code: 
  
  
  
  '''simple docstring'''
  
  
  
  import collections
  import inspect
  import unittest
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_backbone_common import BackboneTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  	from torch import nn
  
  	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
  	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  	from PIL import Image
  
  	from transformers import AutoImageProcessor
  class        UpperCAmelCase__  :
  
  
  
  	def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=3_2		,	lowercase=2		,	lowercase=3		,	lowercase=1_6		,	lowercase=[1, 2, 1]		,	lowercase=[2, 2, 4]		,	lowercase=2		,	lowercase=2.0		,	lowercase=True		,	lowercase=0.0		,	lowercase=0.0		,	lowercase=0.1		,	lowercase="gelu"		,	lowercase=False		,	lowercase=True		,	lowercase=0.02		,	lowercase=1E-5		,	lowercase=True		,	lowercase=None		,	lowercase=True		,	lowercase=1_0		,	lowercase=8		,	lowercase=["stage1", "stage2"]		,	lowercase=[1, 2]		,	)  ->							str:
  		__UpperCamelCase				      =  parent
  		__UpperCamelCase				      =  batch_size
  		__UpperCamelCase				      =  image_size
  		__UpperCamelCase				      =  patch_size
  		__UpperCamelCase				      =  num_channels
  		__UpperCamelCase				      =  embed_dim
  		__UpperCamelCase				      =  depths
  		__UpperCamelCase				      =  num_heads
  		__UpperCamelCase				      =  window_size
  		__UpperCamelCase				      =  mlp_ratio
  		__UpperCamelCase				      =  qkv_bias
  		__UpperCamelCase				      =  hidden_dropout_prob
  		__UpperCamelCase				      =  attention_probs_dropout_prob
  		__UpperCamelCase				      =  drop_path_rate
  		__UpperCamelCase				      =  hidden_act
  		__UpperCamelCase				      =  use_absolute_embeddings
  		__UpperCamelCase				      =  patch_norm
  		__UpperCamelCase				      =  layer_norm_eps
  		__UpperCamelCase				      =  initializer_range
  		__UpperCamelCase				      =  is_training
  		__UpperCamelCase				      =  scope
  		__UpperCamelCase				      =  use_labels
  		__UpperCamelCase				      =  type_sequence_label_size
  		__UpperCamelCase				      =  encoder_stride
  		__UpperCamelCase				      =  out_features
  		__UpperCamelCase				      =  out_indices
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  		__UpperCamelCase				      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  
  		__UpperCamelCase				      =  None
  		if self.use_labels:
  			__UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.type_sequence_label_size			)
  
  		__UpperCamelCase				      =  self.get_config()
  
  		return config, pixel_values, labels
  
  
  
  	def    __lowerCamelCase				(  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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Tuple:
  		__UpperCamelCase				      =  SwinModel(config=lowercase			)
  		model.to(lowercase			)
  		model.eval()
  		__UpperCamelCase				      =  model(lowercase			)
  
  		__UpperCamelCase				      =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths			) - 1))
  		__UpperCamelCase				      =  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		,	lowercase		,	lowercase		,	lowercase			)  ->							Tuple:
  		__UpperCamelCase				      =  SwinBackbone(config=lowercase			)
  		model.to(lowercase			)
  		model.eval()
  		__UpperCamelCase				      =  model(lowercase			)
  
  		# 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], 1_6, 1_6]			)
  
  		# verify channels
  		self.parent.assertEqual(len(model.channels			)		,	len(config.out_features			)			)
  
  		# verify backbone works with out_features=None
  		__UpperCamelCase				      =  None
  		__UpperCamelCase				      =  SwinBackbone(config=lowercase			)
  		model.to(lowercase			)
  		model.eval()
  		__UpperCamelCase				      =  model(lowercase			)
  
  		# verify feature maps
  		self.parent.assertEqual(len(result.feature_maps			)		,	1			)
  		self.parent.assertListEqual(list(result.feature_maps[0].shape			)		,	[self.batch_size, model.channels[-1], 4, 4]			)
  
  		# verify channels
  		self.parent.assertEqual(len(model.channels			)		,	1			)
  
  
  
  	def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							List[str]:
  		__UpperCamelCase				      =  SwinForMaskedImageModeling(config=lowercase			)
  		model.to(lowercase			)
  		model.eval()
  		__UpperCamelCase				      =  model(lowercase			)
  		self.parent.assertEqual(
  		    result.logits.shape		,	(self.batch_size, self.num_channels, self.image_size, self.image_size)			)
  
  		# test greyscale images
  		__UpperCamelCase				      =  1
  		__UpperCamelCase				      =  SwinForMaskedImageModeling(lowercase			)
  		model.to(lowercase			)
  		model.eval()
  
  		__UpperCamelCase				      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
  		__UpperCamelCase				      =  model(lowercase			)
  		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, 1, self.image_size, self.image_size)			)
  
