


import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
				from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import SwinaSRImageProcessor







class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=3_2       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=3       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=[1, 2, 1]       ,		lowerCAmelCase__=[2, 2, 4]       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=2.0       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.0       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=0.02       ,		lowerCAmelCase__=1E-5       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=2       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= parent
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= batch_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= image_size
										SCREAMING_SNAKE_CASE_						:			int       						= patch_size
										SCREAMING_SNAKE_CASE_						:			str       						= num_channels
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			int       						= depths
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= num_heads
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= window_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= mlp_ratio
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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_						:			Dict       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= use_absolute_embeddings
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= patch_norm
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			str       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= is_training
										SCREAMING_SNAKE_CASE_						:			Dict       						= scope
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= use_labels
										SCREAMING_SNAKE_CASE_						:			Dict       						= upscale

										# here we set some attributes to make tests pass
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= len(A__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			Any       						= (image_size // patch_size) ** 2






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

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

										return config, pixel_values, labels






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return SwinaSRConfig(
										    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       ,		upscale=self.upscale       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= SwinaSRModel(config=A__    )
										model.to(A__    )
										model.eval()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(A__    )

										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,		(self.batch_size, self.embed_dim, self.image_size, self.image_size)    )






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= SwinaSRForImageSuperResolution(A__    )
										model.to(A__    )
										model.eval()
										SCREAMING_SNAKE_CASE_						:			Any       						= model(A__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self.image_size * self.upscale
										self.parent.assertEqual(
										    result.reconstruction.shape       ,		(self.batch_size, self.num_channels, expected_image_size, expected_image_size)    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




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







@require_torch
class      __lowercase  (_lowerCamelCase     ,    _lowerCamelCase     ,    unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
							_UpperCAmelCase     =		{"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False
							_UpperCAmelCase     =		False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= SwinaSRModelTester(self    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= ConfigTester(self       ,		config_class=A__       ,		embed_dim=3_7    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_super_resolution(*A__    )






							@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							@unittest.skip(reason='Swin2SR does not use inputs_embeds'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							@unittest.skip(reason='Swin2SR does not support training yet'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							@unittest.skip(reason='Swin2SR does not support training yet'    )
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										pass






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= self.model_tester.prepare_config_and_inputs_for_common()

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






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			str       						= self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE_						:			Dict       						= ['pixel_values']
													self.assertListEqual(arg_names[:1]       ,		A__    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_						:			Dict       						= SwinaSRModel.from_pretrained(A__    )
													self.assertIsNotNone(A__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Dict       						= self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= _config_zero_init(A__    )
										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(config=A__    )
													for name, param in model.named_parameters():
																if "logit_scale" in name:
																			continue
																if 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'''       ,		)






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= True

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= True
													SCREAMING_SNAKE_CASE_						:			Dict       						= False
													SCREAMING_SNAKE_CASE_						:			Any       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model_class(A__    )
													model.to(A__    )
													model.eval()
													with torch.no_grad():
																SCREAMING_SNAKE_CASE_						:			str       						= model(**self._prepare_for_class(A__       ,		A__    )    )
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= outputs.attentions
													SCREAMING_SNAKE_CASE_						:			int       						= len(self.model_tester.depths    )
													self.assertEqual(len(A__    )       ,		A__    )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													SCREAMING_SNAKE_CASE_						:			Any       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= config.window_size**2
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= model_class(A__    )
													model.to(A__    )
													model.eval()
													with torch.no_grad():
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model(**self._prepare_for_class(A__       ,		A__    )    )
													SCREAMING_SNAKE_CASE_						:			Any       						= outputs.attentions
													self.assertEqual(len(A__    )       ,		A__    )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)
													SCREAMING_SNAKE_CASE_						:			Tuple       						= len(A__    )

													# Check attention is always last and order is fine
													SCREAMING_SNAKE_CASE_						:			Tuple       						= True
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= True
													SCREAMING_SNAKE_CASE_						:			Any       						= model_class(A__    )
													model.to(A__    )
													model.eval()
													with torch.no_grad():
																SCREAMING_SNAKE_CASE_						:			List[str]       						= model(**self._prepare_for_class(A__       ,		A__    )    )

													self.assertEqual(out_len + 1       ,		len(A__    )    )

													SCREAMING_SNAKE_CASE_						:			Tuple       						= outputs.attentions

													self.assertEqual(len(A__    )       ,		A__    )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]    )       ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,		)







