





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

   __snake_case							       =   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 |
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__    (							snake_case_	:  List[Any]       )      ->				Union[str, Any]:
   __snake_case							       =   re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,      snake_case_       )
   return [m.group(0       ) for m in matches]






def lowerCamelCase__    (							snake_case_	:  Union[str, Any]       ,      snake_case_	:  Tuple       )      ->				Optional[Any]:
   __snake_case							       =   2 if text == '''✅''' or text == '''❌''' else len(snake_case_       )
   __snake_case							       =   (width - text_length) // 2
   __snake_case							       =   width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent






def lowerCamelCase__    (							)      ->				List[str]:
   __snake_case							       =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   __snake_case							       =   {
       name: config_maping_names[code]
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if code in config_maping_names
   }
   __snake_case							       =   {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.
   __snake_case							       =   collections.defaultdict(snake_case_       )
   __snake_case							       =   collections.defaultdict(snake_case_       )
   __snake_case							       =   collections.defaultdict(snake_case_       )
   __snake_case							       =   collections.defaultdict(snake_case_       )
   __snake_case							       =   collections.defaultdict(snake_case_       )

   # Let's lookup through all transformers object (once).
   for attr_name in dir(snake_case_       ):
      __snake_case							       =   None
      if attr_name.endswith('''Tokenizer'''       ):
         __snake_case							       =   slow_tokenizers
         __snake_case							       =   attr_name[:-9]
      elif attr_name.endswith('''TokenizerFast'''       ):
         __snake_case							       =   fast_tokenizers
         __snake_case							       =   attr_name[:-13]
      elif _re_tf_models.match(snake_case_       ) is not None:
         __snake_case							       =   tf_models
         __snake_case							       =   _re_tf_models.match(snake_case_       ).groups()[0]
      elif _re_flax_models.match(snake_case_       ) is not None:
         __snake_case							       =   flax_models
         __snake_case							       =   _re_flax_models.match(snake_case_       ).groups()[0]
      elif _re_pt_models.match(snake_case_       ) is not None:
         __snake_case							       =   pt_models
         __snake_case							       =   _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():
               __snake_case							       =   True
               break
            # Try again after removing the last word in the name
            __snake_case							       =   ''''''.join(camel_case_split(snake_case_       )[:-1]       )

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

   # Build the table per se
   __snake_case							       =   '''|''' + '''|'''.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"

   __snake_case							       =   {True: '''✅''', False: '''❌'''}
   for name in model_names:
      __snake_case							       =   model_name_to_prefix[name]
      __snake_case							       =   [
          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 lowerCamelCase__    (							snake_case_	:  List[str]=False       )      ->				Optional[int]:
   __snake_case       ,	__snake_case       ,	__snake_case       ,	__snake_case							       =   _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-->'''       ,      )
   __snake_case							       =   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__":
    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)








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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

