


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_						:			Optional[Any]													=							'''src/transformers'''
snake_case_						:			Optional[Any]													=							'''docs/source/en'''
snake_case_						:			List[str]													=							'''.'''







def 						lowerCamelCase(   a__	,a__	,a__):
				with open(a__	,'''r'''	,encoding='''utf-8'''	,newline='''\n''') as f:
								_SCREAMING_SNAKE_CASE			  =f.readlines()
				# Find the start prompt.
				_SCREAMING_SNAKE_CASE			  =0
				while not lines[start_index].startswith(a__):
								start_index += 1
				start_index += 1

				_SCREAMING_SNAKE_CASE			  =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 |
snake_case_						:			Tuple													=							'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case_						:			Optional[int]													=							re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case_						:			List[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.
snake_case_						:			List[Any]													=							re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =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__):
				_SCREAMING_SNAKE_CASE			  =2 if text == '''✅''' or text == '''❌''' else len(a__)
				_SCREAMING_SNAKE_CASE			  =(width - text_length) // 2
				_SCREAMING_SNAKE_CASE			  =width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				_SCREAMING_SNAKE_CASE			  ={
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				_SCREAMING_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.
				_SCREAMING_SNAKE_CASE			  =collections.defaultdict(a__)
				_SCREAMING_SNAKE_CASE			  =collections.defaultdict(a__)
				_SCREAMING_SNAKE_CASE			  =collections.defaultdict(a__)
				_SCREAMING_SNAKE_CASE			  =collections.defaultdict(a__)
				_SCREAMING_SNAKE_CASE			  =collections.defaultdict(a__)

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

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

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

				_SCREAMING_SNAKE_CASE			  ={True: '''✅''', False: '''❌'''}
				for name in model_names:
								_SCREAMING_SNAKE_CASE			  =model_name_to_prefix[name]
								_SCREAMING_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(a__	,a__) for l, w in zip(a__	,a__)]) + "|\n"
				return table







def 						lowerCamelCase(   a__=False):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =_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-->'''	,)
				_SCREAMING_SNAKE_CASE			  =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__":
							snake_case_						:			Any													=							argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							snake_case_						:			Dict													=							parser.parse_args()

							check_model_table(args.fix_and_overwrite)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_						:			List[Any]													=							{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			str													=							[
							    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GraphormerForGraphClassification''',
							    '''GraphormerModel''',
							    '''GraphormerPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							snake_case_						:			Union[str, Any]													=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)