  
  
  	def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Any:
  		__UpperCamelCase				      =  self.type_sequence_label_size
  		__UpperCamelCase				      =  SwinForImageClassification(lowercase			)
  		model.to(lowercase			)
  		model.eval()
  		__UpperCamelCase				      =  model(lowercase		,	labels=lowercase			)
  		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)			)
  
  		# test greyscale images
  		__UpperCamelCase				      =  1
  		__UpperCamelCase				      =  SwinForImageClassification(lowercase			)
  		model.to(lowercase			)
  		model.eval()
  
  		__UpperCamelCase				      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
  		__UpperCamelCase				      =  model(lowercase			)
  		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)			)
  
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Any:
  		__UpperCamelCase				      =  self.prepare_config_and_inputs()
  		(
  		    (
  		    __UpperCamelCase		
  		)      ,		(
  		    __UpperCamelCase		
  		)      ,		(
  		    __UpperCamelCase		
  		)      ,				
  		)				      =  config_and_inputs
  		__UpperCamelCase				      =  {"""pixel_values""": pixel_values}
  		return config, inputs_dict
  
  
  @require_torch
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
  	__SCREAMING_SNAKE_CASE					      =				(
  	    (
  	        SwinModel,
  	        SwinBackbone,
  	        SwinForImageClassification,
  	        SwinForMaskedImageModeling,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	__SCREAMING_SNAKE_CASE					      =				(
  	    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
  	    if is_torch_available()
  	    else {}
  	)
  	__SCREAMING_SNAKE_CASE					      =				True
  
  	__SCREAMING_SNAKE_CASE					      =				False
  	__SCREAMING_SNAKE_CASE					      =				False
  	__SCREAMING_SNAKE_CASE					      =				False
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							List[str]:
  		__UpperCamelCase				      =  SwinModelTester(self			)
  		__UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase		,	embed_dim=3_7			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							List[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    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  		return
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_model(*lowercase			)
  
  
  
  	@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""			)
  	def    __lowerCamelCase				(  self			)  ->							str:
  		pass
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							List[Any]:
  		super().test_training_gradient_checkpointing()
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_backbone(*lowercase			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_masked_image_modeling(*lowercase			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_image_classification(*lowercase			)
  
  
  
  	@unittest.skip(reason="""Swin does not use inputs_embeds"""			)
  	def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  		pass
  
  
  
  	@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""			)
  	def    __lowerCamelCase				(  self			)  ->							Tuple:
  		pass
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
  		for model_class in self.all_model_classes:
  			__UpperCamelCase				      =  model_class(lowercase			)
  			self.assertIsInstance(model.get_input_embeddings()		,	(nn.Module)			)
  			__UpperCamelCase				      =  model.get_output_embeddings()
  			self.assertTrue(x is None or isinstance(lowercase		,	nn.Linear			)			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Tuple:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
  		for model_class in self.all_model_classes:
  			__UpperCamelCase				      =  model_class(lowercase			)
  			__UpperCamelCase				      =  inspect.signature(model.forward			)
  			# signature.parameters is an OrderedDict => so arg_names order is deterministic
  			__UpperCamelCase				      =  [*signature.parameters.keys()]
  
  			__UpperCamelCase				      =  ["""pixel_values"""]
  			self.assertListEqual(arg_names[:1]		,	lowercase			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Tuple:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  		__UpperCamelCase				      =  True
  
  		for model_class in self.all_model_classes:
  			__UpperCamelCase				      =  True
  			__UpperCamelCase				      =  False
  			__UpperCamelCase				      =  True
  			__UpperCamelCase				      =  model_class(lowercase			)
  			model.to(lowercase			)
  			model.eval()
  			with torch.no_grad():
  				__UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)
  			__UpperCamelCase				      =  outputs.attentions
  			__UpperCamelCase				      =  len(self.model_tester.depths			)
  			self.assertEqual(len(lowercase			)		,	lowercase			)
  
  			# check that output_attentions also work using config
  			del inputs_dict["output_attentions"]
  			__UpperCamelCase				      =  True
  			__UpperCamelCase				      =  config.window_size**2
  			__UpperCamelCase				      =  model_class(lowercase			)
  			model.to(lowercase			)
  			model.eval()
  			with torch.no_grad():
  				__UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)
  			__UpperCamelCase				      =  outputs.attentions
  			self.assertEqual(len(lowercase			)		,	lowercase			)
  
  			self.assertListEqual(
  			    list(attentions[0].shape[-3:]			)		,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,	)
  			__UpperCamelCase				      =  len(lowercase			)
  
  			# Check attention is always last and order is fine
  			__UpperCamelCase				      =  True
  			__UpperCamelCase				      =  True
  			__UpperCamelCase				      =  model_class(lowercase			)
  			model.to(lowercase			)
  			model.eval()
  			with torch.no_grad():
  				__UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)
  
  			# also another +1 for reshaped_hidden_states
  			__UpperCamelCase				      =  1 if model_class.__name__ == """SwinBackbone""" else 2
  			self.assertEqual(out_len + added_hidden_states		,	len(lowercase			)			)
  
