



from __future__ import annotations
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       Dict			,     lowercase	:       list[list[int]]   )						->					Any:
									"""simple docstring"""







									__lowercase					=				TypeError(
									    """Matrices must be formed from a list of zero or more lists containing at """
									    """least one and the same number of values, each of which must be of type """
									    """int or float."""   )
									if len(UpperCAmelCase__   ) != 0:
															__lowercase					=				len(rows[0]   )
															if cols == 0:
																					raise error
															for row in rows:
																					if len(UpperCAmelCase__   ) != cols:
																											raise error
																					for value in row:
																											if not isinstance(UpperCAmelCase__			,     (int, float)   ):
																																	raise error
															__lowercase					=				rows
									else:
															__lowercase					=				[]





			def 		snake_case__			(				self	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







									return [[row[i] for row in self.rows] for i in range(len(self.rows[0]   )   )]





			@property
			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									return len(self.rows   )





			@property
			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[int]:
									"""simple docstring"""







									return len(self.rows[0]   )





			@property
			def 		snake_case__			(				self	:       List[str]   )						->					List[Any]:
									"""simple docstring"""







									return (self.num_rows, self.num_columns)





			@property
			def 		snake_case__			(				self	:       Dict   )						->					List[str]:
									"""simple docstring"""







									return self.order[0] == self.order[1]





			def 		snake_case__			(				self	:       List[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				[
									    [0 if column_num != row_num else 1 for column_num in range(self.num_rows   )]
									    for row_num in range(self.num_rows   )
									]
									return Matrix(UpperCAmelCase__   )





			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									if not self.is_square:
															return 0
									if self.order == (0, 0):
															return 1
									if self.order == (1, 1):
															return int(self.rows[0][0]   )
									if self.order == (2, 2):
															return int(
															    (self.rows[0][0] * self.rows[1][1])
															    - (self.rows[0][1] * self.rows[1][0])   )
									else:
															return sum(
															    self.rows[0][column] * self.cofactors().rows[0][column]
															    for column in range(self.num_columns   )   )





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[int]:
									"""simple docstring"""







									return bool(self.determinant()   )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       int			,     lowercase	:       int   )						->					str:
									"""simple docstring"""







									__lowercase					=				[
									    [
									        self.rows[other_row][other_column]
									        for other_column in range(self.num_columns   )
									        if other_column != column
									    ]
									    for other_row in range(self.num_rows   )
									    if other_row != row
									]
									return Matrix(UpperCAmelCase__   ).determinant()





			def 		snake_case__			(				self	:       int			,     lowercase	:       int			,     lowercase	:       int   )						->					List[Any]:
									"""simple docstring"""







									if (row + column) % 2 == 0:
															return self.get_minor(UpperCAmelCase__			,     UpperCAmelCase__   )
									return -1 * self.get_minor(UpperCAmelCase__			,     UpperCAmelCase__   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									return Matrix(
									    [
									        [self.get_minor(UpperCAmelCase__			,     UpperCAmelCase__   ) for column in range(self.num_columns   )]
									        for row in range(self.num_rows   )
									    ]   )





			def 		snake_case__			(				self	:       List[Any]   )						->					str:
									"""simple docstring"""







									return Matrix(
									    [
									        [
									            self.minors().rows[row][column]
									            if (row + column) % 2 == 0
									            else self.minors().rows[row][column] * -1
									            for column in range(self.minors().num_columns   )
									        ]
									        for row in range(self.minors().num_rows   )
									    ]   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				[
									    [self.cofactors().rows[column][row] for column in range(self.num_columns   )]
									    for row in range(self.num_rows   )
									]
									return Matrix(UpperCAmelCase__   )





