





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)

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

class       lowerCAmelCase_						(						UpperCAmelCase__  ,UpperCAmelCase__						):
 __lowerCamelCase  :							Optional[int]				     =    "dinat"

 __lowerCamelCase  :							Tuple				     =    {
     "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=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]	,   _lowerCAmelCase=3.0	,   _lowerCAmelCase=True	,   _lowerCAmelCase=0.0	,   _lowerCAmelCase=0.0	,   _lowerCAmelCase=0.1	,   _lowerCAmelCase="gelu"	,   _lowerCAmelCase=0.02	,   _lowerCAmelCase=1E-5	,   _lowerCAmelCase=0.0	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   **_lowerCAmelCase	,   )			->							Any:
     super().__init__(**_a						)

     _lowerCAmelCase			=					patch_size
     _lowerCAmelCase			=					num_channels
     _lowerCAmelCase			=					embed_dim
     _lowerCAmelCase			=					depths
     _lowerCAmelCase			=					len(_a						)
     _lowerCAmelCase			=					num_heads
     _lowerCAmelCase			=					kernel_size
     _lowerCAmelCase			=					dilations
     _lowerCAmelCase			=					mlp_ratio
     _lowerCAmelCase			=					qkv_bias
     _lowerCAmelCase			=					hidden_dropout_prob
     _lowerCAmelCase			=					attention_probs_dropout_prob
     _lowerCAmelCase			=					drop_path_rate
     _lowerCAmelCase			=					hidden_act
     _lowerCAmelCase			=					layer_norm_eps
     _lowerCAmelCase			=					initializer_range
     # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _lowerCAmelCase			=					int(embed_dim * 2 ** (len(_a						) - 1)						)
     _lowerCAmelCase			=					layer_scale_init_value
     _lowerCAmelCase			=					["stem"] + [f'''stage{idx}''' for idx in range(1	,   len(_a						) + 1						)]
     _lowerCAmelCase	,	_lowerCAmelCase			=					get_aligned_output_features_output_indices(
         out_features=_a	,   out_indices=_a	,   stage_names=self.stage_names						)








"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



