
"""simple docstring"""






from __future__ import annotations




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Dict		, lowerCamelCase_    :int					)							->			None:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= data
			SCREAMING_SNAKE_CASE       :     Node | None				= None
			SCREAMING_SNAKE_CASE       :     Node | None				= None





def __A						(   a_ :							Node | None     )->							None:  # In Order traversal of the tree




		'''simple docstring'''






		if tree:
				display(tree.left     )
				print(tree.data     )
				display(tree.right     )





def __A						(   a_ :							Node | None     )->							int:




		'''simple docstring'''






		return 1 + max(depth_of_tree(tree.left     )		,		depth_of_tree(tree.right     )     ) if tree else 0





def __A						(   a_ :							Node     )->							bool:




		'''simple docstring'''






		if not tree:
				return True
		if tree.left and tree.right:
				return is_full_binary_tree(tree.left     ) and is_full_binary_tree(tree.right     )
		else:
				return not tree.left and not tree.right





def __A						(   )->							None:  # Main function for testing.




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= Node(1     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= Node(2     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= Node(3     )
		SCREAMING_SNAKE_CASE       :     int				= Node(4     )
		SCREAMING_SNAKE_CASE       :     str				= Node(5     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= Node(6     )
		SCREAMING_SNAKE_CASE       :     Any				= Node(7     )
		SCREAMING_SNAKE_CASE       :     List[str]				= Node(8     )
		SCREAMING_SNAKE_CASE       :     Dict				= Node(9     )

		print(is_full_binary_tree(a_     )     )
		print(depth_of_tree(a_     )     )
		print('''Tree is: '''     )
		display(a_     )


if __name__ == "__main__":
					main()







"""simple docstring"""






import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
					from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :str		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Any=13		, lowerCamelCase_    :Any=32		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :Union[str, Any]=16		, lowerCamelCase_    :int=[1, 2, 1]		, lowerCamelCase_    :str=[2, 2, 4]		, lowerCamelCase_    :str=2		, lowerCamelCase_    :Tuple=2.0		, lowerCamelCase_    :Union[str, Any]=True		, lowerCamelCase_    :str=0.0		, lowerCamelCase_    :Optional[int]=0.0		, lowerCamelCase_    :Dict=0.1		, lowerCamelCase_    :Union[str, Any]="gelu"		, lowerCamelCase_    :str=False		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :int=0.0_2		, lowerCamelCase_    :List[Any]=1E-5		, lowerCamelCase_    :int=True		, lowerCamelCase_    :str=None		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Union[str, Any]=10		, lowerCamelCase_    :List[Any]=8		, )							->			int:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     int				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)

			SCREAMING_SNAKE_CASE       :     Tuple				= self.get_config()

			return config, pixel_values, labels





	def   __lowerCAmelCase (						self    :int					)							->			int:







			'''simple docstring'''




			return SwinvaConfig(
			    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		, )





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= SwinvaModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
			SCREAMING_SNAKE_CASE       :     Dict				= 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    :str		, lowerCamelCase_    :str		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= SwinvaForMaskedImageModeling(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Dict				= 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       :     Tuple				= 1
			SCREAMING_SNAKE_CASE       :     List[Any]				= SwinvaForMaskedImageModeling(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

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





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :int					)							->			Tuple:







			'''simple docstring'''




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







	def   __lowerCAmelCase (						self    :List[Any]					)							->			List[Any]:







			'''simple docstring'''




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





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	UpperCamelCase				=  (
	    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= SwinvaModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Dict				= ConfigTester(self		, config_class=lowerCamelCase_		, embed_dim=37					)





	def   __lowerCAmelCase (						self    :Dict					)							->			List[str]:







			'''simple docstring'''




			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]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, 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    :str					)							->			List[Any]:







			'''simple docstring'''




			pass





	@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''					)
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Any:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :int					)							->			Union[str, Any]:







			'''simple docstring'''




