

'''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      SCREAMING_SNAKE_CASE__      :




    def __init__( self    ,					lowercase__    ,					lowercase__=13    ,					lowercase__=32    ,					lowercase__=2    ,					lowercase__=3    ,					lowercase__=16    ,					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__=10    ,					lowercase__=8    ,					lowercase__=["stage1", "stage2"]    ,					lowercase__=[1, 2]    ,					):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.get_config()

          return config, pixel_values, labels




    def   __lowerCamelCase				( 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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	SwinModel(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      int					=	model(lowercase__      )

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






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	SwinBackbone(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	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], 16, 16]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )    ,					len(config.out_features      )      )

          # verify backbone works with out_features=None
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	None
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	SwinBackbone(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      int					=	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__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	SwinForMaskedImageModeling(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(lowercase__      )
          self.parent.assertEqual(
              result.logits.shape    ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

          # test greyscale images
          SCREAMING_SNAKE_CASE_				:      str					=	1
          SCREAMING_SNAKE_CASE_				:      Any					=	SwinForMaskedImageModeling(lowercase__      )
          model.to(lowercase__      )
          model.eval()

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	model(lowercase__      )
          self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, 1, self.image_size, self.image_size)      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.type_sequence_label_size
          SCREAMING_SNAKE_CASE_				:      int					=	SwinForImageClassification(lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model(lowercase__    ,					labels=lowercase__      )
          self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)      )

          # test greyscale images
          SCREAMING_SNAKE_CASE_				:      int					=	1
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	SwinForImageClassification(lowercase__      )
          model.to(lowercase__      )
          model.eval()

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
          SCREAMING_SNAKE_CASE_				:      int					=	model(lowercase__      )
          self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.type_sequence_label_size)      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.prepare_config_and_inputs()
          (
              (
              SCREAMING_SNAKE_CASE_
          ),						(
              SCREAMING_SNAKE_CASE_
          ),						(
              SCREAMING_SNAKE_CASE_
          ),						
          )				:      List[str]					=	config_and_inputs
          SCREAMING_SNAKE_CASE_				:      Tuple					=	{"pixel_values": pixel_values}
          return config, inputs_dict






@require_torch
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,_UpperCAmelCase,unittest.TestCase				):
    _A         =							(
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    _A         =							(
        {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    _A         =							True

    _A         =							False
    _A         =							False
    _A         =							False




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	SwinModelTester(self      )
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	ConfigTester(self    ,					config_class=lowercase__    ,					embed_dim=37      )




    def   __lowerCamelCase				( 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   __lowerCamelCase				( self      ):






          """simple docstring"""






          return




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	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      ):






          """simple docstring"""






          pass




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          super().test_training_gradient_checkpointing()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	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      ):






          """simple docstring"""






          pass




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






          """simple docstring"""






          pass




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.model_tester.prepare_config_and_inputs_for_common()

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

                SCREAMING_SNAKE_CASE_				:      Tuple					=	["pixel_values"]
                self.assertListEqual(arg_names[:1]    ,					lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      List[str]					=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_				:      Any					=	True

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

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

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

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

                SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	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__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	model_class(lowercase__      )
          model.to(lowercase__      )
          model.eval()

          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      int					=	model(**self._prepare_for_class(lowercase__    ,					lowercase__      )      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	outputs.hidden_states

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	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
          SCREAMING_SNAKE_CASE_				:      List[str]					=	(
              config.patch_size
              if isinstance(config.patch_size    ,					collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          SCREAMING_SNAKE_CASE_				:      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":
                SCREAMING_SNAKE_CASE_				:      Any					=	outputs.reshaped_hidden_states
                self.assertEqual(len(lowercase__      )    ,					lowercase__      )

                SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	reshaped_hidden_states[0].shape
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	(
                    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      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size    ,					collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	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"]
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	True

                self.check_hidden_states_output(lowercase__    ,					lowercase__    ,					lowercase__    ,					lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_				:      List[str]					=	3

          SCREAMING_SNAKE_CASE_				:      int					=	(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size    ,					collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	(
              config.patch_size
              if isinstance(config.patch_size    ,					collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          SCREAMING_SNAKE_CASE_				:      int					=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          SCREAMING_SNAKE_CASE_				:      Any					=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	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"]
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	True
                self.check_hidden_states_output(lowercase__    ,					lowercase__    ,					lowercase__    ,					(padded_height, padded_width)      )




    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE_				:      Any					=	SwinModel.from_pretrained(lowercase__      )
                self.assertIsNotNone(lowercase__      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_				:      Dict					=	_config_zero_init(lowercase__      )
          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	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      SCREAMING_SNAKE_CASE__      (    unittest.TestCase				):




    @cached_property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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







    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"      ).to(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.default_image_processor

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
          SCREAMING_SNAKE_CASE_				:      str					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      str					=	model(**lowercase__      )

          # verify the logits
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.Size((1, 1000)      )
          self.assertEqual(outputs.logits.shape    ,					lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Tuple					=	torch.tensor([-0.0948, -0.6454, -0.0921]      ).to(lowercase__      )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,					lowercase__    ,					atol=1e-4      )      )






@require_torch
class      SCREAMING_SNAKE_CASE__      (    unittest.TestCase,_UpperCAmelCase				):
    _A         =							(SwinBackbone,) if is_torch_available() else ()
    _A         =							SwinConfig




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	SwinModelTester(self      )




'''simple docstring'''






import requests
from bsa import BeautifulSoup


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     str   )							->       str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(
          "https://www.horoscope.com/us/horoscopes/general/"
          F"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
      )
      SCREAMING_SNAKE_CASE_				:      Dict					=	BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_   ).content  ,  "html.parser"   )
      return soup.find("div"  ,  class_="main-horoscope"   ).p.text


if __name__ == "__main__":
      print('Daily Horoscope. \n')
      print(
          'enter your Zodiac sign number:\n',
          '1. Aries\n',
          '2. Taurus\n',
          '3. Gemini\n',
          '4. Cancer\n',
          '5. Leo\n',
          '6. Virgo\n',
          '7. Libra\n',
          '8. Scorpio\n',
          '9. Sagittarius\n',
          '10. Capricorn\n',
          '11. Aquarius\n',
          '12. Pisces\n',
      )
      snake_case_					  =	int(input('number> ').strip())
      print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
      snake_case_					  =	input('enter the day> ')
      snake_case_					  =	horoscope(zodiac_sign, day)
      print(horoscope_text)

