code: 
  
  
  
  
  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 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_=13		,		snake_case_=32		,		snake_case_=2		,		snake_case_=3		,		snake_case_=16		,		snake_case_=[1, 2, 1]		,		snake_case_=[2, 2, 4]		,		snake_case_=2		,		snake_case_=2.0		,		snake_case_=True		,		snake_case_=0.0		,		snake_case_=0.0		,		snake_case_=0.1		,		snake_case_="gelu"		,		snake_case_=False		,		snake_case_=True		,		snake_case_=0.02		,		snake_case_=1E-5		,		snake_case_=True		,		snake_case_=None		,		snake_case_=True		,		snake_case_=10		,		snake_case_=8		,		snake_case_=["stage1", "stage2"]		,		snake_case_=[1, 2]		,		)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =parent
  											SCREAMING_SNAKE_CASE			: List[Any]	     =batch_size
  											SCREAMING_SNAKE_CASE			: str	     =image_size
  											SCREAMING_SNAKE_CASE			: List[str]	     =patch_size
  											SCREAMING_SNAKE_CASE			: int	     =num_channels
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =embed_dim
  											SCREAMING_SNAKE_CASE			: Dict	     =depths
  											SCREAMING_SNAKE_CASE			: int	     =num_heads
  											SCREAMING_SNAKE_CASE			: List[Any]	     =window_size
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =mlp_ratio
  											SCREAMING_SNAKE_CASE			: str	     =qkv_bias
  											SCREAMING_SNAKE_CASE			: Dict	     =hidden_dropout_prob
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =attention_probs_dropout_prob
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =drop_path_rate
  											SCREAMING_SNAKE_CASE			: str	     =hidden_act
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =use_absolute_embeddings
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =patch_norm
  											SCREAMING_SNAKE_CASE			: Any	     =layer_norm_eps
  											SCREAMING_SNAKE_CASE			: Tuple	     =initializer_range
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =is_training
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =scope
  											SCREAMING_SNAKE_CASE			: int	     =use_labels
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =type_sequence_label_size
  											SCREAMING_SNAKE_CASE			: Dict	     =encoder_stride
  											SCREAMING_SNAKE_CASE			: List[Any]	     =out_features
  											SCREAMING_SNAKE_CASE			: int	     =out_indices
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
  
  											SCREAMING_SNAKE_CASE			: str	     =None
  											if self.use_labels:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =ids_tensor([self.batch_size]		,		self.type_sequence_label_size							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_config()
  
  											return config, pixel_values, labels
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											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     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =SwinModel(config=snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =model(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
  											SCREAMING_SNAKE_CASE			: 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		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     =SwinBackbone(config=snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =model(snake_case_							)
  
  											# 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			: Optional[int]	     =None
  											SCREAMING_SNAKE_CASE			: List[str]	     =SwinBackbone(config=snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =model(snake_case_							)
  
  											# 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     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: List[str]	     =SwinForMaskedImageModeling(config=snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  											SCREAMING_SNAKE_CASE			: Any	     =model(snake_case_							)
  											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			: int	     =SwinForMaskedImageModeling(snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  
  											SCREAMING_SNAKE_CASE			: Dict	     =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, 1, self.image_size, self.image_size)							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.type_sequence_label_size
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =SwinForImageClassification(snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  											SCREAMING_SNAKE_CASE			: str	     =model(snake_case_		,		labels=snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size)							)
  
  											# test greyscale images
  											SCREAMING_SNAKE_CASE			: int	     =1
  											SCREAMING_SNAKE_CASE			: List[Any]	     =SwinForImageClassification(snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.type_sequence_label_size)							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.prepare_config_and_inputs()
  											(
  											    SCREAMING_SNAKE_CASE							
  											)			: Optional[Any]	     =config_and_inputs
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ={'''pixel_values''': pixel_values}
  											return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__  ,   unittest.TestCase	):
  				lowerCamelCase__									=    (
  				    (
  				        SwinModel,
  				        SwinBackbone,
  				        SwinForImageClassification,
  				        SwinForMaskedImageModeling,
  				    )
  				    if is_torch_available()
  				    else ()
  				)
  				lowerCamelCase__									=    (
  				    {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
  				    if is_torch_available()
  				    else {}
  				)
  				lowerCamelCase__									=    True
  
  				lowerCamelCase__									=    False
  				lowerCamelCase__									=    False
  				lowerCamelCase__									=    False
  
  
  
  
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =SwinModelTester(self							)
  											SCREAMING_SNAKE_CASE			: Dict	     =ConfigTester(self		,		config_class=snake_case_		,		embed_dim=37							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											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							)    ->      Any:
  											return
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_model(*snake_case_							)
  
  
  
