


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			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=13			,       __SCREAMING_SNAKE_CASE=32			,       __SCREAMING_SNAKE_CASE=2			,       __SCREAMING_SNAKE_CASE=3			,       __SCREAMING_SNAKE_CASE=16			,       __SCREAMING_SNAKE_CASE=[1, 2, 1]			,       __SCREAMING_SNAKE_CASE=[2, 2, 4]			,       __SCREAMING_SNAKE_CASE=2			,       __SCREAMING_SNAKE_CASE=2.0			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=0.0			,       __SCREAMING_SNAKE_CASE=0.0			,       __SCREAMING_SNAKE_CASE=0.1			,       __SCREAMING_SNAKE_CASE="gelu"			,       __SCREAMING_SNAKE_CASE=False			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=0.02			,       __SCREAMING_SNAKE_CASE=1e-5			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=True			,       __SCREAMING_SNAKE_CASE=10			,       __SCREAMING_SNAKE_CASE=8			,       __SCREAMING_SNAKE_CASE=["stage1", "stage2"]			,       __SCREAMING_SNAKE_CASE=[1, 2]			,       ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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

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

              UpperCamelCase     :						Tuple 						=		self.get_config()

              return config, pixel_values, labels






       def 			_lowercase (  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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		SwinModel(config=__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()
              UpperCamelCase     :						Optional[int] 						=		model(__SCREAMING_SNAKE_CASE    )

              UpperCamelCase     :						List[Any] 						=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
              UpperCamelCase     :						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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		SwinBackbone(config=__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()
              UpperCamelCase     :						Optional[int] 						=		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
              UpperCamelCase     :						Any 						=		None
              UpperCamelCase     :						int 						=		SwinBackbone(config=__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()
              UpperCamelCase     :						Dict 						=		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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		SwinForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()
              UpperCamelCase     :						Optional[Any] 						=		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
              UpperCamelCase     :						List[str] 						=		1
              UpperCamelCase     :						List[Any] 						=		SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()

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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self.type_sequence_label_size
              UpperCamelCase     :						int 						=		SwinForImageClassification(__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()
              UpperCamelCase     :						Tuple 						=		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
              UpperCamelCase     :						int 						=		1
              UpperCamelCase     :						Union[str, Any] 						=		SwinForImageClassification(__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()

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






       def 			_lowercase (  self    ):




              """simple docstring"""




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




@require_torch
class  UpperCAmelCase_						(				_a,  _a,  unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  int				=       (
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       __UpperCamelCase   :  Tuple				=       (
           {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       __UpperCamelCase   :  List[str]				=       True

       __UpperCamelCase   :  Optional[Any]				=       False
       __UpperCamelCase   :  Union[str, Any]				=       False
       __UpperCamelCase   :  str				=       False






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Union[str, Any] 						=		SwinModelTester(self    )
              UpperCamelCase     :						str 						=		ConfigTester(self			,       config_class=__SCREAMING_SNAKE_CASE			,       embed_dim=37    )






       def 			_lowercase (  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 			_lowercase (  self    ):




              """simple docstring"""




              return






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              pass






       def 			_lowercase (  self    ):




              """simple docstring"""




              super().test_training_gradient_checkpointing()






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Tuple 						=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						List[Any] 						=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              pass






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




              """simple docstring"""




              pass






       def 			_lowercase (  self    ):




              """simple docstring"""




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

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






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		self.model_tester.prepare_config_and_inputs_for_common()

