


'''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__    :



     """simple docstring"""






     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]					,		):

            '''simple docstring'''

            _lowerCAmelCase			:							List[str]      	=				parent
            _lowerCAmelCase			:							str      	=				batch_size
            _lowerCAmelCase			:							Any      	=				image_size
            _lowerCAmelCase			:							List[Any]      	=				patch_size
            _lowerCAmelCase			:							List[Any]      	=				num_channels
            _lowerCAmelCase			:							List[str]      	=				embed_dim
            _lowerCAmelCase			:							Optional[int]      	=				depths
            _lowerCAmelCase			:							Dict      	=				num_heads
            _lowerCAmelCase			:							List[Any]      	=				window_size
            _lowerCAmelCase			:							Any      	=				mlp_ratio
            _lowerCAmelCase			:							int      	=				qkv_bias
            _lowerCAmelCase			:							int      	=				hidden_dropout_prob
            _lowerCAmelCase			:							Tuple      	=				attention_probs_dropout_prob
            _lowerCAmelCase			:							Tuple      	=				drop_path_rate
            _lowerCAmelCase			:							List[Any]      	=				hidden_act
            _lowerCAmelCase			:							Optional[int]      	=				use_absolute_embeddings
            _lowerCAmelCase			:							Any      	=				patch_norm
            _lowerCAmelCase			:							Any      	=				layer_norm_eps
            _lowerCAmelCase			:							Optional[Any]      	=				initializer_range
            _lowerCAmelCase			:							Any      	=				is_training
            _lowerCAmelCase			:							List[Any]      	=				scope
            _lowerCAmelCase			:							List[Any]      	=				use_labels
            _lowerCAmelCase			:							Optional[Any]      	=				type_sequence_label_size
            _lowerCAmelCase			:							Tuple      	=				encoder_stride
            _lowerCAmelCase			:							Any      	=				out_features
            _lowerCAmelCase			:							Optional[int]      	=				out_indices







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							int      	=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

            _lowerCAmelCase			:							Optional[Any]      	=				self.get_config()

            return config, pixel_values, labels







     def a		(       self      ):

            '''simple docstring'''

            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__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							int      	=				SwinModel(config=_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()
            _lowerCAmelCase			:							str      	=				model(_SCREAMING_SNAKE_CASE      )

            _lowerCAmelCase			:							Tuple      	=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
            _lowerCAmelCase			:							str      	=				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__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Dict      	=				SwinBackbone(config=_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()
            _lowerCAmelCase			:							str      	=				model(_SCREAMING_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
            _lowerCAmelCase			:							Dict      	=				None
            _lowerCAmelCase			:							List[str]      	=				SwinBackbone(config=_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()
            _lowerCAmelCase			:							Any      	=				model(_SCREAMING_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__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							List[str]      	=				SwinForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()
            _lowerCAmelCase			:							Tuple      	=				model(_SCREAMING_SNAKE_CASE      )
            self.parent.assertEqual(
                result.logits.shape					,		(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

            # test greyscale images
            _lowerCAmelCase			:							Optional[Any]      	=				1
            _lowerCAmelCase			:							List[Any]      	=				SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()

            _lowerCAmelCase			:							int      	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            _lowerCAmelCase			:							Dict      	=				model(_SCREAMING_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__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Tuple      	=				self.type_sequence_label_size
            _lowerCAmelCase			:							str      	=				SwinForImageClassification(_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()
            _lowerCAmelCase			:							List[Any]      	=				model(_SCREAMING_SNAKE_CASE					,		labels=_SCREAMING_SNAKE_CASE      )
            self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.type_sequence_label_size)      )

            # test greyscale images
            _lowerCAmelCase			:							Tuple      	=				1
            _lowerCAmelCase			:							Dict      	=				SwinForImageClassification(_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()

            _lowerCAmelCase			:							Any      	=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            _lowerCAmelCase			:							int      	=				model(_SCREAMING_SNAKE_CASE      )
            self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.type_sequence_label_size)      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							str      	=				self.prepare_config_and_inputs()
            (
                _lowerCAmelCase				
            )			:							str      	=				config_and_inputs
            _lowerCAmelCase			:							Any      	=				{"pixel_values": pixel_values}
            return config, inputs_dict
@require_torch
class 		UpperCamelCase__    (       SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			,    unittest.TestCase		):



     """simple docstring"""






     __magic_name__				=			(
         (
             SwinModel,
             SwinBackbone,
             SwinForImageClassification,
             SwinForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__				=			(
         {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
         if is_torch_available()
         else {}
     )
     __magic_name__				=			True

     __magic_name__				=			False
     __magic_name__				=			False
     __magic_name__				=			False







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Union[str, Any]      	=				SwinModelTester(self      )
            _lowerCAmelCase			:							List[Any]      	=				ConfigTester(self					,		config_class=_SCREAMING_SNAKE_CASE					,		embed_dim=37      )







     def a		(       self      ):

            '''simple docstring'''

            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      ):

            '''simple docstring'''

            return







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Dict      	=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE      )







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

            '''simple docstring'''

            pass







     def a		(       self      ):

            '''simple docstring'''

            super().test_training_gradient_checkpointing()







