

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     a__    :







  """simple docstring"""




  def __init__(     self      :	List[Any]			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	Any=1_3			,			UpperCAmelCase__      :	List[str]=3_2			,			UpperCAmelCase__      :	int=2			,			UpperCAmelCase__      :	Any=3			,			UpperCAmelCase__      :	Union[str, Any]=1_6			,			UpperCAmelCase__      :	Dict=[1, 2, 1]			,			UpperCAmelCase__      :	Optional[int]=[2, 2, 4]			,			UpperCAmelCase__      :	str=2			,			UpperCAmelCase__      :	Optional[int]=2.0			,			UpperCAmelCase__      :	int=True			,			UpperCAmelCase__      :	Optional[Any]=0.0			,			UpperCAmelCase__      :	int=0.0			,			UpperCAmelCase__      :	Dict=0.1			,			UpperCAmelCase__      :	List[str]="gelu"			,			UpperCAmelCase__      :	List[Any]=False			,			UpperCAmelCase__      :	List[Any]=True			,			UpperCAmelCase__      :	List[Any]=0.02			,			UpperCAmelCase__      :	Dict=1e-5			,			UpperCAmelCase__      :	Any=True			,			UpperCAmelCase__      :	str=None			,			UpperCAmelCase__      :	int=True			,			UpperCAmelCase__      :	Any=1_0			,			UpperCAmelCase__      :	List[str]=8			,			UpperCAmelCase__      :	Dict=["stage1", "stage2"]			,			UpperCAmelCase__      :	Union[str, Any]=[1, 2]			,			)   ->Tuple:
        """simple docstring"""







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


  def 	_lowercase (     self      :	Any						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				None
        if self.use_labels:
              SCREAMING_SNAKE_CASE	: List[Any]					       =				ids_tensor([self.batch_size]			,			self.type_sequence_label_size						)

        SCREAMING_SNAKE_CASE	: Tuple					       =				self.get_config()

        return config, pixel_values, labels


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Optional[Any]:
        """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      :	int			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Dict			,			UpperCAmelCase__      :	Any						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				SwinModel(config=UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()
        SCREAMING_SNAKE_CASE	: Dict					       =				model(UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
        SCREAMING_SNAKE_CASE	: List[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      :	Optional[Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	List[str]						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				SwinBackbone(config=UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				model(UpperCAmelCase__						)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps						)			,			len(config.out_features						)						)
        self.parent.assertListEqual(list(result.feature_maps[0].shape						)			,			[self.batch_size, model.channels[0], 1_6, 1_6]						)

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

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

        # 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      :	List[str]			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Union[str, Any]			,			UpperCAmelCase__      :	List[Any]						)   ->Union[str, Any]:
        """simple docstring"""







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

        # test greyscale images
        SCREAMING_SNAKE_CASE	: Any					       =				1
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				SwinForMaskedImageModeling(UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()

        SCREAMING_SNAKE_CASE	: str					       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        SCREAMING_SNAKE_CASE	: int					       =				model(UpperCAmelCase__						)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, 1, self.image_size, self.image_size)						)


  def 	_lowercase (     self      :	List[str]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				self.type_sequence_label_size
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				SwinForImageClassification(UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				model(UpperCAmelCase__			,			labels=UpperCAmelCase__						)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)						)

        # test greyscale images
        SCREAMING_SNAKE_CASE	: Dict					       =				1
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				SwinForImageClassification(UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()

        SCREAMING_SNAKE_CASE	: Dict					       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				model(UpperCAmelCase__						)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size)						)




  def 	_lowercase (     self      :	Any						)   ->str:
        """simple docstring"""







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







@require_torch
class     a__    (				UpperCAmelCase		,     UpperCAmelCase		,     unittest.TestCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	Optional[int]         =(
      (
          SwinModel,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  UpperCAmelCase__ :	Union[str, Any]         =(
      {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
      if is_torch_available()
      else {}
  )
  UpperCAmelCase__ :	Optional[int]         =True

  UpperCAmelCase__ :	Union[str, Any]         =False
  UpperCAmelCase__ :	Optional[int]         =False
  UpperCAmelCase__ :	Dict         =False


  def 	_lowercase (     self      :	int						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				SwinModelTester(self						)
        SCREAMING_SNAKE_CASE	: str					       =				ConfigTester(self			,			config_class=UpperCAmelCase__			,			embed_dim=3_7						)


  def 	_lowercase (     self      :	Dict						)   ->int:
        """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      :	Tuple						)   ->Union[str, Any]:
        """simple docstring"""







        return


  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__						)


  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
  def 	_lowercase (     self      :	Any						)   ->Dict:
        """simple docstring"""







        pass


  def 	_lowercase (     self      :	Dict						)   ->Dict:
        """simple docstring"""







        super().test_training_gradient_checkpointing()


  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*UpperCAmelCase__						)


  def 	_lowercase (     self      :	List[Any]						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase__						)


  def 	_lowercase (     self      :	str						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__						)


  @unittest.skip(reason="""Swin does not use inputs_embeds"""						)
  def 	_lowercase (     self      :	List[str]						)   ->Optional[int]:
        """simple docstring"""







        pass


  @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""						)
  def 	_lowercase (     self      :	str						)   ->List[str]:
        """simple docstring"""







        pass


  def 	_lowercase (     self      :	Dict						)   ->Tuple:
        """simple docstring"""







