


'''simple docstring'''






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbart import MBartTokenizer
else:
     lowerCAmelCase:      List[Any]		 =		None


lowerCAmelCase:      List[str]		 =		logging.get_logger(__name__)


lowerCAmelCase:      Optional[Any]		 =		{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase:      Tuple		 =		{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase:      Optional[int]		 =		{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
lowerCAmelCase:      Optional[int]		 =		["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]

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

    lowercase__				=					[]
    lowercase__				=					[]







    def __init__(   self				:						List[str]		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						List[Any]="<s>"		,    __snake_case				:						List[Any]="</s>"		,    __snake_case				:						List[Any]="</s>"		,    __snake_case				:						Dict="<s>"		,    __snake_case				:						int="<unk>"		,    __snake_case				:						Tuple="<pad>"		,    __snake_case				:						Optional[int]="<mask>"		,    __snake_case				:						List[str]=None		,    __snake_case				:						Tuple=None		,    __snake_case				:						str=None		,    **__snake_case				:						Optional[Any]		,    ):
      # Mask token behave like a normal word, i.e. include the space before it
      a		:      int       =  AddedToken(A__		,    lstrip=A__		,    rstrip=A__			) if isinstance(A__		,    A__			) else mask_token

      super().__init__(
          vocab_file=A__		,    tokenizer_file=A__		,    bos_token=A__		,    eos_token=A__		,    sep_token=A__		,    cls_token=A__		,    unk_token=A__		,    pad_token=A__		,    mask_token=A__		,    src_lang=A__		,    tgt_lang=A__		,    additional_special_tokens=A__		,    **A__		,    )

      a		:      Dict       =  vocab_file
      a		:      Dict       =  False if not self.vocab_file else True

      a		:      Dict       =  FAIRSEQ_LANGUAGE_CODES.copy()

      if additional_special_tokens is not None:
        # Only add those special tokens if they are not already there.
        _additional_special_tokens.extend(
            [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

      self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
      a		:      List[str]       =  {
          lang_code: self.convert_tokens_to_ids(A__			) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      a		:      List[str]       =  src_lang if src_lang is not None else 'en_XX'
      a		:      Union[str, Any]       =  self.convert_tokens_to_ids(self._src_lang			)
      a		:      Union[str, Any]       =  tgt_lang
      self.set_src_lang_special_tokens(self._src_lang			)







    @property
    def     lowercase_ (   self				:						str			):
      return self._src_lang







    @src_lang.setter
    def     lowercase_ (   self				:						Any		,    __snake_case				:						Optional[Any]			):
      a		:      List[str]       =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang			)







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int = None			):
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						str		,    __snake_case				:						Dict = None			):
      a		:      Any       =  [self.sep_token_id]
      a		:      int       =  [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[Any]		,    __snake_case				:						str		,    __snake_case				:						Dict		,    __snake_case				:						Any		,    __snake_case				:						List[Any]		,    **__snake_case				:						Union[str, Any]			):
      if src_lang is None or tgt_lang is None:
        raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
      a		:      Optional[Any]       =  src_lang
      a		:      List[str]       =  self(A__		,    add_special_tokens=A__		,    return_tensors=A__		,    **A__			)
      a		:      List[str]       =  self.convert_tokens_to_ids(A__			)
      a		:      List[Any]       =  tgt_lang_id
      return inputs







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						List[Any] = "en_XX"		,    __snake_case				:						Tuple = None		,    __snake_case				:						Optional[Any] = "ro_RO"		,    **__snake_case				:						Optional[int]		,    ):
      a		:      Union[str, Any]       =  src_lang
      a		:      Any       =  tgt_lang
      return super().prepare_seqaseq_batch(A__		,    A__		,    **A__			)







    def     lowercase_ (   self				:						Any			):
      return self.set_src_lang_special_tokens(self.src_lang			)







    def     lowercase_ (   self				:						Dict			):
      return self.set_tgt_lang_special_tokens(self.tgt_lang			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Dict			):
      a		:      Dict       =  self.convert_tokens_to_ids(A__			)
      a		:      str       =  []
      a		:      List[Any]       =  [self.eos_token_id, self.cur_lang_code]

      a		:      List[str]       =  self.convert_ids_to_tokens(self.prefix_tokens			)
      a		:      Dict       =  self.convert_ids_to_tokens(self.suffix_tokens			)

      a		:      Optional[Any]       =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str		,    pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens			)			)		,    )







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Union[str, Any]			):
      a		:      List[Any]       =  self.convert_tokens_to_ids(A__			)
      a		:      Dict       =  []
      a		:      Dict       =  [self.eos_token_id, self.cur_lang_code]

      a		:      int       =  self.convert_ids_to_tokens(self.prefix_tokens			)
      a		:      Optional[Any]       =  self.convert_ids_to_tokens(self.suffix_tokens			)

      a		:      List[Any]       =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str		,    pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str		,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,    self.prefix_tokens + self.suffix_tokens			)			)		,    )







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						int		,    __snake_case				:						int = None			):
      if not self.can_save_slow_tokenizer:
        raise ValueError(
            'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
            'tokenizer.'			)

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

      if os.path.abspath(self.vocab_file			) != os.path.abspath(A__			):
        copyfile(self.vocab_file		,    A__			)

      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
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)