
"""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
_lowercase							   =				'''src/transformers'''
_lowercase							   =				'''docs/source/en'''
_lowercase							   =				'''.'''






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

	A						   =   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 |
_lowercase							   =				'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
_lowercase							   =				re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_lowercase							   =				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.
_lowercase							   =				re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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






def       _snake_case				(		snake_case__   :     Any     ):
	A						   =   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				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     Optional[Any]     ):
	A						   =   2 if text == '✅' or text == '❌' else len(snake_case__     )
	A						   =   (width - text_length) // 2
	A						   =   width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def       _snake_case				(		):
	A						   =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	A						   =   {
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	A						   =   {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.
	A						   =   collections.defaultdict(snake_case__     )
	A						   =   collections.defaultdict(snake_case__     )
	A						   =   collections.defaultdict(snake_case__     )
	A						   =   collections.defaultdict(snake_case__     )
	A						   =   collections.defaultdict(snake_case__     )

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

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

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

	A						   =   {True: '✅', False: '❌'}
	for name in model_names:
		A						   =   model_name_to_prefix[name]
		A						   =   [
		    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				(		snake_case__   :     Dict=False     ):
	A   ,      A   ,      A   ,      A						   =   _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-->'	,      )
	A						   =   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__":
		_lowercase							   =				argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		_lowercase							   =				parser.parse_args()

		check_model_table(args.fix_and_overwrite)

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)