





'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
							from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor




class       UpperCamelCase__			:




					"""simple docstring"""


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	parent
							SCREAMING_SNAKE_CASE				:							str						=	batch_size
							SCREAMING_SNAKE_CASE				:							List[Any]						=	image_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	patch_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	num_channels
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	embed_dim
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	depths
							SCREAMING_SNAKE_CASE				:							List[Any]						=	num_heads
							SCREAMING_SNAKE_CASE				:							List[Any]						=	window_size
							SCREAMING_SNAKE_CASE				:							str						=	mlp_ratio
							SCREAMING_SNAKE_CASE				:							Any						=	qkv_bias
							SCREAMING_SNAKE_CASE				:							Dict						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	drop_path_rate
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	hidden_act
							SCREAMING_SNAKE_CASE				:							int						=	use_absolute_embeddings
							SCREAMING_SNAKE_CASE				:							List[Any]						=	patch_norm
							SCREAMING_SNAKE_CASE				:							str						=	layer_norm_eps
							SCREAMING_SNAKE_CASE				:							Any						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Dict						=	is_training
							SCREAMING_SNAKE_CASE				:							int						=	scope
							SCREAMING_SNAKE_CASE				:							Any						=	use_labels
							SCREAMING_SNAKE_CASE				:							List[str]						=	type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	encoder_stride
							SCREAMING_SNAKE_CASE				:							List[Any]						=	out_features
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	out_indices


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	None
							if self.use_labels:
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size		)

							SCREAMING_SNAKE_CASE				:							int						=	self.get_config()

							return config, pixel_values, labels


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							return SwinConfig(
							    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 lowerCamelCase_	(		self							:	Optional[Any]     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	SwinModel(config=lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
							SCREAMING_SNAKE_CASE				:							int						=	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 lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	SwinBackbone(config=lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							int						=	model(lowerCamelCase_		)

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps		)     ,		len(config.out_features		)		)
							self.parent.assertListEqual(list(result.feature_maps[0].shape		)     ,		[self.batch_size, model.channels[0], 16, 16]		)

							# verify channels
							self.parent.assertEqual(len(model.channels		)     ,		len(config.out_features		)		)

							# verify backbone works with out_features=None
							SCREAMING_SNAKE_CASE				:							List[Any]						=	None
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinBackbone(config=lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(lowerCamelCase_		)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps		)     ,		1		)
							self.parent.assertListEqual(list(result.feature_maps[0].shape		)     ,		[self.batch_size, model.channels[-1], 4, 4]		)

							# verify channels
							self.parent.assertEqual(len(model.channels		)     ,		1		)


					def lowerCamelCase_	(		self							:	List[Any]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	SwinForMaskedImageModeling(config=lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							int						=	model(lowerCamelCase_		)
							self.parent.assertEqual(
							    result.logits.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

							# test greyscale images
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	1
							SCREAMING_SNAKE_CASE				:							List[str]						=	SwinForMaskedImageModeling(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)		)


					def lowerCamelCase_	(		self							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.type_sequence_label_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinForImageClassification(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()
							SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		labels=lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							SCREAMING_SNAKE_CASE				:							str						=	1
							SCREAMING_SNAKE_CASE				:							List[Any]						=	SwinForImageClassification(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()

							SCREAMING_SNAKE_CASE				:							Any						=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(lowerCamelCase_		)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)		)



					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE
							),    (
							    SCREAMING_SNAKE_CASE
							),    (
							    SCREAMING_SNAKE_CASE
							),    
							)				:							Union[str, Any]						=	config_and_inputs
							SCREAMING_SNAKE_CASE				:							List[str]						=	{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class       UpperCamelCase__			(			lowercase_		,			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					(
					    (
					        SwinModel,
					        SwinBackbone,
					        SwinForImageClassification,
					        SwinForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE__						=					(
					    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE__						=					True

					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False
					SCREAMING_SNAKE_CASE__						=					False


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	SwinModelTester(self		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	ConfigTester(self     ,		config_class=lowerCamelCase_     ,		embed_dim=37		)


					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''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 lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							return


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase_		)


					@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""		)
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							pass


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							super().test_training_gradient_checkpointing()


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)


					@unittest.skip(reason="""Swin does not use inputs_embeds"""		)
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							pass


					@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""		)
					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							pass


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs_for_common()

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


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE				:							List[Any]						=	["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							Tuple						=	True