@require_vision
@require_torch
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= SwinaSRImageProcessor()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= SwinaSRForImageSuperResolution.from_pretrained('caidas/swin2SR-classical-sr-x2-64'    ).to(A__    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
										SCREAMING_SNAKE_CASE_						:			str       						= processor(images=A__       ,		return_tensors='pt'    ).to(A__    )

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model(**A__    )

										# verify the logits
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.Size([1, 3, 9_7_6, 1_2_9_6]    )
										self.assertEqual(outputs.reconstruction.shape       ,		A__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= torch.tensor(
										    [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]    ).to(A__    )
										self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,		A__       ,		atol=1E-4    )    )







'''simple docstring'''



import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
     from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import SwinaSRImageProcessor

class       _a :





       """simple docstring"""




       def __init__(							self				,							A__				,							A__=13				,							A__=32				,							A__=1				,							A__=3				,							A__=16				,							A__=[1, 2, 1]				,							A__=[2, 2, 4]				,							A__=2				,							A__=2.0				,							A__=True				,							A__=0.0				,							A__=0.0				,							A__=0.1				,							A__="gelu"				,							A__=False				,							A__=True				,							A__=0.02				,							A__=1E-5				,							A__=True				,							A__=None				,							A__=False				,							A__=2				,							)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = parent
          _SCREAMING_SNAKE_CASE          = batch_size
          _SCREAMING_SNAKE_CASE          = image_size
          _SCREAMING_SNAKE_CASE          = patch_size
          _SCREAMING_SNAKE_CASE          = num_channels
          _SCREAMING_SNAKE_CASE          = embed_dim
          _SCREAMING_SNAKE_CASE          = depths
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = window_size
          _SCREAMING_SNAKE_CASE          = mlp_ratio
          _SCREAMING_SNAKE_CASE          = qkv_bias
          _SCREAMING_SNAKE_CASE          = hidden_dropout_prob
          _SCREAMING_SNAKE_CASE          = attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE          = drop_path_rate
          _SCREAMING_SNAKE_CASE          = hidden_act
          _SCREAMING_SNAKE_CASE          = use_absolute_embeddings
          _SCREAMING_SNAKE_CASE          = patch_norm
          _SCREAMING_SNAKE_CASE          = layer_norm_eps
          _SCREAMING_SNAKE_CASE          = initializer_range
          _SCREAMING_SNAKE_CASE          = is_training
          _SCREAMING_SNAKE_CASE          = scope
          _SCREAMING_SNAKE_CASE          = use_labels
          _SCREAMING_SNAKE_CASE          = upscale

          # here we set some attributes to make tests pass
          _SCREAMING_SNAKE_CASE          = len(A__					)
          _SCREAMING_SNAKE_CASE          = embed_dim
          _SCREAMING_SNAKE_CASE          = (image_size // patch_size) ** 2






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE          = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

          _SCREAMING_SNAKE_CASE          = self.get_config()

          return config, pixel_values, labels






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return SwinaSRConfig(
              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				,							upscale=self.upscale				,							)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = SwinaSRModel(config=A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__					)

          self.parent.assertEqual(
              result.last_hidden_state.shape				,							(self.batch_size, self.embed_dim, self.image_size, self.image_size)					)






       def 				UpperCamelCase (							self				,							A__				,							A__				,							A__					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = SwinaSRForImageSuperResolution(A__					)
          model.to(A__					)
          model.eval()
          _SCREAMING_SNAKE_CASE          = model(A__					)

          _SCREAMING_SNAKE_CASE          = self.image_size * self.upscale
          self.parent.assertEqual(
              result.reconstruction.shape				,							(self.batch_size, self.num_channels, expected_image_size, expected_image_size)					)






       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          _SCREAMING_SNAKE_CASE          = self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = config_and_inputs
          _SCREAMING_SNAKE_CASE          = {"""pixel_values""": pixel_values}
          return config, inputs_dict






@require_torch
class       _a (_lowerCamelCase     ,    _lowerCamelCase     ,    unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     (SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE					=     {'feature-extraction': SwinaSRModel} if is_torch_available() else {}

       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False
       SCREAMING_SNAKE_CASE					=     False






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = SwinaSRModelTester(self					)
          _SCREAMING_SNAKE_CASE          = ConfigTester(self				,							config_class=A__				,							embed_dim=37					)






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          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 				UpperCamelCase (							self					)			->	int:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__					)






       def 				UpperCamelCase (							self					)			->	List[Any]:
          _SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_super_resolution(*A__					)






       @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
       def 				UpperCamelCase (							self					)			->	List[Any]:
          pass