  
  				@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''							)
  				def     __a		( self							)    ->      int:
  											pass
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											super().test_training_gradient_checkpointing()
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_backbone(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      int:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_for_image_classification(*snake_case_							)
  
  
  
  
  				@unittest.skip(reason='''Swin does not use inputs_embeds'''							)
  				def     __a		( self							)    ->      Dict:
  											pass
  
  
  
  
  				@unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''							)
  				def     __a		( self							)    ->      List[str]:
  											pass
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											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			: int	     =model_class(snake_case_							)
  																		self.assertIsInstance(model.get_input_embeddings()		,		(nn.Module)							)
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =model.get_output_embeddings()
  																		self.assertTrue(x is None or isinstance(snake_case_		,		nn.Linear							)							)
  
  
  
  
  				def     __a		( self							)    ->      int:
  											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(snake_case_							)
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =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			: Dict	     =['''pixel_values''']
  																		self.assertListEqual(arg_names[:1]		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      int:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.model_tester.prepare_config_and_inputs_for_common()
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =True
  
  											for model_class in self.all_model_classes:
  																		SCREAMING_SNAKE_CASE			: Dict	     =True
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =False
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =True
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =model_class(snake_case_							)
  																		model.to(snake_case_							)
  																		model.eval()
  																		with torch.no_grad():
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =model(**self._prepare_for_class(snake_case_		,		snake_case_							)							)
  																		SCREAMING_SNAKE_CASE			: int	     =outputs.attentions
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(self.model_tester.depths							)
  																		self.assertEqual(len(snake_case_							)		,		snake_case_							)
  
  																		# check that output_attentions also work using config
  																		del inputs_dict["output_attentions"]
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =True
  																		SCREAMING_SNAKE_CASE			: int	     =config.window_size**2
  																		SCREAMING_SNAKE_CASE			: str	     =model_class(snake_case_							)
  																		model.to(snake_case_							)
  																		model.eval()
  																		with torch.no_grad():
  																									SCREAMING_SNAKE_CASE			: List[str]	     =model(**self._prepare_for_class(snake_case_		,		snake_case_							)							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =outputs.attentions
  																		self.assertEqual(len(snake_case_							)		,		snake_case_							)
  
  																		self.assertListEqual(
  																		    list(attentions[0].shape[-3:]							)		,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,		)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(snake_case_							)
  
  																		# Check attention is always last and order is fine
  																		SCREAMING_SNAKE_CASE			: str	     =True
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =True
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =model_class(snake_case_							)
  																		model.to(snake_case_							)
  																		model.eval()
  																		with torch.no_grad():
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =model(**self._prepare_for_class(snake_case_		,		snake_case_							)							)
  
  																		# also another +1 for reshaped_hidden_states
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =1 if model_class.__name__ == '''SwinBackbone''' else 2
  																		self.assertEqual(out_len + added_hidden_states		,		len(snake_case_							)							)
  
  																		SCREAMING_SNAKE_CASE			: Dict	     =outputs.attentions
  
  																		self.assertEqual(len(snake_case_							)		,		snake_case_							)
  
  																		self.assertListEqual(
  																		    list(self_attentions[0].shape[-3:]							)		,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,		)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: List[str]	     =model_class(snake_case_							)
  											model.to(snake_case_							)
  											model.eval()
  
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Any	     =model(**self._prepare_for_class(snake_case_		,		snake_case_							)							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =outputs.hidden_states
  
  											SCREAMING_SNAKE_CASE			: str	     =getattr(
  											    self.model_tester		,		'''expected_num_hidden_layers'''		,		len(self.model_tester.depths							) + 1							)
  											self.assertEqual(len(snake_case_							)		,		snake_case_							)
  
  											# Swin has a different seq_length
  											SCREAMING_SNAKE_CASE			: int	     =(
  											    config.patch_size
  											    if isinstance(config.patch_size		,		collections.abc.Iterable							)
  											    else (config.patch_size, config.patch_size)
  											)
  
  											SCREAMING_SNAKE_CASE			: List[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]		,		)
  
  											if not model_class.__name__ == "SwinBackbone":
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =outputs.reshaped_hidden_states
  																		self.assertEqual(len(snake_case_							)		,		snake_case_							)
  
  																		SCREAMING_SNAKE_CASE			: List[str]	     =reshaped_hidden_states[0].shape
  																		SCREAMING_SNAKE_CASE			: Dict	     =(
  																		    reshaped_hidden_states[0].view(snake_case_		,		snake_case_		,		height * width							).permute(0		,		2		,		1							)
  																		)
  																		self.assertListEqual(
  																		    list(reshaped_hidden_states.shape[-2:]							)		,		[num_patches, self.model_tester.embed_dim]		,		)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.model_tester.prepare_config_and_inputs_for_common()
  