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	True
									SCREAMING_SNAKE_CASE				:							Any						=	False
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	True
									SCREAMING_SNAKE_CASE				:							List[Any]						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()
									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							List[str]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							Dict						=	outputs.attentions
									SCREAMING_SNAKE_CASE				:							Any						=	len(self.model_tester.depths		)
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE				:							List[str]						=	True
									SCREAMING_SNAKE_CASE				:							List[str]						=	config.window_size**2
									SCREAMING_SNAKE_CASE				:							str						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()
									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							List[str]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	outputs.attentions
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	len(lowerCamelCase_		)

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE				:							List[str]						=	True
									SCREAMING_SNAKE_CASE				:							Dict						=	True
									SCREAMING_SNAKE_CASE				:							Dict						=	model_class(lowerCamelCase_		)
									model.to(lowerCamelCase_		)
									model.eval()
									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

									# also another +1 for reshaped_hidden_states
									SCREAMING_SNAKE_CASE				:							Any						=	1 if model_class.__name__ == """SwinBackbone""" else 2
									self.assertEqual(out_len + added_hidden_states     ,		len(lowerCamelCase_		)		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	outputs.attentions

									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]		)     ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,		)


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(lowerCamelCase_		)
							model.to(lowerCamelCase_		)
							model.eval()

							with torch.no_grad():
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(**self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)		)

							SCREAMING_SNAKE_CASE				:							str						=	outputs.hidden_states

							SCREAMING_SNAKE_CASE				:							Dict						=	getattr(
							    self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths		) + 1		)
							self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

							# Swin has a different seq_length
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	(
							    config.patch_size
							    if isinstance(config.patch_size     ,		collections.abc.Iterable		)
							    else (config.patch_size, config.patch_size)
							)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	(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]     ,		)

							if not model_class.__name__ == "SwinBackbone":
									SCREAMING_SNAKE_CASE				:							Tuple						=	outputs.reshaped_hidden_states
									self.assertEqual(len(lowerCamelCase_		)     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	reshaped_hidden_states[0].shape
									SCREAMING_SNAKE_CASE				:							Any						=	(
									    reshaped_hidden_states[0].view(lowerCamelCase_     ,		lowerCamelCase_     ,		height * width		).permute(0     ,		2     ,		1		)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]		)     ,		[num_patches, self.model_tester.embed_dim]     ,		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	(
							    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:
									SCREAMING_SNAKE_CASE				:							List[str]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE				:							Any						=	True

									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE				:							int						=	3

							SCREAMING_SNAKE_CASE				:							int						=	(
							    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)
							)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	(
							    config.patch_size
							    if isinstance(config.patch_size     ,		collections.abc.Iterable		)
							    else (config.patch_size, config.patch_size)
							)

							SCREAMING_SNAKE_CASE				:							Any						=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							SCREAMING_SNAKE_CASE				:							List[Any]						=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	True
									self.check_hidden_states_output(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		(padded_height, padded_width)		)


					@slow
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE				:							Dict						=	SwinModel.from_pretrained(lowerCamelCase_		)
									self.assertIsNotNone(lowerCamelCase_		)



					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.model_tester.prepare_config_and_inputs_for_common()

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	_config_zero_init(lowerCamelCase_		)
							for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(config=lowerCamelCase_		)
									for name, param in model.named_parameters():
											if "embeddings" not in name and param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''     ,		)







@require_vision
@require_torch
class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""


					@cached_property
					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		)
							    if is_vision_available()
							    else None
							)



					@slow
					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""		).to(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.default_image_processor

							SCREAMING_SNAKE_CASE				:							Any						=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							int						=	image_processor(images=lowerCamelCase_     ,		return_tensors="""pt"""		).to(lowerCamelCase_		)

							# forward pass
							with torch.no_grad():
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(**lowerCamelCase_		)

							# verify the logits
							SCREAMING_SNAKE_CASE				:							List[str]						=	torch.Size((1, 10_00)		)
							self.assertEqual(outputs.logits.shape     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	torch.tensor([-0.0_948, -0.6_454, -0.0_921]		).to(lowerCamelCase_		)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		lowerCamelCase_     ,		atol=1e-4		)		)







@require_torch
class       UpperCamelCase__			(			unittest.TestCase		,			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					(SwinBackbone,) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE__						=					SwinConfig


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	SwinModelTester(self		)










'''simple docstring'''




from __future__ import annotations



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		if len(lowerCamelCase_					) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""					)
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	min(lowerCamelCase_	,	lowerCamelCase_					)
		return min_num


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)