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.determinant()
									if not determinant:
															raise TypeError("""Only matrices with a non-zero determinant have an inverse"""   )
									return self.adjugate() * (1 / determinant)





			def __repr__(				self	:       Dict   )						->					Tuple:
									"""simple docstring"""







									return str(self.rows   )





			def __str__(				self	:       List[Any]   )						->					List[Any]:
									"""simple docstring"""







									if self.num_rows == 0:
															return "[]"
									if self.num_rows == 1:
															return "[[" + ". ".join(str(self.rows[0]   )   ) + "]]"
									return (
									    "["
									    + "\n ".join(
									        [
									            """[""" + """. """.join([str(UpperCAmelCase__   ) for value in row]   ) + """.]"""
									            for row in self.rows
									        ]   )
									    + "]"
									)





			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       list[int]			,     lowercase	:       int | None = None   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				TypeError("""Row must be a list containing all ints and/or floats"""   )
									if not isinstance(UpperCAmelCase__			,     UpperCAmelCase__   ):
															raise type_error
									for value in row:
															if not isinstance(UpperCAmelCase__			,     (int, float)   ):
																					raise type_error
									if len(UpperCAmelCase__   ) != self.num_columns:
															raise ValueError(
															    """Row must be equal in length to the other rows in the matrix"""   )
									if position is None:
															self.rows.append(UpperCAmelCase__   )
									else:
															__lowercase					=				self.rows[0:position] + [row] + self.rows[position:]





			def 		snake_case__			(				self	:       List[Any]			,     lowercase	:       list[int]			,     lowercase	:       int | None = None   )						->					Any:
									"""simple docstring"""







									__lowercase					=				TypeError(
									    """Column must be a list containing all ints and/or floats"""   )
									if not isinstance(UpperCAmelCase__			,     UpperCAmelCase__   ):
															raise type_error
									for value in column:
															if not isinstance(UpperCAmelCase__			,     (int, float)   ):
																					raise type_error
									if len(UpperCAmelCase__   ) != self.num_rows:
															raise ValueError(
															    """Column must be equal in length to the other columns in the matrix"""   )
									if position is None:
															__lowercase					=				[self.rows[i] + [column[i]] for i in range(self.num_rows   )]
									else:
															__lowercase					=				[
															    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
															    for i in range(self.num_rows   )
															]





			def __eq__(				self	:       Optional[int]			,     lowercase	:       object   )						->					str:
									"""simple docstring"""







									if not isinstance(UpperCAmelCase__			,     UpperCAmelCase__   ):
															return NotImplemented
									return self.rows == other.rows





			def __ne__(				self	:       Tuple			,     lowercase	:       object   )						->					str:
									"""simple docstring"""







									return not self == other





			def __neg__(				self	:       Dict   )						->					Union[str, Any]:
									"""simple docstring"""







									return self * -1





			def __add__(				self	:       Tuple			,     lowercase	:       Matrix   )						->					Tuple:
									"""simple docstring"""







									if self.order != other.order:
															raise ValueError("""Addition requires matrices of the same order"""   )
									return Matrix(
									    [
									        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns   )]
									        for i in range(self.num_rows   )
									    ]   )





			def __sub__(				self	:       Optional[Any]			,     lowercase	:       Matrix   )						->					Optional[int]:
									"""simple docstring"""







									if self.order != other.order:
															raise ValueError("""Subtraction requires matrices of the same order"""   )
									return Matrix(
									    [
									        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns   )]
									        for i in range(self.num_rows   )
									    ]   )





			def __mul__(				self	:       Dict			,     lowercase	:       Matrix | int | float   )						->					Union[str, Any]:
									"""simple docstring"""







									if isinstance(UpperCAmelCase__			,     (int, float)   ):
															return Matrix(
															    [[int(element * other   ) for element in row] for row in self.rows]   )
									elif isinstance(UpperCAmelCase__			,     UpperCAmelCase__   ):
															if self.num_columns != other.num_rows:
																					raise ValueError(
																					    """The number of columns in the first matrix must """
																					    """be equal to the number of rows in the second"""   )
															return Matrix(
															    [
															        [Matrix.dot_product(UpperCAmelCase__			,     UpperCAmelCase__   ) for column in other.columns()]
															        for row in self.rows
															    ]   )
									else:
															raise TypeError(
															    """A Matrix can only be multiplied by an int, float, or another matrix"""   )