       @unittest.skip(reason="""Swin2SR does not use inputs_embeds"""					)
       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          pass






       @unittest.skip(reason="""Swin2SR does not support training yet"""					)
       def 				UpperCamelCase (							self					)			->	Any:
          pass






       @unittest.skip(reason="""Swin2SR does not support training yet"""					)
       def 				UpperCamelCase (							self					)			->	Tuple:
          pass






       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE          = model_class(A__					)
             self.assertIsInstance(model.get_input_embeddings()				,							(nn.Module)					)
             _SCREAMING_SNAKE_CASE          = model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A__				,							nn.Linear					)					)






       def 				UpperCamelCase (							self					)			->	Union[str, Any]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE          = model_class(A__					)
             _SCREAMING_SNAKE_CASE          = inspect.signature(model.forward					)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _SCREAMING_SNAKE_CASE          = [*signature.parameters.keys()]

             _SCREAMING_SNAKE_CASE          = ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]				,							A__					)






       @slow
       def 				UpperCamelCase (							self					)			->	Optional[int]:
          for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE          = SwinaSRModel.from_pretrained(A__					)
             self.assertIsNotNone(A__					)






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()

          _SCREAMING_SNAKE_CASE          = _config_zero_init(A__					)
          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE          = model_class(config=A__					)
             for name, param in model.named_parameters():
                if "logit_scale" in name:
                   continue
                if 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"				,							)






       def 				UpperCamelCase (							self					)			->	Any:
          _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE          = True

          for model_class in self.all_model_classes:
             _SCREAMING_SNAKE_CASE          = True
             _SCREAMING_SNAKE_CASE          = False
             _SCREAMING_SNAKE_CASE          = True
             _SCREAMING_SNAKE_CASE          = model_class(A__					)
             model.to(A__					)
             model.eval()
             with torch.no_grad():
                _SCREAMING_SNAKE_CASE          = model(**self._prepare_for_class(A__				,							A__					)					)
             _SCREAMING_SNAKE_CASE          = outputs.attentions
             _SCREAMING_SNAKE_CASE          = len(self.model_tester.depths					)
             self.assertEqual(len(A__					)				,							A__					)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             _SCREAMING_SNAKE_CASE          = True
             _SCREAMING_SNAKE_CASE          = config.window_size**2
             _SCREAMING_SNAKE_CASE          = model_class(A__					)
             model.to(A__					)
             model.eval()
             with torch.no_grad():
                _SCREAMING_SNAKE_CASE          = model(**self._prepare_for_class(A__				,							A__					)					)
             _SCREAMING_SNAKE_CASE          = outputs.attentions
             self.assertEqual(len(A__					)				,							A__					)

             self.assertListEqual(
                 list(attentions[0].shape[-3:]					)				,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,							)
             _SCREAMING_SNAKE_CASE          = len(A__					)

             # Check attention is always last and order is fine
             _SCREAMING_SNAKE_CASE          = True
             _SCREAMING_SNAKE_CASE          = True
             _SCREAMING_SNAKE_CASE          = model_class(A__					)
             model.to(A__					)
             model.eval()
             with torch.no_grad():
                _SCREAMING_SNAKE_CASE          = model(**self._prepare_for_class(A__				,							A__					)					)

             self.assertEqual(out_len + 1				,							len(A__					)					)

             _SCREAMING_SNAKE_CASE          = outputs.attentions

             self.assertEqual(len(A__					)				,							A__					)

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]					)				,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,							)






@require_vision
@require_torch
@slow
class       _a (unittest.TestCase):





       """simple docstring"""




       def 				UpperCamelCase (							self					)			->	Dict:
          _SCREAMING_SNAKE_CASE          = SwinaSRImageProcessor()
          _SCREAMING_SNAKE_CASE          = SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64"""					).to(A__					)

          _SCREAMING_SNAKE_CASE          = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
          _SCREAMING_SNAKE_CASE          = processor(images=A__				,							return_tensors="""pt"""					).to(A__					)

          # forward pass
          with torch.no_grad():
             _SCREAMING_SNAKE_CASE          = model(**A__					)

          # verify the logits
          _SCREAMING_SNAKE_CASE          = torch.Size([1, 3, 9_76, 12_96]					)
          self.assertEqual(outputs.reconstruction.shape				,							A__					)
          _SCREAMING_SNAKE_CASE          = torch.tensor(
              [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]					).to(A__					)
          self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]				,							A__				,							atol=1E-4					)					)






