
'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__lowercase							:   Tuple					=     logging.get_logger(__name__)

__lowercase							:   List[Any]					=     {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}

class 	__lowercase				(  _lowercase      ,     _lowercase    ):
   lowerCamelCase			:		Optional[Any]	      =			"swin"

   lowerCamelCase			:		Any	      =			{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }

   def __init__(self			,   A=2_2_4			,   A=4			,   A=3			,   A=9_6			,   A=[2, 2, 6, 2]			,   A=[3, 6, 1_2, 2_4]			,   A=7			,   A=4.0			,   A=True			,   A=0.0			,   A=0.0			,   A=0.1			,   A="gelu"			,   A=False			,   A=0.02			,   A=1E-5			,   A=3_2			,   A=None			,   A=None			,   **A			,   ):
        super().__init__(**A      )

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

class 	__lowercase				(  _lowercase    ):
   lowerCamelCase			:		List[str]	      =			version.parse("1.11"    )

   @property
   def        UpperCAmelCase__ (self      ):
        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
            ]      )






   @property
   def        UpperCAmelCase__ (self      ):
        return 1E-4




'''simple docstring'''


from __future__ import annotations

from decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		float(
										    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creates a copy of the matrix with swapped positions of the elements
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 3
					    and len(matrix[0]				) == 3
					    and len(matrix[1]				) == 3
					    and len(matrix[2]				) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowerCamelCase_					:      Tuple     =		float(
										    (
										        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
										        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
										        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
										    )
										    - (
										        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
										        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
										        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
										    )				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creating cofactor matrix
										lowerCamelCase_					:      Any     =		[
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      int     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				inverse_matrix[i][j] /= d(_lowercase				)

        # Calculate the inverse of the matrix
										return [[float(d(_lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''				)


