





"""simple docstring"""



import argparse
from collections import defaultdict

import yaml


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







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Optional[int]:



    '''simple docstring'''







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

    __A    :						Any						 =     []
    for duplicate_key in duplicates:
        __A    :						Dict						 =     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 		__lowercase     (   snake_case_  :  Optional[Any]=False						)   ->str:



    '''simple docstring'''







    with open(snake_case_				,encoding='''utf-8'''						) as f:
        __A    :						int						 =     yaml.safe_load(f.read()						)

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

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

    __A    :						Optional[int]						 =     api_doc[model_idx]['''sections''']

    __A    :						int						 =     [(idx, section) for idx, section in enumerate(snake_case_						) if '''sections''' in section]
    __A    :						List[Any]						 =     False
    for idx, modality_doc in modalities_docs:
        __A    :						str						 =     modality_doc['''sections''']
        __A    :						str						 =     clean_model_doc_toc(snake_case_						)

        if old_modality_doc != new_modality_doc:
            __A    :						Dict						 =     True
            if overwrite:
                __A    :						Optional[int]						 =     new_modality_doc

    if diff:
        if overwrite:
            __A    :						List[Any]						 =     model_doc
            __A    :						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__":
       a_ =			argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       a_ =			parser.parse_args()

       check_model_doc(args.fix_and_overwrite)











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_ =			logging.get_logger(__name__)

a_ =			{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """nat"""

 _lowerCamelCase        =     {
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(    self					,						__lowerCamelCase=4					,						__lowerCamelCase=3					,						__lowerCamelCase=64					,						__lowerCamelCase=[3, 4, 6, 5]					,						__lowerCamelCase=[2, 4, 8, 16]					,						__lowerCamelCase=7					,						__lowerCamelCase=3.0					,						__lowerCamelCase=True					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.0					,						__lowerCamelCase=0.1					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-5					,						__lowerCamelCase=0.0					,						__lowerCamelCase=None					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     patch_size
     __A    :						Optional[Any]						 =     num_channels
     __A    :						Tuple						 =     embed_dim
     __A    :						Dict						 =     depths
     __A    :						str						 =     len(__lowerCamelCase  )
     __A    :						Optional[Any]						 =     num_heads
     __A    :						str						 =     kernel_size
     __A    :						Any						 =     mlp_ratio
     __A    :						Optional[int]						 =     qkv_bias
     __A    :						str						 =     hidden_dropout_prob
     __A    :						Any						 =     attention_probs_dropout_prob
     __A    :						int						 =     drop_path_rate
     __A    :						int						 =     hidden_act
     __A    :						Any						 =     layer_norm_eps
     __A    :						Tuple						 =     initializer_range
     # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     __A    :						int						 =     int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A    :						Union[str, Any]						 =     layer_scale_init_value
     __A    :						List[str]						 =     ['''stem'''] + [F"""stage{idx}""" for idx in range(1					,						len(__lowerCamelCase  ) + 1  )]
     __A ,       __A    :						Any						 =     get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,						out_indices=__lowerCamelCase					,						stage_names=self.stage_names  )




