



"""simple docstring"""






import argparse
from collections import defaultdict

import yaml


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






def        snake_case_  (      A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase						:				List[Any]    			= defaultdict(A_						)
       _lowerCamelCase						:				List[Any]    			= []
       _lowerCamelCase						:				Tuple    			= []
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}						)
              else:
                     new_doc_list.append(A_						)

       _lowerCamelCase						:				Optional[Any]    			= new_doc_list
       _lowerCamelCase						:				Tuple    			= [key for key, value in counts.items() if value > 1]

       _lowerCamelCase						:				List[str]    			= []
       for duplicate_key in duplicates:
              _lowerCamelCase						:				Optional[int]    			= list({doc['''title'''] for doc in doc_list 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 doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]						)
       _lowerCamelCase						:				Optional[int]    			= sorted(A_,				key=lambda A_						: s["title"].lower()						)

       # "overview" gets special treatment and is always first
       if len(A_						) > 1:
              raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''						)

       overview_doc.extend(A_						)

       # Sort
       return overview_doc






def        snake_case_  (      A_					:		str=False						):


       '''simple docstring'''





       with open(A_,				encoding='''utf-8'''						) as f:
              _lowerCamelCase						:				Union[str, Any]    			= yaml.safe_load(f.read()						)

       # Get to the API doc
       _lowerCamelCase						:				Union[str, Any]    			= 0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCamelCase						:				List[Any]    			= content[api_idx]['''sections''']

       # Then to the model doc
       _lowerCamelCase						:				str    			= 0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       _lowerCamelCase						:				List[str]    			= api_doc[scheduler_idx]['''sections''']
       _lowerCamelCase						:				Dict    			= clean_doc_toc(A_						)

       _lowerCamelCase						:				Tuple    			= False
       if new_scheduler_doc != scheduler_doc:
              _lowerCamelCase						:				List[str]    			= True
              if overwrite:
                     _lowerCamelCase						:				str    			= new_scheduler_doc

       if diff:
              if overwrite:
                     _lowerCamelCase						:				List[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.'''						)






def        snake_case_  (      A_					:		Any=False						):


       '''simple docstring'''





       with open(A_,				encoding='''utf-8'''						) as f:
              _lowerCamelCase						:				Dict    			= yaml.safe_load(f.read()						)

       # Get to the API doc
       _lowerCamelCase						:				Optional[int]    			= 0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       _lowerCamelCase						:				Union[str, Any]    			= content[api_idx]['''sections''']

       # Then to the model doc
       _lowerCamelCase						:				Union[str, Any]    			= 0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       _lowerCamelCase						:				str    			= False
       _lowerCamelCase						:				Tuple    			= api_doc[pipeline_idx]['''sections''']
       _lowerCamelCase						:				Any    			= []

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     _lowerCamelCase						:				List[str]    			= pipeline_doc['''section''']
                     _lowerCamelCase						:				Optional[Any]    			= clean_doc_toc(A_						)
                     if overwrite:
                            _lowerCamelCase						:				List[str]    			= new_sub_pipeline_doc
              new_pipeline_docs.append(A_						)

       # sort overall pipeline doc
       _lowerCamelCase						:				Any    			= clean_doc_toc(A_						)

       if new_pipeline_docs != pipeline_docs:
              _lowerCamelCase						:				Dict    			= True
              if overwrite:
                     _lowerCamelCase						:				Optional[int]    			= new_pipeline_docs

       if diff:
              if overwrite:
                     _lowerCamelCase						:				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_scheduler_doc(args.fix_and_overwrite)
   check_pipeline_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       :     Any   =     {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Dict   =     ["""ElectraTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Tuple   =     [
        """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ElectraForCausalLM""",
        """ElectraForMaskedLM""",
        """ElectraForMultipleChoice""",
        """ElectraForPreTraining""",
        """ElectraForQuestionAnswering""",
        """ElectraForSequenceClassification""",
        """ElectraForTokenClassification""",
        """ElectraModel""",
        """ElectraPreTrainedModel""",
        """load_tf_weights_in_electra""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     [
        """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFElectraForMaskedLM""",
        """TFElectraForMultipleChoice""",
        """TFElectraForPreTraining""",
        """TFElectraForQuestionAnswering""",
        """TFElectraForSequenceClassification""",
        """TFElectraForTokenClassification""",
        """TFElectraModel""",
        """TFElectraPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[str]   =     [
        """FlaxElectraForCausalLM""",
        """FlaxElectraForMaskedLM""",
        """FlaxElectraForMultipleChoice""",
        """FlaxElectraForPreTraining""",
        """FlaxElectraForQuestionAnswering""",
        """FlaxElectraForSequenceClassification""",
        """FlaxElectraForTokenClassification""",
        """FlaxElectraModel""",
        """FlaxElectraPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

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