optional[Any]       =  f.readlines()
  # Find the start prompt.
  a		:      Dict       =  0
  while not lines[start_index].startswith(_A       ):
    start_index += 1
  start_index += 1

  a		:      Optional[Any]       =  start_index
  while not lines[end_index].startswith(_A       ):
    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 |
lowerCAmelCase:      List[str]		 =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:      Dict		 =		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:      Any		 =		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.
lowerCAmelCase:      Dict		 =		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:      List[Any]		 =		direct_transformers_import(TRANSFORMERS_PATH)
def    lowerCamelCase__ (		_A       ):
  a		:      Tuple       =  re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,    _A       )
  return [m.group(0       ) for m in matches]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  2 if text == '✅' or text == '❌' else len(_A       )
  a		:      Optional[int]       =  (width - text_length) // 2
  a		:      List[Any]       =  width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent
def    lowerCamelCase__ (		):
  a		:      Union[str, Any]       =  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		:      Tuple       =  {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(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      Union[str, Any]       =  collections.defaultdict(_A       )
  a		:      int       =  collections.defaultdict(_A       )

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

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

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

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

  a		:      str       =  {True: '✅', False: '❌'}
  for name in model_names:
    a		:      Any       =  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(_A				,    _A       ) for l, w in zip(_A				,    _A       )]       ) + "|\n"
  return table
def    lowerCamelCase__ (		_A=False       ):
  a     ,			a     ,			a     ,			a		:      Tuple       =  _find_text_in_file(
      filename=os.path.join(_A				,    'index.md'       )				,    start_prompt='<!--This table is updated automatically from the auto modules'				,    end_prompt='<!-- End table-->'				,    )
  a		:      Optional[int]       =  get_model_table_from_auto_modules()

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

     check_model_table(args.fix_and_overwrite)