			def __pow__(				self	:       List[Any]			,     lowercase	:       int   )						->					Union[str, Any]:
									"""simple docstring"""







									if not isinstance(UpperCAmelCase__			,     UpperCAmelCase__   ):
															raise TypeError("""A Matrix can only be raised to the power of an int"""   )
									if not self.is_square:
															raise ValueError("""Only square matrices can be raised to a power"""   )
									if other == 0:
															return self.identity()
									if other < 0:
															if self.is_invertable():
																					return self.inverse() ** (-other)
															raise ValueError(
															    """Only invertable matrices can be raised to a negative power"""   )
									__lowercase					=				self
									for _ in range(other - 1   ):
															result *= self
									return result







			@classmethod
			def 		snake_case__			(				cls	:       Dict			,     lowercase	:       list[int]			,     lowercase	:       list[int]   )						->					int:
									"""simple docstring"""







									return sum(row[i] * column[i] for i in range(len(UpperCAmelCase__   )   )   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()










import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import MaskFormerSwinBackbone
			from transformers.models.maskformer import MaskFormerSwinModel
class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       List[Any]			,     lowercase	:       str			,     lowercase	:       Union[str, Any]=13			,     lowercase	:       Tuple=32			,     lowercase	:       Optional[Any]=2			,     lowercase	:       Tuple=3			,     lowercase	:       Tuple=16			,     lowercase	:       Tuple=[1, 2, 1]			,     lowercase	:       Optional[Any]=[2, 2, 4]			,     lowercase	:       Dict=2			,     lowercase	:       Optional[int]=2.0			,     lowercase	:       List[Any]=True			,     lowercase	:       str=0.0			,     lowercase	:       Any=0.0			,     lowercase	:       Optional[int]=0.1			,     lowercase	:       int="gelu"			,     lowercase	:       Tuple=False			,     lowercase	:       Optional[Any]=True			,     lowercase	:       int=0.02			,     lowercase	:       Union[str, Any]=1E-5			,     lowercase	:       Dict=True			,     lowercase	:       Any=None			,     lowercase	:       str=True			,     lowercase	:       str=10			,     lowercase	:       Dict=8			,     lowercase	:       int=["stage1", "stage2", "stage3"]			,     lowercase	:       Optional[int]=[1, 2, 3]			,     )						->					Any:
									"""simple docstring"""







									__lowercase					=				parent
									__lowercase					=				batch_size
									__lowercase					=				image_size
									__lowercase					=				patch_size
									__lowercase					=				num_channels
									__lowercase					=				embed_dim
									__lowercase					=				depths
									__lowercase					=				num_heads
									__lowercase					=				window_size
									__lowercase					=				mlp_ratio
									__lowercase					=				qkv_bias
									__lowercase					=				hidden_dropout_prob
									__lowercase					=				attention_probs_dropout_prob
									__lowercase					=				drop_path_rate
									__lowercase					=				hidden_act
									__lowercase					=				use_absolute_embeddings
									__lowercase					=				patch_norm
									__lowercase					=				layer_norm_eps
									__lowercase					=				initializer_range
									__lowercase					=				is_training
									__lowercase					=				scope
									__lowercase					=				use_labels
									__lowercase					=				type_sequence_label_size
									__lowercase					=				encoder_stride
									__lowercase					=				out_features
									__lowercase					=				out_indices





			def 		snake_case__			(				self	:       str   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

									__lowercase					=				None
									if self.use_labels:
															__lowercase					=				ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )

									__lowercase					=				self.get_config()

									return config, pixel_values, labels





			def 		snake_case__			(				self	:       List[str]   )						->					int:
									"""simple docstring"""







									return MaskFormerSwinConfig(
									    image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     embed_dim=self.embed_dim			,     depths=self.depths			,     num_heads=self.num_heads			,     window_size=self.window_size			,     mlp_ratio=self.mlp_ratio			,     qkv_bias=self.qkv_bias			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     drop_path_rate=self.drop_path_rate			,     hidden_act=self.hidden_act			,     use_absolute_embeddings=self.use_absolute_embeddings			,     path_norm=self.patch_norm			,     layer_norm_eps=self.layer_norm_eps			,     initializer_range=self.initializer_range			,     encoder_stride=self.encoder_stride			,     out_features=self.out_features			,     out_indices=self.out_indices			,     )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       List[Any]			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				MaskFormerSwinModel(config=lowercase   )
									model.to(lowercase   )
									model.eval()
									__lowercase					=				model(lowercase   )

									__lowercase					=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
									__lowercase					=				int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, expected_seq_len, expected_dim)   )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Tuple			,     lowercase	:       Any			,     lowercase	:       Optional[int]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				MaskFormerSwinBackbone(config=lowercase   )
									model.to(lowercase   )
									model.eval()
									__lowercase					=				model(lowercase   )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps   )			,     len(config.out_features   )   )
									self.parent.assertListEqual(list(result.feature_maps[0].shape   )			,     [13, 16, 16, 16]   )

									# verify channels
									self.parent.assertEqual(len(model.channels   )			,     len(config.out_features   )   )
									self.parent.assertListEqual(model.channels			,     [16, 32, 64]   )

									# verify ValueError
									with self.parent.assertRaises(lowercase   ):
															__lowercase					=				["""stem"""]
															__lowercase					=				MaskFormerSwinBackbone(config=lowercase   )







			def 		snake_case__			(				self	:       int   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.prepare_config_and_inputs()
									__lowercase	,			__lowercase	,			__lowercase					=				config_and_inputs
									__lowercase					=				{"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Optional[int]      =			(
			    (
			        MaskFormerSwinModel,
			        MaskFormerSwinBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			lowercase__      :  List[str]      =			{"""feature-extraction""": MaskFormerSwinModel} if is_torch_available() else {}
			lowercase__      :  List[str]      =			False
			lowercase__      :  int      =			False
			lowercase__      :  int      =			False
			lowercase__      :  Tuple      =			False
			lowercase__      :  Optional[Any]      =			False





			def 		snake_case__			(				self	:       Dict   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				MaskFormerSwinModelTester(self   )
									__lowercase					=				ConfigTester(self			,     config_class=lowercase			,     embed_dim=37   )





			@require_torch_multi_gpu
			@unittest.skip(
			    reason=(
			        """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
			        """ `nn.DataParallel`"""
			    )   )
			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       Tuple   )						->					Optional[int]:
									"""simple docstring"""







									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Dict:
									"""simple docstring"""







									return





			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase   )





			def 		snake_case__			(				self	:       List[str]   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*lowercase   )





			@unittest.skip("""Swin does not use inputs_embeds"""   )
			def 		snake_case__			(				self	:       int   )						->					Any:
									"""simple docstring"""







									pass





			@unittest.skip("""Swin does not support feedforward chunking"""   )
			def 		snake_case__			(				self	:       Optional[int]   )						->					Union[str, Any]:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       Dict   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															__lowercase					=				model_class(lowercase   )
															self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)   )
															__lowercase					=				model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowercase			,     nn.Linear   )   )





			def 		snake_case__			(				self	:       Dict   )						->					Any:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															__lowercase					=				model_class(lowercase   )
															__lowercase					=				inspect.signature(model.forward   )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__lowercase					=				[*signature.parameters.keys()]

