






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
A_		:		Any		     =			"""src/transformers"""
A_		:		Dict		     =			"""docs/source/en"""
A_		:		List[str]		     =			"""."""

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[Any]:





  with open(_snake_case     ,   'r'     ,   encoding='utf-8'     ,   newline='\n'  ) as f:
    UpperCamelCase_: List[Any]   				=    f.readlines()
  # Find the start prompt.
  UpperCamelCase_: Dict   				=    0
  while not lines[start_index].startswith(_snake_case  ):
    start_index += 1
  start_index += 1

  UpperCamelCase_: Optional[int]   				=    start_index
  while not lines[end_index].startswith(_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 |
A_		:		Optional[int]		     =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A_		:		List[Any]		     =			re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A_		:		Union[str, 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.
A_		:		Optional[int]		     =			re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
A_		:		Optional[Any]		     =			direct_transformers_import(TRANSFORMERS_PATH)

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:
  UpperCamelCase_: str   				=    re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,   _snake_case  )
  return [m.group(0  ) for m in matches]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Optional[int]   				=    2 if text == '''✅''' or text == '''❌''' else len(_snake_case  )
  UpperCamelCase_: str   				=    (width - text_length) // 2
  UpperCamelCase_: List[str]   				=    width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def   snake_case ()	->    List[str]:
  UpperCamelCase_: Optional[int]   				=    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  UpperCamelCase_: Optional[int]   				=    {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  UpperCamelCase_: Union[str, Any]   				=    {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.
  UpperCamelCase_: List[Any]   				=    collections.defaultdict(_snake_case  )
  UpperCamelCase_: List[Any]   				=    collections.defaultdict(_snake_case  )
  UpperCamelCase_: Optional[Any]   				=    collections.defaultdict(_snake_case  )
  UpperCamelCase_: Optional[int]   				=    collections.defaultdict(_snake_case  )
  UpperCamelCase_: Any   				=    collections.defaultdict(_snake_case  )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(_snake_case  ):
    UpperCamelCase_: List[str]   				=    None
    if attr_name.endswith('Tokenizer'  ):
      UpperCamelCase_: Tuple   				=    slow_tokenizers
      UpperCamelCase_: Union[str, Any]   				=    attr_name[:-9]
    elif attr_name.endswith('TokenizerFast'  ):
      UpperCamelCase_: List[Any]   				=    fast_tokenizers
      UpperCamelCase_: Optional[int]   				=    attr_name[:-1_3]
    elif _re_tf_models.match(_snake_case  ) is not None:
      UpperCamelCase_: List[Any]   				=    tf_models
      UpperCamelCase_: int   				=    _re_tf_models.match(_snake_case  ).groups()[0]
    elif _re_flax_models.match(_snake_case  ) is not None:
      UpperCamelCase_: Tuple   				=    flax_models
      UpperCamelCase_: Optional[int]   				=    _re_flax_models.match(_snake_case  ).groups()[0]
    elif _re_pt_models.match(_snake_case  ) is not None:
      UpperCamelCase_: Optional[Any]   				=    pt_models
      UpperCamelCase_: List[Any]   				=    _re_pt_models.match(_snake_case  ).groups()[0]

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

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

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

  UpperCamelCase_: Optional[int]   				=    {True: '''✅''', False: '''❌'''}
  for name in model_names:
    UpperCamelCase_: Optional[Any]   				=    model_name_to_prefix[name]
    UpperCamelCase_: 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(_snake_case     ,   _snake_case  ) for l, w in zip(_snake_case     ,   _snake_case  )]  ) + "|\n"
  return table

def   snake_case (UpperCAmelCase__=False  )	->    Dict:
  UpperCamelCase_: int   				=    _find_text_in_file(
      filename=os.path.join(_snake_case     ,   'index.md'  )     ,   start_prompt='<!--This table is updated automatically from the auto modules'     ,   end_prompt='<!-- End table-->'     ,   )
  UpperCamelCase_: Optional[Any]   				=    get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(_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__":
   A_		:		Union[str, Any]		     =			argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   A_		:		List[str]		     =			parser.parse_args()

   check_model_table(args.fix_and_overwrite)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )