




import argparse
from collections import defaultdict

import yaml


__UpperCamelCase					:       List[Any]    					=      "docs/source/en/_toctree.yml"





def 		__A		(	__lowerCamelCase    )      ->       Tuple:
	a										=							defaultdict(_SCREAMING_SNAKE_CASE    )
	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(_SCREAMING_SNAKE_CASE    ) > 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(_SCREAMING_SNAKE_CASE							,			key=lambda __lowerCamelCase    : s["title"].lower()    )





def 		__A		(	__lowerCamelCase=False    )      ->       Dict:


	with open(_SCREAMING_SNAKE_CASE							,			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(_SCREAMING_SNAKE_CASE    ) if 'sections' in section]
	a										=							False
	for idx, modality_doc in modalities_docs:
		a										=							modality_doc['sections']
		a										=							clean_model_doc_toc(_SCREAMING_SNAKE_CASE    )

		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(_SCREAMING_SNAKE_CASE							,			"""w"""							,			encoding="""utf-8"""    ) as f:
				f.write(yaml.dump(_SCREAMING_SNAKE_CASE							,			allow_unicode=_SCREAMING_SNAKE_CASE    )    )
		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__":
		__UpperCamelCase					:       int    					=      argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		__UpperCamelCase					:       Optional[int]    					=      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_tokenizers_available,
    is_torch_available,
)


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ReformerAttention""",
      """ReformerForMaskedLM""",
      """ReformerForQuestionAnswering""",
      """ReformerForSequenceClassification""",
      """ReformerLayer""",
      """ReformerModel""",
      """ReformerModelWithLMHead""",
      """ReformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
