



import argparse
from collections import defaultdict

import yaml


__lowerCamelCase         =			'''docs/source/en/_toctree.yml'''







def    _snake_case (       __snake_case    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[Any]				      =		defaultdict(__snake_case    )
      for doc in model_doc:
            counts[doc["local"]] += 1
      UpperCAmelCase_  :	str				      =		[key for key, value in counts.items() if value > 1]

      UpperCAmelCase_  :	List[Any]				      =		[]
      for duplicate_key in duplicates:
            UpperCAmelCase_  :	str				      =		list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}    )
            if len(__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(__snake_case					,  key=lambda __snake_case    : s["title"].lower()    )







def    _snake_case (       __snake_case=False    )     ->     Union[str, Any]:






      '''simple docstring'''







      with open(__snake_case					,  encoding="utf-8"    ) as f:
            UpperCAmelCase_  :	List[Any]				      =		yaml.safe_load(f.read()    )

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

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

      UpperCAmelCase_  :	Dict				      =		api_doc[model_idx]["sections"]

      UpperCAmelCase_  :	Any				      =		[(idx, section) for idx, section in enumerate(__snake_case    ) if "sections" in section]
      UpperCAmelCase_  :	Any				      =		False
      for idx, modality_doc in modalities_docs:
            UpperCAmelCase_  :	Dict				      =		modality_doc["sections"]
            UpperCAmelCase_  :	Tuple				      =		clean_model_doc_toc(__snake_case    )

            if old_modality_doc != new_modality_doc:
                  UpperCAmelCase_  :	Optional[int]				      =		True
                  if overwrite:
                        UpperCAmelCase_  :	Any				      =		new_modality_doc

      if diff:
            if overwrite:
                  UpperCAmelCase_  :	Dict				      =		model_doc
                  UpperCAmelCase_  :	Optional[int]				      =		api_doc
                  with open(__snake_case					,  "w"					,  encoding="utf-8"    ) as f:
                        f.write(yaml.dump(__snake_case					,  allow_unicode=__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__":
   __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)





from typing import TYPE_CHECKING

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


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

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