






import collections
import inspect
import unittest

from transformers import FocalNetConfig
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 (
	    FocalNetBackbone,
	    FocalNetForImageClassification,
	    FocalNetForMaskedImageModeling,
	    FocalNetModel,
	)
	from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=13 ,  __lowerCamelCase				:					List[Any]=32 ,  __lowerCamelCase				:					Tuple=2 ,  __lowerCamelCase				:					Optional[int]=3 ,  __lowerCamelCase				:					Optional[int]=16 ,  __lowerCamelCase				:					Optional[int]=[32, 64, 128] ,  __lowerCamelCase				:					int=[1, 2, 1] ,  __lowerCamelCase				:					int=[2, 2, 4] ,  __lowerCamelCase				:					Any=2 ,  __lowerCamelCase				:					Tuple=2.0 ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					List[Any]=0.0 ,  __lowerCamelCase				:					Dict=0.0 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					List[str]="gelu" ,  __lowerCamelCase				:					Tuple=False ,  __lowerCamelCase				:					str=True ,  __lowerCamelCase				:					List[Any]=0.02 ,  __lowerCamelCase				:					Optional[Any]=1E-5 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					Optional[Any]=None ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					str=10 ,  __lowerCamelCase				:					Tuple=8 ,  __lowerCamelCase				:					List[Any]=["stage1", "stage2"] ,  __lowerCamelCase				:					Union[str, Any]=[1, 2] ,  ):
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Dict          =	batch_size
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Union[str, Any]          =	num_channels
		UpperCamelCase     :str          =	embed_dim
		UpperCamelCase     :int          =	hidden_sizes
		UpperCamelCase     :Any          =	depths
		UpperCamelCase     :Union[str, Any]          =	num_heads
		UpperCamelCase     :List[str]          =	window_size
		UpperCamelCase     :int          =	mlp_ratio
		UpperCamelCase     :int          =	qkv_bias
		UpperCamelCase     :Optional[int]          =	hidden_dropout_prob
		UpperCamelCase     :Optional[int]          =	attention_probs_dropout_prob
		UpperCamelCase     :Tuple          =	drop_path_rate
		UpperCamelCase     :Optional[Any]          =	hidden_act
		UpperCamelCase     :List[Any]          =	use_absolute_embeddings
		UpperCamelCase     :List[Any]          =	patch_norm
		UpperCamelCase     :int          =	layer_norm_eps
		UpperCamelCase     :Union[str, Any]          =	initializer_range
		UpperCamelCase     :Union[str, Any]          =	is_training
		UpperCamelCase     :Tuple          =	scope
		UpperCamelCase     :int          =	use_labels
		UpperCamelCase     :Optional[int]          =	type_sequence_label_size
		UpperCamelCase     :Union[str, Any]          =	encoder_stride
		UpperCamelCase     :List[str]          =	out_features
		UpperCamelCase     :List[Any]          =	out_indices





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :int          =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

		UpperCamelCase     :Dict          =	self.get_config()

		return config, pixel_values, labels





	def 						_A (		self				:					Optional[int]	):
		return FocalNetConfig(
		    image_size=self.image_size ,  patch_size=self.patch_size ,  num_channels=self.num_channels ,  embed_dim=self.embed_dim ,  hidden_sizes=self.hidden_sizes ,  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 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					Optional[int]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetModel(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
		UpperCamelCase     :Tuple          =	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 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :List[str]          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :List[str]          =	model(__lowerCamelCase	)

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps	) ,  len(config.out_features	)	)
		self.parent.assertListEqual(list(result.feature_maps[0].shape	) ,  [self.batch_size, self.image_size, 8, 8]	)

		# verify channels
		self.parent.assertEqual(len(model.channels	) ,  len(config.out_features	)	)
		self.parent.assertListEqual(model.channels ,  config.hidden_sizes[:-1]	)

		# verify backbone works with out_features=None
		UpperCamelCase     :List[Any]          =	None
		UpperCamelCase     :Dict          =	FocalNetBackbone(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Dict          =	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, self.image_size * 2, 4, 4]	)