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

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     List[Any]				= model_class(lowerCamelCase_					)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)					)
					SCREAMING_SNAKE_CASE       :     str				= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(lowerCamelCase_		, nn.Linear					)					)





	def   __lowerCAmelCase (						self    :int					)							->			List[Any]:







			'''simple docstring'''




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

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= model_class(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     List[str]				= 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       :     Union[str, Any]				= ['''pixel_values''']
					self.assertListEqual(arg_names[:1]		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :int					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= 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[str]				= True
					SCREAMING_SNAKE_CASE       :     List[Any]				= False
					SCREAMING_SNAKE_CASE       :     Any				= True
					SCREAMING_SNAKE_CASE       :     Any				= model_class(lowerCamelCase_					)
					model.to(lowerCamelCase_					)
					model.eval()
					with torch.no_grad():
							SCREAMING_SNAKE_CASE       :     str				= model(**self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_					)					)
					SCREAMING_SNAKE_CASE       :     Tuple				= outputs.attentions
					SCREAMING_SNAKE_CASE       :     Tuple				= 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       :     Union[str, Any]				= True
					SCREAMING_SNAKE_CASE       :     Optional[int]				= config.window_size**2
					SCREAMING_SNAKE_CASE       :     str				= model_class(lowerCamelCase_					)
					model.to(lowerCamelCase_					)
					model.eval()
					with torch.no_grad():
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(**self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_					)					)
					SCREAMING_SNAKE_CASE       :     Dict				= 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       :     Dict				= len(lowerCamelCase_					)

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

					if hasattr(self.model_tester		, '''num_hidden_states_types'''					):
							SCREAMING_SNAKE_CASE       :     Any				= self.model_tester.num_hidden_states_types
					else:
							# also another +1 for reshaped_hidden_states
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= 2
					self.assertEqual(out_len + added_hidden_states		, len(lowerCamelCase_					)					)

					SCREAMING_SNAKE_CASE       :     Tuple				= 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    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :Any		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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       :     Dict				= outputs.hidden_states

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

			# Swinv2 has a different seq_length
			SCREAMING_SNAKE_CASE       :     List[str]				= (
			    config.patch_size
			    if isinstance(config.patch_size		, collections.abc.Iterable					)
			    else (config.patch_size, config.patch_size)
			)

			SCREAMING_SNAKE_CASE       :     Dict				= (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]		, )

			SCREAMING_SNAKE_CASE       :     Any				= outputs.reshaped_hidden_states
			self.assertEqual(len(lowerCamelCase_					)		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= reshaped_hidden_states[0].shape
			SCREAMING_SNAKE_CASE       :     Optional[int]				= (
			    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    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     List[str]				= (
			    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       :     Tuple				= 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       :     List[str]				= True

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





	def   __lowerCAmelCase (						self    :str					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     List[Any]				= 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       :     Optional[int]				= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			SCREAMING_SNAKE_CASE       :     Optional[int]				= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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





	def   __lowerCAmelCase (						self    :str					)							->			List[Any]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_					)





	@slow
	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




			for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     Tuple				= SwinvaModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :int					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= self.model_tester.prepare_config_and_inputs_for_common()

			SCREAMING_SNAKE_CASE       :     Tuple				= _config_zero_init(lowerCamelCase_					)
			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Dict				= model_class(config=lowerCamelCase_					)
					for name, param in model.named_parameters():
							if "embeddings" not in name and "logit_scale" 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 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@cached_property
	def   __lowerCAmelCase (						self    :Dict					)							->			List[Any]:







			'''simple docstring'''




			return (
			    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''					)
			    if is_vision_available()
			    else None
			)







	@slow
	def   __lowerCAmelCase (						self    :Dict					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''					).to(
			    lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Dict				= self.default_image_processor

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			SCREAMING_SNAKE_CASE       :     List[str]				= image_processor(images=lowerCamelCase_		, return_tensors='''pt'''					).to(lowerCamelCase_					)

			# forward pass
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Tuple				= 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([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]					).to(lowerCamelCase_					)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]		, lowerCamelCase_		, atol=1E-4					)					)





