

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


a		=logging.get_logger(__name__)

a		={
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  int										=				'''markuplm'''
							def __init__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any=3_0_5_2_2       ,SCREAMING_SNAKE_CASE__		:      List[Any]=7_6_8       ,SCREAMING_SNAKE_CASE__		:      int=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=1_2       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      Optional[int]="gelu"       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Tuple=0.1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=5_1_2       ,SCREAMING_SNAKE_CASE__		:      List[str]=2       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.02       ,SCREAMING_SNAKE_CASE__		:      Any=1E-12       ,SCREAMING_SNAKE_CASE__		:      str=0       ,SCREAMING_SNAKE_CASE__		:      Dict=0       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2       ,SCREAMING_SNAKE_CASE__		:      str=2_5_6       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_0_2_4       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2_1_6       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_0_0_1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=3_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5_0       ,SCREAMING_SNAKE_CASE__		:      Dict="absolute"       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      List[Any]=None       ,**SCREAMING_SNAKE_CASE__		:      Tuple       ,):
								super().__init__(
								    pad_token_id=SCREAMING_SNAKE_CASE__       ,bos_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    List[Any] 	=			vocab_size
								__lowerCamelCase    :    Any 	=			hidden_size
								__lowerCamelCase    :    str 	=			num_hidden_layers
								__lowerCamelCase    :    Tuple 	=			num_attention_heads
								__lowerCamelCase    :    str 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			intermediate_size
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    Optional[int] 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Dict 	=			max_position_embeddings
								__lowerCamelCase    :    Any 	=			type_vocab_size
								__lowerCamelCase    :    List[Any] 	=			initializer_range
								__lowerCamelCase    :    str 	=			layer_norm_eps
								__lowerCamelCase    :    int 	=			position_embedding_type
								__lowerCamelCase    :    Optional[int] 	=			use_cache
								__lowerCamelCase    :    int 	=			classifier_dropout
								# additional properties
								__lowerCamelCase    :    Tuple 	=			max_depth
								__lowerCamelCase    :    str 	=			max_xpath_tag_unit_embeddings
								__lowerCamelCase    :    Optional[Any] 	=			max_xpath_subs_unit_embeddings
								__lowerCamelCase    :    Optional[int] 	=			tag_pad_id
								__lowerCamelCase    :    int 	=			subs_pad_id
								__lowerCamelCase    :    Any 	=			xpath_unit_hidden_size







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		="""src/transformers"""
a		="""docs/source/en"""
a		="""."""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	with open(lowerCamelCase__     ,			'r'     ,			encoding='utf-8'     ,			newline='\n'      ) as f:
		__lowerCamelCase    :    Any 	=			f.readlines()
	# Find the start prompt.
	__lowerCamelCase    :    List[str] 	=			0
	while not lines[start_index].startswith(lowerCamelCase__      ):
		start_index += 1
	start_index += 1

	__lowerCamelCase    :    int 	=			start_index
	while not lines[end_index].startswith(lowerCamelCase__      ):
		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		="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
a		=re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a		=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		=re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
a		=direct_transformers_import(TRANSFORMERS_PATH)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[str]:
	__lowerCamelCase    :    int 	=			re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,			lowerCamelCase__      )
	return [m.group(0      ) for m in matches]






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






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

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

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

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

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

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__=False      )  ->					Any:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			_find_text_in_file(
	    filename=os.path.join(lowerCamelCase__     ,			'index.md'      )     ,			start_prompt='<!--This table is updated automatically from the auto modules'     ,			end_prompt='<!-- End table-->'     ,			)
	__lowerCamelCase    :    List[Any] 	=			get_model_table_from_auto_modules()

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

					check_model_table(args.fix_and_overwrite)