  			__UpperCamelCase				      =  outputs.attentions
  
  			self.assertEqual(len(lowercase			)		,	lowercase			)
  
  			self.assertListEqual(
  			    list(self_attentions[0].shape[-3:]			)		,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,	)
  
  
  
  	def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Union[str, Any]:
  		__UpperCamelCase				      =  model_class(lowercase			)
  		model.to(lowercase			)
  		model.eval()
  
  		with torch.no_grad():
  			__UpperCamelCase				      =  model(**self._prepare_for_class(lowercase		,	lowercase			)			)
  
  		__UpperCamelCase				      =  outputs.hidden_states
  
  		__UpperCamelCase				      =  getattr(
  		    self.model_tester		,	"""expected_num_hidden_layers"""		,	len(self.model_tester.depths			) + 1			)
  		self.assertEqual(len(lowercase			)		,	lowercase			)
  
  		# Swin has a different seq_length
  		__UpperCamelCase				      =  (
  		    config.patch_size
  		    if isinstance(config.patch_size		,	collections.abc.Iterable			)
  		    else (config.patch_size, config.patch_size)
  		)
  
  		__UpperCamelCase				      =  (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":
  			__UpperCamelCase				      =  outputs.reshaped_hidden_states
  			self.assertEqual(len(lowercase			)		,	lowercase			)
  
  			__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  reshaped_hidden_states[0].shape
  			__UpperCamelCase				      =  (
  			    reshaped_hidden_states[0].view(lowercase		,	lowercase		,	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[int]:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
  		__UpperCamelCase				      =  (
  		    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:
  			__UpperCamelCase				      =  True
  			self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
  			# check that output_hidden_states also work using config
  			del inputs_dict["output_hidden_states"]
  			__UpperCamelCase				      =  True
  
  			self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	lowercase			)
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  		__UpperCamelCase				      =  3
  
  		__UpperCamelCase				      =  (
  		    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				      =  (
  		    config.patch_size
  		    if isinstance(config.patch_size		,	collections.abc.Iterable			)
  		    else (config.patch_size, config.patch_size)
  		)
  
  		__UpperCamelCase				      =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  		__UpperCamelCase				      =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  		for model_class in self.all_model_classes:
  			__UpperCamelCase				      =  True
  			self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	(padded_height, padded_width)			)
  
  			# check that output_hidden_states also work using config
  			del inputs_dict["output_hidden_states"]
  			__UpperCamelCase				      =  True
  			self.check_hidden_states_output(lowercase		,	lowercase		,	lowercase		,	(padded_height, padded_width)			)
  
  
  
  	@slow
  	def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  		for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  			__UpperCamelCase				      =  SwinModel.from_pretrained(lowercase			)
  			self.assertIsNotNone(lowercase			)
  
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							Any:
  		__UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
  		__UpperCamelCase				      =  _config_zero_init(lowercase			)
  		for model_class in self.all_model_classes:
  			__UpperCamelCase				      =  model_class(config=lowercase			)
  			for name, param in model.named_parameters():
  				if "embeddings" not in name and param.requires_grad:
  					self.assertIn(
  					    ((param.data.mean() * 1E9).round() / 1E9).item()		,	[0.0, 1.0]		,	msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,	)
  
  
  @require_vision
  @require_torch
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
  	@cached_property
  	def    __lowerCamelCase				(  self			)  ->							int:
  		return (
  		    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""			)
  		    if is_vision_available()
  		    else None
  		)
  
  
  
  
  	@slow
  	def    __lowerCamelCase				(  self			)  ->							Dict:
  		__UpperCamelCase				      =  SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""			).to(lowercase			)
  		__UpperCamelCase				      =  self.default_image_processor
  
  		__UpperCamelCase				      =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  		__UpperCamelCase				      =  image_processor(images=lowercase		,	return_tensors="""pt"""			).to(lowercase			)
  
  		# forward pass
  		with torch.no_grad():
  			__UpperCamelCase				      =  model(**lowercase			)
  
  		# verify the logits
  		__UpperCamelCase				      =  torch.Size((1, 1_0_0_0)			)
  		self.assertEqual(outputs.logits.shape		,	lowercase			)
  		__UpperCamelCase				      =  torch.tensor([-0.0_948, -0.6_454, -0.0_921]			).to(lowercase			)
  		self.assertTrue(torch.allclose(outputs.logits[0, :3]		,	lowercase		,	atol=1E-4			)			)
  
  
  @require_torch
  class        UpperCAmelCase__  (    unittest.TestCase							,			UpperCAmelCase_):
  	__SCREAMING_SNAKE_CASE					      =				(SwinBackbone,) if is_torch_available() else ()
  	__SCREAMING_SNAKE_CASE					      =				SwinConfig
  
  
  
  	def    __lowerCamelCase				(  self			)  ->							str:
  		__UpperCamelCase				      =  SwinModelTester(self			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     res *= a
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     __UpperCamelCase				      =  ((res % c) * (a % c)) % c
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  