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

                     UpperCamelCase     :						Dict 						=		['''pixel_values''']
                     self.assertListEqual(arg_names[:1]			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						int 						=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase     :						int 						=		True

              for model_class in self.all_model_classes:
                     UpperCamelCase     :						str 						=		True
                     UpperCamelCase     :						int 						=		False
                     UpperCamelCase     :						List[Any] 						=		True
                     UpperCamelCase     :						List[str] 						=		model_class(__SCREAMING_SNAKE_CASE    )
                     model.to(__SCREAMING_SNAKE_CASE    )
                     model.eval()
                     with torch.no_grad():
                            UpperCamelCase     :						int 						=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )    )
                     UpperCamelCase     :						Dict 						=		outputs.attentions
                     UpperCamelCase     :						List[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"]
                     UpperCamelCase     :						Union[str, Any] 						=		True
                     UpperCamelCase     :						int 						=		config.window_size**2
                     UpperCamelCase     :						Union[str, Any] 						=		model_class(__SCREAMING_SNAKE_CASE    )
                     model.to(__SCREAMING_SNAKE_CASE    )
                     model.eval()
                     with torch.no_grad():
                            UpperCamelCase     :						str 						=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )    )
                     UpperCamelCase     :						int 						=		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]			,       )
                     UpperCamelCase     :						List[Any] 						=		len(__SCREAMING_SNAKE_CASE    )

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

                     # also another +1 for reshaped_hidden_states
                     UpperCamelCase     :						str 						=		1 if model_class.__name__ == '''SwinBackbone''' else 2
                     self.assertEqual(out_len + added_hidden_states			,       len(__SCREAMING_SNAKE_CASE    )    )

                     UpperCamelCase     :						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 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		model_class(__SCREAMING_SNAKE_CASE    )
              model.to(__SCREAMING_SNAKE_CASE    )
              model.eval()

              with torch.no_grad():
                     UpperCamelCase     :						Tuple 						=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )    )

              UpperCamelCase     :						str 						=		outputs.hidden_states

              UpperCamelCase     :						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
              UpperCamelCase     :						str 						=		(
                  config.patch_size
                  if isinstance(config.patch_size			,       collections.abc.Iterable    )
                  else (config.patch_size, config.patch_size)
              )

              UpperCamelCase     :						Dict 						=		(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     :						Union[str, Any] 						=		outputs.reshaped_hidden_states
                     self.assertEqual(len(__SCREAMING_SNAKE_CASE    )			,       __SCREAMING_SNAKE_CASE    )

                     UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Union[str, Any] 						=		reshaped_hidden_states[0].shape
                     UpperCamelCase     :						Any 						=		(
                         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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						List[str] 						=		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase     :						Union[str, 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:
                     UpperCamelCase     :						Any 						=		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"]
                     UpperCamelCase     :						Dict 						=		True

                     self.check_hidden_states_output(__SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase     :						Any 						=		3

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

              UpperCamelCase     :						Tuple 						=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
              UpperCamelCase     :						str 						=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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






       @slow
       def 			_lowercase (  self    ):




              """simple docstring"""




              for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase     :						List[Any] 						=		SwinModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )






       def 			_lowercase (  self    ):




              """simple docstring"""




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

              UpperCamelCase     :						Any 						=		_config_zero_init(__SCREAMING_SNAKE_CASE    )
              for model_class in self.all_model_classes:
                     UpperCamelCase     :						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 			_lowercase (  self    ):




              """simple docstring"""




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






       @slow
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[int] 						=		SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    ).to(__SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						int 						=		self.default_image_processor

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

              # forward pass
              with torch.no_grad():
                     UpperCamelCase     :						Dict 						=		model(**__SCREAMING_SNAKE_CASE    )

              # verify the logits
              UpperCamelCase     :						Any 						=		torch.Size((1, 1_000)    )
              self.assertEqual(outputs.logits.shape			,       __SCREAMING_SNAKE_CASE    )
              UpperCamelCase     :						List[str] 						=		torch.tensor([-0.0_948, -0.6_454, -0.0_921]    ).to(__SCREAMING_SNAKE_CASE    )
              self.assertTrue(torch.allclose(outputs.logits[0, :3]			,       __SCREAMING_SNAKE_CASE			,       atol=1e-4    )    )




@require_torch
class  UpperCAmelCase_						(				unittest.TestCase,  _a):
       '''simple docstring'''
       __UpperCamelCase   :  List[Any]				=       (SwinBackbone,) if is_torch_available() else ()
       __UpperCamelCase   :  int				=       SwinConfig






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Any 						=		SwinModelTester(self    )





import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