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

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


  def 	_lowercase (     self      :	Dict						)   ->int:
        """simple docstring"""







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

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

              SCREAMING_SNAKE_CASE	: Any					       =				["""pixel_values"""]
              self.assertListEqual(arg_names[:1]			,			UpperCAmelCase__						)


  def 	_lowercase (     self      :	List[str]						)   ->Union[str, Any]:
        """simple docstring"""







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

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

              # 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(UpperCAmelCase__						)
              model.to(UpperCAmelCase__						)
              model.eval()
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model(**self._prepare_for_class(UpperCAmelCase__			,			UpperCAmelCase__						)						)
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				outputs.attentions
              self.assertEqual(len(UpperCAmelCase__						)			,			UpperCAmelCase__						)

              self.assertListEqual(
                  list(attentions[0].shape[-3:]						)			,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,			)
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				len(UpperCAmelCase__						)

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

              # also another +1 for reshaped_hidden_states
              SCREAMING_SNAKE_CASE	: Dict					       =				1 if model_class.__name__ == """SwinBackbone""" else 2
              self.assertEqual(out_len + added_hidden_states			,			len(UpperCAmelCase__						)						)

              SCREAMING_SNAKE_CASE	: List[Any]					       =				outputs.attentions

              self.assertEqual(len(UpperCAmelCase__						)			,			UpperCAmelCase__						)

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


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	Optional[Any]			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Dict			,			UpperCAmelCase__      :	Dict						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model_class(UpperCAmelCase__						)
        model.to(UpperCAmelCase__						)
        model.eval()

        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: str					       =				model(**self._prepare_for_class(UpperCAmelCase__			,			UpperCAmelCase__						)						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				outputs.hidden_states

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				getattr(
            self.model_tester			,			"""expected_num_hidden_layers"""			,			len(self.model_tester.depths						) + 1						)
        self.assertEqual(len(UpperCAmelCase__						)			,			UpperCAmelCase__						)

        # Swin has a different seq_length
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				(
            config.patch_size
            if isinstance(config.patch_size			,			collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(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	: List[str]					       =				outputs.reshaped_hidden_states
              self.assertEqual(len(UpperCAmelCase__						)			,			UpperCAmelCase__						)

              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				reshaped_hidden_states[0].shape
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(
                  reshaped_hidden_states[0].view(UpperCAmelCase__			,			UpperCAmelCase__			,			height * width						).permute(0			,			2			,			1						)
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-2:]						)			,			[num_patches, self.model_tester.embed_dim]			,			)


  def 	_lowercase (     self      :	Any						)   ->List[Any]:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE	: 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)
        )

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE	: Tuple					       =				True
              self.check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)

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

              self.check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)


  def 	_lowercase (     self      :	Tuple						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				3

        SCREAMING_SNAKE_CASE	: Dict					       =				(
            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	: Dict					       =				(
            config.patch_size
            if isinstance(config.patch_size			,			collections.abc.Iterable						)
            else (config.patch_size, config.patch_size)
        )

        SCREAMING_SNAKE_CASE	: str					       =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        SCREAMING_SNAKE_CASE	: List[Any]					       =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE	: str					       =				True
              self.check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			(padded_height, padded_width)						)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE	: List[str]					       =				True
              self.check_hidden_states_output(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			(padded_height, padded_width)						)


  @slow
  def 	_lowercase (     self      :	Union[str, Any]						)   ->str:
        """simple docstring"""







        for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE	: List[str]					       =				SwinModel.from_pretrained(UpperCAmelCase__						)
              self.assertIsNotNone(UpperCAmelCase__						)




  def 	_lowercase (     self      :	Tuple						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				self.model_tester.prepare_config_and_inputs_for_common()

        SCREAMING_SNAKE_CASE	: List[str]					       =				_config_zero_init(UpperCAmelCase__						)
        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE	: Any					       =				model_class(config=UpperCAmelCase__						)
              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     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @cached_property
  def 	_lowercase (     self      :	Optional[Any]						)   ->str:
        """simple docstring"""







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




  @slow
  def 	_lowercase (     self      :	Dict						)   ->Dict:
        """simple docstring"""







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

        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        SCREAMING_SNAKE_CASE	: Any					       =				image_processor(images=UpperCAmelCase__			,			return_tensors="""pt"""						).to(UpperCAmelCase__						)

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE	: str					       =				model(**UpperCAmelCase__						)

        # verify the logits
        SCREAMING_SNAKE_CASE	: Tuple					       =				torch.Size((1, 1_0_0_0)						)
        self.assertEqual(outputs.logits.shape			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				torch.tensor([-0.09_48, -0.64_54, -0.09_21]						).to(UpperCAmelCase__						)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			UpperCAmelCase__			,			atol=1e-4						)						)







@require_torch
class     a__    (				unittest.TestCase		,     UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	int         =(SwinBackbone,) if is_torch_available() else ()
  UpperCAmelCase__ :	Any         =SwinConfig


  def 	_lowercase (     self      :	Tuple						)   ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				SwinModelTester(self						)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__	:       int			      =      {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__	:       int			      =      [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__	:       int			      =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




