

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


_UpperCAmelCase		:   Optional[int]										=       logging.get_logger(__name__)

_UpperCAmelCase		:   Optional[int]										=       {
    """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  lowercase				(       lowercase_	,  lowercase_			):
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''dinat'''

				__SCREAMING_SNAKE_CASE					: List[str]       			=			{
				    '''num_attention_heads''': '''num_heads''',
				    '''num_hidden_layers''': '''num_layers''',
				}




				def __init__(					self      ,   snake_case=4      ,   snake_case=3      ,   snake_case=64      ,   snake_case=[3, 4, 6, 5]      ,   snake_case=[2, 4, 8, 16]      ,   snake_case=7      ,   snake_case=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]      ,   snake_case=3.0      ,   snake_case=True      ,   snake_case=0.0      ,   snake_case=0.0      ,   snake_case=0.1      ,   snake_case="gelu"      ,   snake_case=0.02      ,   snake_case=1e-5      ,   snake_case=0.0      ,   snake_case=None      ,   snake_case=None      ,   **snake_case      ,   ):
											super().__init__(**snake_case       )

											snake_case_							=    patch_size
											snake_case_							=    num_channels
											snake_case_							=    embed_dim
											snake_case_							=    depths
											snake_case_							=    len(snake_case       )
											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(snake_case       ) - 1)       )
											snake_case_							=    layer_scale_init_value
											snake_case_							=    ['stem'] + [F'''stage{idx}''' for idx in range(1      ,   len(snake_case       ) + 1       )]
											snake_case_				,    snake_case_							=    get_aligned_output_features_output_indices(
											    out_features=snake_case      ,   out_indices=snake_case      ,   stage_names=self.stage_names       )







from __future__ import annotations

import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    np.shape(UpperCamelCase__      )
							if rows != columns:
														snake_case_							=    (
														    '\'table\' has to be of square shaped array but got a '
														    F'''{rows}x{columns} array:\n{table}'''
														)
														raise ValueError(UpperCamelCase__      )

							snake_case_							=    np.zeros((rows, columns)      )
							snake_case_							=    np.zeros((rows, columns)      )
							for i in range(UpperCamelCase__      ):
														for j in range(UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					if upper[j][j] == 0:
																												raise ArithmeticError('No LU decomposition exists'      )
																					snake_case_							=    (table[i][j] - total) / upper[j][j]
														snake_case_							=    1
														for j in range(UpperCamelCase__   ,							UpperCamelCase__      ):
																					snake_case_							=    sum(lower[i][k] * upper[k][j] for k in range(UpperCamelCase__      )      )
																					snake_case_							=    table[i][j] - total
							return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()