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Union[str, Any]      	=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							str      	=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Optional[int]      	=				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE      )







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

            '''simple docstring'''

            pass







     @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
     def a		(       self      ):

            '''simple docstring'''

            pass







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							str      	=				self.model_tester.prepare_config_and_inputs_for_common()

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







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							List[Any]      	=				self.model_tester.prepare_config_and_inputs_for_common()

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

                   _lowerCAmelCase			:							Union[str, Any]      	=				["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,		_SCREAMING_SNAKE_CASE      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Any      	=				self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase			:							int      	=				True

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

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

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

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

                   # also another +1 for reshaped_hidden_states
                   _lowerCAmelCase			:							Union[str, Any]      	=				1 if model_class.__name__ == "SwinBackbone" else 2
                   self.assertEqual(out_len + added_hidden_states					,		len(_SCREAMING_SNAKE_CASE      )      )

                   _lowerCAmelCase			:							Union[str, Any]      	=				outputs.attentions

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE      )					,		_SCREAMING_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__      ):

            '''simple docstring'''

            _lowerCAmelCase			:							List[str]      	=				model_class(_SCREAMING_SNAKE_CASE      )
            model.to(_SCREAMING_SNAKE_CASE      )
            model.eval()

            with torch.no_grad():
                   _lowerCAmelCase			:							Optional[Any]      	=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )      )

            _lowerCAmelCase			:							List[Any]      	=				outputs.hidden_states

            _lowerCAmelCase			:							List[Any]      	=				getattr(
                self.model_tester					,		'expected_num_hidden_layers'					,		len(self.model_tester.depths      ) + 1      )
            self.assertEqual(len(_SCREAMING_SNAKE_CASE      )					,		_SCREAMING_SNAKE_CASE      )

            # Swin has a different seq_length
            _lowerCAmelCase			:							Tuple      	=				(
                config.patch_size
                if isinstance(config.patch_size					,		collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            _lowerCAmelCase			:							Optional[int]      	=				(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":
                   _lowerCAmelCase			:							str      	=				outputs.reshaped_hidden_states
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE      )					,		_SCREAMING_SNAKE_CASE      )

                   _lowerCAmelCase			:							Dict      	=				reshaped_hidden_states[0].shape
                   _lowerCAmelCase			:							Optional[int]      	=				(
                       reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE					,		_SCREAMING_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      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Optional[Any]      	=				self.model_tester.prepare_config_and_inputs_for_common()

            _lowerCAmelCase			:							str      	=				(
                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:
                   _lowerCAmelCase			:							Optional[int]      	=				True
                   self.check_hidden_states_output(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _lowerCAmelCase			:							List[Any]      	=				True

                   self.check_hidden_states_output(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Any      	=				self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase			:							int      	=				3

            _lowerCAmelCase			:							Tuple      	=				(
                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)
            )
            _lowerCAmelCase			:							Tuple      	=				(
                config.patch_size
                if isinstance(config.patch_size					,		collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            _lowerCAmelCase			:							Dict      	=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            _lowerCAmelCase			:							List[str]      	=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                   _lowerCAmelCase			:							List[str]      	=				True
                   self.check_hidden_states_output(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		(padded_height, padded_width)      )

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _lowerCAmelCase			:							int      	=				True
                   self.check_hidden_states_output(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		(padded_height, padded_width)      )







     @slow
     def a		(       self      ):

            '''simple docstring'''

            for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase			:							Optional[int]      	=				SwinModel.from_pretrained(_SCREAMING_SNAKE_CASE      )
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							int      	=				self.model_tester.prepare_config_and_inputs_for_common()

            _lowerCAmelCase			:							Optional[int]      	=				_config_zero_init(_SCREAMING_SNAKE_CASE      )
            for model_class in self.all_model_classes:
                   _lowerCAmelCase			:							Union[str, Any]      	=				model_class(config=_SCREAMING_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 		UpperCamelCase__    (       unittest.TestCase		):



     """simple docstring"""






     @cached_property
     def a		(       self      ):

            '''simple docstring'''

            return (
                AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
                if is_vision_available()
                else None
            )







     @slow
     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Tuple      	=				SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							Any      	=				self.default_image_processor

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

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

            # verify the logits
            _lowerCAmelCase			:							Any      	=				torch.Size((1, 1000)      )
            self.assertEqual(outputs.logits.shape					,		_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							Tuple      	=				torch.tensor([-0.0948, -0.6454, -0.0921]      ).to(_SCREAMING_SNAKE_CASE      )
            self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		_SCREAMING_SNAKE_CASE					,		atol=1E-4      )      )
@require_torch
class 		UpperCamelCase__    (       unittest.TestCase			,    SCREAMING_SNAKE_CASE__		):



     """simple docstring"""






     __magic_name__				=			(SwinBackbone,) if is_torch_available() else ()
     __magic_name__				=			SwinConfig







     def a		(       self      ):

            '''simple docstring'''

            _lowerCAmelCase			:							Dict      	=				SwinModelTester(self      )









from __future__ import annotations

def    lowerCAmelCase__		(			_a				:	list[int]     ,					_a				:	int			):
		snake_case_       ,				snake_case_ :							Tuple         =				0, len(_a			) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(1_00)), 40))
