

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      {
    '''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       snake_case_  (						__A      ,			__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							"dinat"

			SCREAMING_SNAKE_CASE	:  Dict		     =							{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}







			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Any=4				,  _UpperCamelCase					:							Tuple=3				,  _UpperCamelCase					:							Optional[int]=6_4				,  _UpperCamelCase					:							int=[3, 4, 6, 5]				,  _UpperCamelCase					:							str=[2, 4, 8, 1_6]				,  _UpperCamelCase					:							List[str]=7				,  _UpperCamelCase					:							Optional[int]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]				,  _UpperCamelCase					:							List[str]=3.0				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							int=0.0				,  _UpperCamelCase					:							List[str]=0.0				,  _UpperCamelCase					:							List[str]=0.1				,  _UpperCamelCase					:							List[str]="gelu"				,  _UpperCamelCase					:							Tuple=0.02				,  _UpperCamelCase					:							List[Any]=1e-5				,  _UpperCamelCase					:							Union[str, Any]=0.0				,  _UpperCamelCase					:							Dict=None				,  _UpperCamelCase					:							Optional[Any]=None				,  **_UpperCamelCase					:							Any				,  ) ->List[str]:
									super().__init__(**_UpperCamelCase				)

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

"""simple docstring"""




import doctest
from collections import deque

import numpy as np





class 			snake_case  :

							def __init__(self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_    			=			[1, 2, 3, 4]







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			len(self.first_signal     )
									SCREAMING_SNAKE_CASE_    			=			len(self.second_signal     )

									SCREAMING_SNAKE_CASE_    			=			max(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_    			=			[[0] * max_length for i in range(SCREAMING_SNAKE_CASE_     )]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
											self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
											self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(SCREAMING_SNAKE_CASE_     ):
											SCREAMING_SNAKE_CASE_    			=			deque(self.second_signal     )
											rotated_signal.rotate(SCREAMING_SNAKE_CASE_     )
											for j, item in enumerate(SCREAMING_SNAKE_CASE_     ):
													matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_    			=			np.matmul(np.transpose(SCREAMING_SNAKE_CASE_     )							,							np.transpose(self.first_signal     )     )

									# rounding-off to two decimal places
									return [round(SCREAMING_SNAKE_CASE_							,							2     ) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()