





"""simple docstring"""
import argparse
from collections import defaultdict

import yaml


lowerCamelCase_        =						"""docs/source/en/_toctree.yml"""



def 	__lowerCamelCase  (				a_	:						int				)     -> Any:
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    defaultdict(a_				)
      for doc in model_doc:
            counts[doc["local"]] += 1
      __SCREAMING_SNAKE_CASE  :str													=    [key for key, value in counts.items() if value > 1]

      __SCREAMING_SNAKE_CASE  :Dict													=    []
      for duplicate_key in duplicates:
            __SCREAMING_SNAKE_CASE  :List[Any]													=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}				)
            if len(a_				) > 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(a_					,  key=lambda a_				: s["title"].lower()				)



def 	__lowerCamelCase  (				a_	:						List[str]=False				)     -> Optional[Any]:
      with open(a_					,  encoding='''utf-8'''				) as f:
            __SCREAMING_SNAKE_CASE  :str													=    yaml.safe_load(f.read()				)

      # Get to the API doc
      __SCREAMING_SNAKE_CASE  :Optional[int]													=    0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      __SCREAMING_SNAKE_CASE  :str													=    content[api_idx]["sections"]

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

      __SCREAMING_SNAKE_CASE  :Tuple													=    api_doc[model_idx]["sections"]

      __SCREAMING_SNAKE_CASE  :Dict													=    [(idx, section) for idx, section in enumerate(a_				) if "sections" in section]
      __SCREAMING_SNAKE_CASE  :List[str]													=    False
      for idx, modality_doc in modalities_docs:
            __SCREAMING_SNAKE_CASE  :List[Any]													=    modality_doc["sections"]
            __SCREAMING_SNAKE_CASE  :Optional[int]													=    clean_model_doc_toc(a_				)

            if old_modality_doc != new_modality_doc:
                  __SCREAMING_SNAKE_CASE  :int													=    True
                  if overwrite:
                        __SCREAMING_SNAKE_CASE  :List[Any]													=    new_modality_doc

      if diff:
            if overwrite:
                  __SCREAMING_SNAKE_CASE  :List[str]													=    model_doc
                  __SCREAMING_SNAKE_CASE  :Union[str, Any]													=    api_doc
                  with open(a_					,  '''w'''					,  encoding='''utf-8'''				) as f:
                        f.write(yaml.dump(a_					,  allow_unicode=a_				)				)
            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__":
  lowerCamelCase_        =						argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  lowerCamelCase_        =						parser.parse_args()

  check_model_doc(args.fix_and_overwrite)



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCAmelCase   : List[Any]             =				{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Any             =				["""AlbertTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				["""AlbertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : List[str]             =				[
					    """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """AlbertForMaskedLM""",
					    """AlbertForMultipleChoice""",
					    """AlbertForPreTraining""",
					    """AlbertForQuestionAnswering""",
					    """AlbertForSequenceClassification""",
					    """AlbertForTokenClassification""",
					    """AlbertModel""",
					    """AlbertPreTrainedModel""",
					    """load_tf_weights_in_albert""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Tuple             =				[
					    """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFAlbertForMaskedLM""",
					    """TFAlbertForMultipleChoice""",
					    """TFAlbertForPreTraining""",
					    """TFAlbertForQuestionAnswering""",
					    """TFAlbertForSequenceClassification""",
					    """TFAlbertForTokenClassification""",
					    """TFAlbertMainLayer""",
					    """TFAlbertModel""",
					    """TFAlbertPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[int]             =				[
					    """FlaxAlbertForMaskedLM""",
					    """FlaxAlbertForMultipleChoice""",
					    """FlaxAlbertForPreTraining""",
					    """FlaxAlbertForQuestionAnswering""",
					    """FlaxAlbertForSequenceClassification""",
					    """FlaxAlbertForTokenClassification""",
					    """FlaxAlbertModel""",
					    """FlaxAlbertPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert import AlbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_albert_fast import AlbertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_albert import (
										    ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AlbertForMaskedLM,
										    AlbertForMultipleChoice,
										    AlbertForPreTraining,
										    AlbertForQuestionAnswering,
										    AlbertForSequenceClassification,
										    AlbertForTokenClassification,
										    AlbertModel,
										    AlbertPreTrainedModel,
										    load_tf_weights_in_albert,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_albert import (
										    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAlbertForMaskedLM,
										    TFAlbertForMultipleChoice,
										    TFAlbertForPreTraining,
										    TFAlbertForQuestionAnswering,
										    TFAlbertForSequenceClassification,
										    TFAlbertForTokenClassification,
										    TFAlbertMainLayer,
										    TFAlbertModel,
										    TFAlbertPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_albert import (
										    FlaxAlbertForMaskedLM,
										    FlaxAlbertForMultipleChoice,
										    FlaxAlbertForPreTraining,
										    FlaxAlbertForQuestionAnswering,
										    FlaxAlbertForSequenceClassification,
										    FlaxAlbertForTokenClassification,
										    FlaxAlbertModel,
										    FlaxAlbertPreTrainedModel,
										)
else:
					import sys

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


