



'''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			)










'''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





