




'''simple docstring'''


import argparse
from collections import defaultdict

import yaml


_snake_case				:				int       							=		'docs/source/en/_toctree.yml'
def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    defaultdict(UpperCamelCase				)
							for doc in model_doc:
														counts[doc["local"]] += 1
							_a     					=    [key for key, value in counts.items() if value > 1]

							_a     					=    []
							for duplicate_key in duplicates:
														_a     					=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}				)
														if len(UpperCamelCase				) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''				)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}				)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]				)

							# Sort
							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: s["title"].lower()				)
def 		snake_case_   (UpperCamelCase	:  str=False				):





							'''simple docstring'''


							with open(UpperCamelCase			,						encoding='''utf-8'''				) as f:
														_a     					=    yaml.safe_load(f.read()				)

							# Get to the API doc
							_a     					=    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_a     					=    content[api_idx]['''sections''']

							# Then to the model doc
							_a     					=    0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							_a     					=    api_doc[model_idx]['''sections''']

							_a     					=    [(idx, section) for idx, section in enumerate(UpperCamelCase				) if '''sections''' in section]
							_a     					=    False
							for idx, modality_doc in modalities_docs:
														_a     					=    modality_doc['''sections''']
														_a     					=    clean_model_doc_toc(UpperCamelCase				)

														if old_modality_doc != new_modality_doc:
																					_a     					=    True
																					if overwrite:
																												_a     					=    new_modality_doc

							if diff:
														if overwrite:
																					_a     					=    model_doc
																					_a     					=    api_doc
																					with open(UpperCamelCase			,						'''w'''			,						encoding='''utf-8'''				) as f:
																												f.write(yaml.dump(UpperCamelCase			,						allow_unicode=UpperCamelCase				)				)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''				)


if __name__ == "__main__":
				_snake_case				:				List[str]       							=		argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case				:				List[Any]       							=		parser.parse_args()

				check_model_doc(args.fix_and_overwrite)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case				:				List[str]       							=		{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				List[Any]       							=		['XLNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				int       							=		['XLNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				Optional[int]       							=		[
				    'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'XLNetForMultipleChoice',
				    'XLNetForQuestionAnswering',
				    'XLNetForQuestionAnsweringSimple',
				    'XLNetForSequenceClassification',
				    'XLNetForTokenClassification',
				    'XLNetLMHeadModel',
				    'XLNetModel',
				    'XLNetPreTrainedModel',
				    'load_tf_weights_in_xlnet',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case				:				str       							=		[
				    'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFXLNetForMultipleChoice',
				    'TFXLNetForQuestionAnsweringSimple',
				    'TFXLNetForSequenceClassification',
				    'TFXLNetForTokenClassification',
				    'TFXLNetLMHeadModel',
				    'TFXLNetMainLayer',
				    'TFXLNetModel',
				    'TFXLNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet import XLNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlnet_fast import XLNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlnet import (
								    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLNetForMultipleChoice,
								    XLNetForQuestionAnswering,
								    XLNetForQuestionAnsweringSimple,
								    XLNetForSequenceClassification,
								    XLNetForTokenClassification,
								    XLNetLMHeadModel,
								    XLNetModel,
								    XLNetPreTrainedModel,
								    load_tf_weights_in_xlnet,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlnet import (
								    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLNetForMultipleChoice,
								    TFXLNetForQuestionAnsweringSimple,
								    TFXLNetForSequenceClassification,
								    TFXLNetForTokenClassification,
								    TFXLNetLMHeadModel,
								    TFXLNetMainLayer,
								    TFXLNetModel,
								    TFXLNetPreTrainedModel,
								)

else:
				import sys

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