  											SCREAMING_SNAKE_CASE			: 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:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =True
  																		self.check_hidden_states_output(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  																		# check that output_hidden_states also work using config
  																		del inputs_dict["output_hidden_states"]
  																		SCREAMING_SNAKE_CASE			: int	     =True
  
  																		self.check_hidden_states_output(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.model_tester.prepare_config_and_inputs_for_common()
  											SCREAMING_SNAKE_CASE			: int	     =3
  
  											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)
  											)
  											SCREAMING_SNAKE_CASE			: Dict	     =(
  											    config.patch_size
  											    if isinstance(config.patch_size		,		collections.abc.Iterable							)
  											    else (config.patch_size, config.patch_size)
  											)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =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(snake_case_		,		snake_case_		,		snake_case_		,		(padded_height, padded_width)							)
  
  																		# check that output_hidden_states also work using config
  																		del inputs_dict["output_hidden_states"]
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =True
  																		self.check_hidden_states_output(snake_case_		,		snake_case_		,		snake_case_		,		(padded_height, padded_width)							)
  
  
  
  
  				@slow
  				def     __a		( self							)    ->      List[Any]:
  											for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  																		SCREAMING_SNAKE_CASE			: str	     =SwinModel.from_pretrained(snake_case_							)
  																		self.assertIsNotNone(snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.model_tester.prepare_config_and_inputs_for_common()
  
  											SCREAMING_SNAKE_CASE			: Any	     =_config_zero_init(snake_case_							)
  											for model_class in self.all_model_classes:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =model_class(config=snake_case_							)
  																		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 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				@cached_property
  				def     __a		( self							)    ->      List[Any]:
  											return (
  											    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''							)
  											    if is_vision_available()
  											    else None
  											)
  
  
  
  
  
  
  
  				@slow
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''							).to(snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.default_image_processor
  
  											SCREAMING_SNAKE_CASE			: str	     =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =image_processor(images=snake_case_		,		return_tensors='''pt'''							).to(snake_case_							)
  
  											# forward pass
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =model(**snake_case_							)
  
  											# verify the logits
  											SCREAMING_SNAKE_CASE			: List[Any]	     =torch.Size((1, 1_000)							)
  											self.assertEqual(outputs.logits.shape		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =torch.tensor([-0.0948, -0.6454, -0.0921]							).to(snake_case_							)
  											self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		snake_case_		,		atol=1E-4							)							)
  
  
  
  
  
  
  
  @require_torch
  class 							_lowerCAmelCase		(     unittest.TestCase  ,   UpperCamelCase__	):
  				lowerCamelCase__									=    (SwinBackbone,) if is_torch_available() else ()
  				lowerCamelCase__									=    SwinConfig
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Tuple	     =SwinModelTester(self							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
  from ....modeling_utils import ModuleUtilsMixin
  from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	"""MMBTConfig"""
  
  
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      str:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: Dict	     =encoder
  											SCREAMING_SNAKE_CASE			: Tuple	     =nn.Linear(config.modal_hidden_size		,		config.hidden_size							)
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.position_embeddings
  											SCREAMING_SNAKE_CASE			: List[str]	     =embeddings.token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =embeddings.word_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.LayerNorm
  											SCREAMING_SNAKE_CASE			: Any	     =nn.Dropout(p=config.hidden_dropout_prob							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.proj_embeddings(self.encoder(snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =token_embeddings.size(1							)
  
  											if start_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.cat([start_token_embeds.unsqueeze(1							), token_embeddings]		,		dim=1							)
  
  											if end_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.cat([token_embeddings, end_token_embeds.unsqueeze(1							)]		,		dim=1							)
  
  											if position_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.arange(snake_case_		,		dtype=torch.long		,		device=input_modal.device							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =position_ids.unsqueeze(0							).expand(input_modal.size(0							)		,		snake_case_							)
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.zeros(
  																		    (input_modal.size(0							), seq_length)		,		dtype=torch.long		,		device=input_modal.device							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.position_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.token_type_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Dict	     =token_embeddings + position_embeddings + token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =self.LayerNorm(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.dropout(snake_case_							)
  											return embeddings
  
  
  
  
  
  
  
  _A							    =	r"""
      MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
      Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
      It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
      obtain state-of-the-art performance on various multimodal classification benchmark tasks.
  
      This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
      and behavior.
  
      Parameters:
          config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration.
          transformer (`nn.Module`): A text transformer that is used by MMBT.
              It should have embeddings, encoder, and pooler attributes.
          encoder (`nn.Module`): Encoder for the second modality.
              It should take in a batch of modal inputs and return k, n dimension embeddings.
  """
  
  _A							    =	r"""
      Args:
          input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
              The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
              Encoder, the shape would be (batch_size, channels, height, width)
          input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
              appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
              [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
              tasks.
          modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
          attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
              1]`:
  
              - 0 corresponds to a *sentence A* token,
              - 1 corresponds to a *sentence B* token.
  
