



'''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
UpperCamelCase__			:      Union[str, Any]        =					'''src/transformers'''
UpperCamelCase__			:      int        =					'''docs/source/en'''
UpperCamelCase__			:      Dict        =					'''.'''


def 							lowerCAmelCase_  (      _lowerCamelCase:  Optional[Any]			, _lowerCamelCase:  Union[str, Any]			, _lowerCamelCase:  Tuple   ):
		with open(_lowerCamelCase			, """r"""			, encoding="""utf-8"""			, newline="""\n"""   ) as f:
				__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						f.readlines()
		# Find the start prompt.
		__SCREAMING_SNAKE_CASE			:						str       			=						0
		while not lines[start_index].startswith(_lowerCamelCase   ):
				start_index += 1
		start_index += 1

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


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


def 							lowerCAmelCase_  (      _lowerCamelCase:  List[Any]   ):
		__SCREAMING_SNAKE_CASE			:						Dict       			=						re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""			, _lowerCamelCase   )
		return [m.group(0   ) for m in matches]


def 							lowerCAmelCase_  (      _lowerCamelCase:  Dict			, _lowerCamelCase:  List[str]   ):
		__SCREAMING_SNAKE_CASE			:						Tuple       			=						2 if text == '''✅''' or text == '''❌''' else len(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						str       			=						(width - text_length) // 2
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						width - text_length - left_indent
		return " " * left_indent + text + " " * right_indent


def 							lowerCAmelCase_  (      ):
		__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		__SCREAMING_SNAKE_CASE			:						Dict       			=						{
		    name: config_maping_names[code]
		    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_maping_names
		}
		__SCREAMING_SNAKE_CASE			:						Optional[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.
		__SCREAMING_SNAKE_CASE			:						Tuple       			=						collections.defaultdict(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						Dict       			=						collections.defaultdict(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						Tuple       			=						collections.defaultdict(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						collections.defaultdict(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						collections.defaultdict(_lowerCamelCase   )

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

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

		# Build the table per se
		__SCREAMING_SNAKE_CASE			:						List[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"

		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						{True: '''✅''', False: '''❌'''}
		for name in model_names:
				__SCREAMING_SNAKE_CASE			:						Dict       			=						model_name_to_prefix[name]
				__SCREAMING_SNAKE_CASE			:						int       			=						[
				    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 							lowerCAmelCase_  (      _lowerCamelCase:  Tuple=False   ):
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						_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-->"""			, )
		__SCREAMING_SNAKE_CASE			:						Optional[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__":
						UpperCamelCase__			:      List[Any]        =					argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						UpperCamelCase__			:      List[str]        =					parser.parse_args()

						check_model_table(args.fix_and_overwrite)
UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}