		# verify channels
		self.parent.assertEqual(len(model.channels	) ,  1	)
		self.parent.assertListEqual(model.channels ,  [config.hidden_sizes[-1]]	)





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					int	):
		UpperCamelCase     :Tuple          =	FocalNetForMaskedImageModeling(config=__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Optional[Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(
		    result.reconstruction.shape ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

		# test greyscale images
		UpperCamelCase     :str          =	1
		UpperCamelCase     :List[str]          =	FocalNetForMaskedImageModeling(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :str          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.reconstruction.shape ,  (self.batch_size, 1, self.image_size, self.image_size)	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[Any]	):
		UpperCamelCase     :Optional[int]          =	self.type_sequence_label_size
		UpperCamelCase     :Optional[Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()
		UpperCamelCase     :Tuple          =	model(__lowerCamelCase ,  labels=__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)

		# test greyscale images
		UpperCamelCase     :List[str]          =	1
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

		UpperCamelCase     :Optional[int]          =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
		UpperCamelCase     :Union[str, Any]          =	model(__lowerCamelCase	)
		self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.type_sequence_label_size)	)





	def 						_A (		self				:					str	):
		UpperCamelCase     :Optional[Any]          =	self.prepare_config_and_inputs()

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Tuple          =	config_and_inputs
		UpperCamelCase     :str          =	{"""pixel_values""": pixel_values}
		return config, inputs_dict

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   _a		,   unittest.TestCase      ):
	snake_case__							:     str     	=  (
	    (
	        FocalNetModel,
	        FocalNetForImageClassification,
	        FocalNetForMaskedImageModeling,
	        FocalNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case__							:     List[str]     	=  (
	    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
	    if is_torch_available()
	    else {}
	)
	snake_case__							:     Optional[int]     	=  False

	snake_case__							:     Tuple     	=  False
	snake_case__							:     Optional[Any]     	=  False
	snake_case__							:     Optional[int]     	=  False
	snake_case__							:     Union[str, Any]     	=  False





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Dict          =	FocalNetModelTester(self	)
		UpperCamelCase     :Optional[int]          =	ConfigTester(self ,  config_class=__lowerCamelCase ,  embed_dim=37 ,  has_text_modality=__lowerCamelCase	)





	def 						_A (		self				:					Union[str, Any]	):
		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 						_A (		self				:					List[Any]	):
		return





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase	)





	@unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
	def 						_A (		self				:					Optional[int]	):
		pass





	@unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
	def 						_A (		self				:					Optional[Any]	):
		pass





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     ,      UpperCamelCase     :Tuple          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
			self.assertIsInstance(model.get_input_embeddings() ,  (nn.Module)	)
			UpperCamelCase     :Tuple          =	model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__lowerCamelCase ,  nn.Linear	)	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :int          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :Dict          =	model_class(__lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	inspect.signature(model.forward	)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			UpperCamelCase     :Union[str, Any]          =	[*signature.parameters.keys()]

			UpperCamelCase     :str          =	["""pixel_values"""]
			self.assertListEqual(arg_names[:1] ,  __lowerCamelCase	)





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					int ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Optional[Any]          =	model_class(__lowerCamelCase	)
		model.to(__lowerCamelCase	)
		model.eval()

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

		UpperCamelCase     :Tuple          =	outputs.hidden_states

		UpperCamelCase     :Union[str, Any]          =	getattr(
		    self.model_tester ,  """expected_num_hidden_layers""" ,  len(self.model_tester.depths	) + 1	)
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

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

		UpperCamelCase     :Any          =	(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] ,  )

		UpperCamelCase     :Union[str, Any]          =	outputs.reshaped_hidden_states
		self.assertEqual(len(__lowerCamelCase	) ,  __lowerCamelCase	)

		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	reshaped_hidden_states[0].shape
		UpperCamelCase     :List[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 						_A (		self				:					List[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Optional[Any]          =	(
		    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[:-1]:
			UpperCamelCase     :List[Any]          =	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"]
			UpperCamelCase     :Optional[Any]          =	True

			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					int	):
		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.model_tester.prepare_config_and_inputs_for_common()
		UpperCamelCase     :List[str]          =	3

