





'''simple docstring'''




from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 					SCREAMING_SNAKE_CASE       (yaml.SafeLoader				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			[self.constructed_objects[key_node] for key_node, _ in node.value]
          __A		:					Dict						=			[tuple(_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else key for key in keys]
          __A		:					str						=			Counter(_UpperCAmelCase)
          __A		:					Optional[int]						=			[key for key in counter if counter[key] > 1]
          if duplicate_keys:
               raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}')


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[int]						=			super().construct_mapping(_UpperCAmelCase		,      deep=_UpperCAmelCase)
          self._check_no_duplicates_on_constructed_node(_UpperCAmelCase)
          return mapping






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple[Optional[str], str]:
     __A		:					Union[str, Any]						=			list(readme_content.splitlines()     )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          __A		:					Optional[int]						=			full_content[1:].index('---'     ) + 1
          __A		:					int						=			'\n'.join(full_content[1:sep_idx]     )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

     return None, "\n".join(__snake_case     )


class 					SCREAMING_SNAKE_CASE       (a__				):
     # class attributes
     lowerCAmelCase						     =			{'''train_eval_index'''}  # train-eval-index in the YAML metadata


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls		,      _UpperCAmelCase):

          '''simple docstring'''






          with open(_UpperCAmelCase		,      encoding='utf-8') as readme_file:
               __A					,__A		:					int						=			_split_yaml_from_readme(readme_file.read())
          if yaml_string is not None:
               return cls.from_yaml_string(_UpperCAmelCase)
          else:
               return cls()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if path.exists():
               with open(_UpperCAmelCase		,      encoding='utf-8') as readme_file:
                    __A		:					Dict						=			readme_file.read()
          else:
               __A		:					Optional[Any]						=			None
          __A		:					str						=			self._to_readme(_UpperCAmelCase)
          with open(_UpperCAmelCase		,      'w'		,      encoding='utf-8') as readme_file:
               readme_file.write(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if readme_content is not None:
               __A					,__A		:					Any						=			_split_yaml_from_readme(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			'---\n' + self.to_yaml_string() + '---\n' + content
          else:
               __A		:					Optional[int]						=			'---\n' + self.to_yaml_string() + '---\n'
          return full_content


     @classmethod
     def 				SCREAMING_SNAKE_CASE    (		cls		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			yaml.load(_UpperCAmelCase		,      Loader=_NoDuplicateSafeLoader) or {}

          # Convert the YAML keys to DatasetMetadata fields
          __A		:					Tuple						=			{
              (key.replace('-'		,      '_') if key.replace('-'		,      '_') in cls._FIELDS_WITH_DASHES else key): value
              for key, value in metadata_dict.items()
          }
          return cls(**_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return yaml.safe_dump(
              {
                  (key.replace('_'		,      '-') if key in self._FIELDS_WITH_DASHES else key): value
                  for key, value in self.items()
              }		,      sort_keys=_UpperCAmelCase		,      allow_unicode=_UpperCAmelCase		,      encoding='utf-8'		,      ).decode('utf-8')




lowercase__       : List[Any]         =       {
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
  from argparse import ArgumentParser

  lowercase__       : int         =       ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
  ap.add_argument('''readme_filepath''')
  lowercase__       : Tuple         =       ap.parse_args()

  lowercase__       : int         =       Path(args.readme_filepath)
  lowercase__       : Any         =       DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """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  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

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

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	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(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