															__lowercase					=				["""pixel_values"""]
															self.assertListEqual(arg_names[:1]			,     lowercase   )





			@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""   )
			def 		snake_case__			(				self	:       Any   )						->					Union[str, Any]:
									"""simple docstring"""







									pass





			@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""   )
			def 		snake_case__			(				self	:       Tuple   )						->					Optional[Any]:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       Tuple			,     lowercase	:       Optional[int]			,     lowercase	:       Optional[int]			,     lowercase	:       Union[str, Any]   )						->					Dict:
									"""simple docstring"""







									__lowercase					=				model_class(lowercase   )
									model.to(lowercase   )
									model.eval()

									with torch.no_grad():
															__lowercase					=				model(**self._prepare_for_class(lowercase			,     lowercase   )   )

									__lowercase					=				outputs.hidden_states

									__lowercase					=				getattr(
									    self.model_tester			,     """expected_num_hidden_layers"""			,     len(self.model_tester.depths   ) + 1   )
									self.assertEqual(len(lowercase   )			,     lowercase   )

									# Swin has a different seq_length
									__lowercase					=				(
									    config.patch_size
									    if isinstance(config.patch_size			,     collections.abc.Iterable   )
									    else (config.patch_size, config.patch_size)
									)

									__lowercase					=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]   )			,     [num_patches, self.model_tester.embed_dim]			,     )





			def 		snake_case__			(				self	:       int   )						->					Dict:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()

									__lowercase					=				(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size			,     collections.abc.Iterable   )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)

									for model_class in self.all_model_classes:
															__lowercase					=				True
															self.check_hidden_states_output(lowercase			,     lowercase			,     lowercase			,     lowercase   )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__lowercase					=				True

															self.check_hidden_states_output(lowercase			,     lowercase			,     lowercase			,     lowercase   )





			def 		snake_case__			(				self	:       int   )						->					str:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()
									__lowercase					=				3

									__lowercase					=				(
									    self.model_tester.image_size
									    if isinstance(self.model_tester.image_size			,     collections.abc.Iterable   )
									    else (self.model_tester.image_size, self.model_tester.image_size)
									)
									__lowercase					=				(
									    config.patch_size
									    if isinstance(config.patch_size			,     collections.abc.Iterable   )
									    else (config.patch_size, config.patch_size)
									)

									__lowercase					=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
									__lowercase					=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

									for model_class in self.all_model_classes:
															__lowercase					=				True
															self.check_hidden_states_output(lowercase			,     lowercase			,     lowercase			,     (padded_height, padded_width)   )

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__lowercase					=				True
															self.check_hidden_states_output(lowercase			,     lowercase			,     lowercase			,     (padded_height, padded_width)   )





			@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""   )
			def 		snake_case__			(				self	:       Any   )						->					Any:
									"""simple docstring"""







									pass





			@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""   )
			def 		snake_case__			(				self	:       List[str]   )						->					str:
									"""simple docstring"""







									pass





			@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""   )
			def 		snake_case__			(				self	:       str   )						->					Union[str, Any]:
									"""simple docstring"""







									pass







			def 		snake_case__			(				self	:       Tuple   )						->					List[Any]:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()

									def set_nan_tensor_to_zero(lowercase	:       Optional[int]   ):
															__lowercase					=				0
															return t

									def check_equivalence(lowercase	:       Optional[int]			,     lowercase	:       str			,     lowercase	:       str			,     lowercase	:       Tuple={}   ):
															with torch.no_grad():
																					__lowercase					=				model(**lowercase			,     return_dict=lowercase			,     **lowercase   )
																					__lowercase					=				model(**lowercase			,     return_dict=lowercase			,     **lowercase   ).to_tuple()