		UpperCamelCase     :Optional[Any]          =	(
		    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)
		)
		UpperCamelCase     :List[str]          =	(
		    config.patch_size
		    if isinstance(config.patch_size ,  collections.abc.Iterable	)
		    else (config.patch_size, config.patch_size)
		)

		UpperCamelCase     :Optional[Any]          =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		UpperCamelCase     :Dict          =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes[:-1]:
			UpperCamelCase     :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"]
			UpperCamelCase     :Dict          =	True
			self.check_hidden_states_output(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  (padded_height, padded_width)	)





	@slow
	def 						_A (		self				:					Tuple	):
		for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCamelCase     :Union[str, Any]          =	FocalNetModel.from_pretrained(__lowerCamelCase	)
			self.assertIsNotNone(__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.model_tester.prepare_config_and_inputs_for_common()

		UpperCamelCase     :Dict          =	_config_zero_init(__lowerCamelCase	)
		for model_class in self.all_model_classes:
			UpperCamelCase     :Optional[Any]          =	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     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	@cached_property
	def 						_A (		self				:					List[str]	):
		# TODO update organization
		return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None





	@slow
	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Union[str, Any]          =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	self.default_image_processor

		UpperCamelCase     :List[str]          =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
		UpperCamelCase     :int          =	image_processor(images=__lowerCamelCase ,  return_tensors="""pt"""	).to(__lowerCamelCase	)

		# forward pass
		with torch.no_grad():
			UpperCamelCase     :int          =	model(**__lowerCamelCase	)

		# verify the logits
		UpperCamelCase     :str          =	torch.Size((1, 1_000)	)
		self.assertEqual(outputs.logits.shape ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	torch.tensor([0.2166, -0.4368, 0.2191]	).to(__lowerCamelCase	)
		self.assertTrue(torch.allclose(outputs.logits[0, :3] ,  __lowerCamelCase ,  atol=1E-4	)	)
		self.assertTrue(outputs.logits.argmax(dim=-1	).item() ,  281	)

@require_torch
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  (FocalNetBackbone,) if is_torch_available() else ()
	snake_case__							:     str     	=  FocalNetConfig

	snake_case__							:     int     	=  False





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Optional[Any]          =	FocalNetModelTester(self	)







import inspect
import unittest

from transformers import ViTMSNConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=30					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=None					,  )			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	List[str] =				batch_size
          a__						:	List[str] =				image_size
          a__						:	Dict =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	List[Any] =				is_training
          a__						:	str =				use_labels
          a__						:	Dict =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Tuple =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	List[str] =				hidden_act
          a__						:	List[str] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Dict =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Optional[int] =				scope

          # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          a__						:	List[str] =				(image_size // patch_size) ** 2
          a__						:	Any =				num_patches + 1

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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

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

          a__						:	List[str] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return ViTMSNConfig(
              image_size=self.image_size					,  patch_size=self.patch_size					,  num_channels=self.num_channels					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	int =				ViTMSNModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.type_sequence_label_size
          a__						:	List[str] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  labels=lowercase)
          print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
          print('Labels: {labels}')
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

          # test greyscale images
          a__						:	List[str] =				1
          a__						:	Optional[int] =				ViTMSNForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()

          a__						:	Dict =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          a__						:	Dict =				model(lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[Any] =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Optional[Any] =				config_and_inputs
          a__						:	Dict =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   __A   :				Tuple         =  (
       {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  False
   __A   :				Optional[Any]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				Any         =  False

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[int] =				ViTMSNModelTester(self)
          a__						:	Union[str, Any] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViTMSN does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

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

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

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

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

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

                 a__						:	Optional[Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Tuple =				ViTMSNModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		Dict:
       a__						:	List[str] =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



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







          '''simple docstring'''

          return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          torch.manual_seed(2)
          a__						:	List[str] =				ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(lowercase)

          a__						:	Any =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Tuple =				model(**lowercase)

          # verify the logits
          a__						:	Union[str, Any] =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Any =				torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(lowercase)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,  lowercase					,  atol=1e-4))