              [What are token type IDs?](../glossary#token-type-ids)
          modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
              Segment token indices to indicate different portions of the non-text modality. The embeddings from these
              tokens will be summed with the respective token embeddings for the non-text modality.
          position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
              config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
              Selected in the range `[0, config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      'The bare MMBT Model outputting raw hidden-states without any specific head on top.'  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module  ,   UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Any:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: str	     =transformer
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =ModalEmbeddings(snake_case_		,		snake_case_		,		transformer.embeddings							)
  
  
  
  
  				@add_start_docstrings_to_model_forward(snake_case_							)
  				@replace_return_docstrings(output_type=snake_case_		,		config_class=_CONFIG_FOR_DOC							)
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =output_attentions if output_attentions is not None else self.config.output_attentions
  											SCREAMING_SNAKE_CASE			: int	     =(
  											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  											)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											if input_ids is not None and inputs_embeds is not None:
  																		raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''							)
  											elif input_ids is not None:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.size()
  											elif inputs_embeds is not None:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =inputs_embeds.size()[:-1]
  											else:
  																		raise ValueError('''You have to specify either input_ids or inputs_embeds'''							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.device if input_ids is not None else inputs_embeds.device
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.modal_encoder(
  											    snake_case_		,		start_token=snake_case_		,		end_token=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =modal_embeddings.size()[:-1]
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.ones(snake_case_		,		dtype=torch.long		,		device=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.transformer.embeddings(
  											    input_ids=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		inputs_embeds=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =torch.cat([modal_embeddings, txt_embeddings]		,		1							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =embedding_output.size()[:-1]
  
  											if attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_		,		dtype=torch.long							), attention_mask]		,		dim=1							)
  											if encoder_attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_							), encoder_attention_mask]		,		dim=1							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_extended_attention_mask(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.invert_attention_mask(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.get_head_mask(snake_case_		,		self.config.num_hidden_layers							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.transformer.encoder(
  											    snake_case_		,		attention_mask=snake_case_		,		head_mask=snake_case_		,		encoder_hidden_states=snake_case_		,		encoder_attention_mask=snake_case_		,		output_attentions=snake_case_		,		output_hidden_states=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =encoder_outputs[0]
  											SCREAMING_SNAKE_CASE			: str	     =self.transformer.pooler(snake_case_							)
  
  											if not return_dict:
  																		return (sequence_output, pooled_output) + encoder_outputs[1:]
  
  											return BaseModelOutputWithPooling(
  											    last_hidden_state=snake_case_		,		pooler_output=snake_case_		,		hidden_states=encoder_outputs.hidden_states		,		attentions=encoder_outputs.attentions		,		)
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											return self.embeddings.word_embeddings
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: str	     =value
  
  
  
  
  
  
  
  @add_start_docstrings(
      '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    '  ,   UpperCamelCase__  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      int:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: Dict	     =config.num_labels
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =MMBTModel(snake_case_		,		snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: int	     =nn.Dropout(config.hidden_dropout_prob							)
  											SCREAMING_SNAKE_CASE			: Dict	     =nn.Linear(config.hidden_size		,		config.num_labels							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Dict	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.mmbt(
  											    input_modal=snake_case_		,		input_ids=snake_case_		,		modal_start_tokens=snake_case_		,		modal_end_tokens=snake_case_		,		attention_mask=snake_case_		,		token_type_ids=snake_case_		,		modal_token_type_ids=snake_case_		,		position_ids=snake_case_		,		modal_position_ids=snake_case_		,		head_mask=snake_case_		,		inputs_embeds=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =outputs[1]
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.dropout(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.classifier(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =None
  											if labels is not None:
  																		if self.num_labels == 1:
  																									#  We are doing regression
  																									SCREAMING_SNAKE_CASE			: List[Any]	     =MSELoss()
  																									SCREAMING_SNAKE_CASE			: int	     =loss_fct(logits.view(-1							)		,		labels.view(-1							)							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: List[str]	     =CrossEntropyLoss()
  																									SCREAMING_SNAKE_CASE			: Dict	     =loss_fct(logits.view(-1		,		self.num_labels							)		,		labels.view(-1							)							)
  
  											if not return_dict:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =(logits,) + outputs[2:]
  																		return ((loss,) + output) if loss is not None else output
  
  											return SequenceClassifierOutput(
  											    loss=snake_case_		,		logits=snake_case_		,		hidden_states=outputs.hidden_states		,		attentions=outputs.attentions		,		)
  
  
  
  
  
  
  