																					def recursive_check(lowercase	:       int			,     lowercase	:       Optional[Any]   ):
																											if isinstance(lowercase			,     (List, Tuple)   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(lowercase			,     lowercase   ):
																																							recursive_check(lowercase			,     lowercase   )
																											elif isinstance(lowercase			,     lowercase   ):
																																	for tuple_iterable_value, dict_iterable_value in zip(
																																	    tuple_object.values()			,     dict_object.values()   ):
																																							recursive_check(lowercase			,     lowercase   )
																											elif tuple_object is None:
																																	return
																											else:
																																	self.assertTrue(
																																	    torch.allclose(
																																	        set_nan_tensor_to_zero(lowercase   )			,     set_nan_tensor_to_zero(lowercase   )			,     atol=1E-5   )			,     msg=(
																																	        """Tuple and dict output are not equal. Difference:"""
																																	        F" {torch.max(torch.abs(tuple_object - dict_object   )   )}. Tuple has `nan`:"
																																	        F" {torch.isnan(lowercase   ).any()} and `inf`: {torch.isinf(lowercase   )}. Dict has"
																																	        F" `nan`: {torch.isnan(lowercase   ).any()} and `inf`: {torch.isinf(lowercase   )}."
																																	    )			,     )

																					recursive_check(lowercase			,     lowercase   )

									for model_class in self.all_model_classes:
															__lowercase					=				model_class(lowercase   )
															model.to(lowercase   )
															model.eval()

															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase   )
															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase   )
															check_equivalence(lowercase			,     lowercase			,     lowercase   )

															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase			,     return_labels=lowercase   )
															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase			,     return_labels=lowercase   )
															check_equivalence(lowercase			,     lowercase			,     lowercase   )

															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase   )
															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase   )
															check_equivalence(lowercase			,     lowercase			,     lowercase			,     {"""output_hidden_states""": True}   )

															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase			,     return_labels=lowercase   )
															__lowercase					=				self._prepare_for_class(lowercase			,     lowercase			,     return_labels=lowercase   )
															check_equivalence(lowercase			,     lowercase			,     lowercase			,     {"""output_hidden_states""": True}   )





@require_torch
class 			_lowerCAmelCase   ( unittest.TestCase    ,					_UpperCAmelCase			):






			"""simple docstring"""





			lowercase__      :  List[str]      =			(MaskFormerSwinBackbone,) if is_torch_available() else ()
			lowercase__      :  Any      =			MaskFormerSwinConfig





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				MaskFormerSwinModelTester(self   )







			def 		snake_case__			(				self	:       Any   )						->					Tuple:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()
									__lowercase					=				inputs_dict["""pixel_values"""].shape[0]

									for backbone_class in self.all_model_classes:
															__lowercase					=				backbone_class(lowercase   )
															backbone.to(lowercase   )
															backbone.eval()

															__lowercase					=				backbone(**lowercase   )

															# Test default outputs and verify feature maps
															self.assertIsInstance(outputs.feature_maps			,     lowercase   )
															self.assertTrue(len(outputs.feature_maps   ) == len(backbone.channels   )   )
															for feature_map, n_channels in zip(outputs.feature_maps			,     backbone.channels   ):
																					self.assertTrue(feature_map.shape[:2]			,     (batch_size, n_channels)   )
															self.assertIsNone(outputs.hidden_states   )
															self.assertIsNone(outputs.attentions   )

															# Test output_hidden_states=True
															__lowercase					=				backbone(**lowercase			,     output_hidden_states=lowercase   )
															self.assertIsNotNone(outputs.hidden_states   )
															self.assertTrue(len(outputs.hidden_states   )			,     len(backbone.stage_names   )   )
															# We skip the stem layer
															for hidden_states, n_channels in zip(outputs.hidden_states[1:]			,     backbone.channels   ):
																					for hidden_state in hidden_states:
																											# Hidden states are in the format (batch_size, (height * width), n_channels)
																											__lowercase	,			__lowercase	,			__lowercase					=				hidden_state.shape
																											self.assertTrue((h_batch_size, h_n_channels)			,     (batch_size, n_channels)   )

            # Test output_attentions=True
															if self.has_attentions:
																					__lowercase					=				backbone(**lowercase			,     output_attentions=lowercase   )
																					self.assertIsNotNone(outputs.attentions   )





