




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_       				=      {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_       				=      [
        'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RegNetForImageClassification',
        'RegNetModel',
        'RegNetPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_       				=      [
        'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRegNetForImageClassification',
        'TFRegNetModel',
        'TFRegNetPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_       				=      [
        'FlaxRegNetForImageClassification',
        'FlaxRegNetModel',
        'FlaxRegNetPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_regnet import (
            REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            RegNetForImageClassification,
            RegNetModel,
            RegNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_regnet import (
            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRegNetForImageClassification,
            TFRegNetModel,
            TFRegNetPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_regnet import (
            FlaxRegNetForImageClassification,
            FlaxRegNetModel,
            FlaxRegNetPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_       				=      _LazyModule(__name__, globals()['__file__'], _import_structure)






'''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 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int]=13						,		_UpperCAmelCase    :	Dict=32						,		_UpperCAmelCase    :	Any=2						,		_UpperCAmelCase    :	Optional[int]=3						,		_UpperCAmelCase    :	int=16						,		_UpperCAmelCase    :	Optional[int]=[1, 2, 1]						,		_UpperCAmelCase    :	List[str]=[2, 2, 4]						,		_UpperCAmelCase    :	Union[str, Any]=2						,		_UpperCAmelCase    :	int=2.0						,		_UpperCAmelCase    :	List[Any]=True						,		_UpperCAmelCase    :	Optional[int]=0.0						,		_UpperCAmelCase    :	Optional[Any]=0.0						,		_UpperCAmelCase    :	Any=0.1						,		_UpperCAmelCase    :	Union[str, Any]="gelu"						,		_UpperCAmelCase    :	Optional[int]=False						,		_UpperCAmelCase    :	Tuple=True						,		_UpperCAmelCase    :	Dict=0.02						,		_UpperCAmelCase    :	int=1E-5						,		_UpperCAmelCase    :	Tuple=True						,		_UpperCAmelCase    :	Tuple=None						,		_UpperCAmelCase    :	Optional[int]=True						,		_UpperCAmelCase    :	str=10						,		_UpperCAmelCase    :	Tuple=8						,		_UpperCAmelCase    :	str=["stage1", "stage2"]						,		_UpperCAmelCase    :	str=[1, 2]						,		):






   """simple docstring"""






   UpperCAmelCase__										=    parent
   UpperCAmelCase__										=    batch_size
   UpperCAmelCase__										=    image_size
   UpperCAmelCase__										=    patch_size
   UpperCAmelCase__										=    num_channels
   UpperCAmelCase__										=    embed_dim
   UpperCAmelCase__										=    depths
   UpperCAmelCase__										=    num_heads
   UpperCAmelCase__										=    window_size
   UpperCAmelCase__										=    mlp_ratio
   UpperCAmelCase__										=    qkv_bias
   UpperCAmelCase__										=    hidden_dropout_prob
   UpperCAmelCase__										=    attention_probs_dropout_prob
   UpperCAmelCase__										=    drop_path_rate
   UpperCAmelCase__										=    hidden_act
   UpperCAmelCase__										=    use_absolute_embeddings
   UpperCAmelCase__										=    patch_norm
   UpperCAmelCase__										=    layer_norm_eps
   UpperCAmelCase__										=    initializer_range
   UpperCAmelCase__										=    is_training
   UpperCAmelCase__										=    scope
   UpperCAmelCase__										=    use_labels
   UpperCAmelCase__										=    type_sequence_label_size
   UpperCAmelCase__										=    encoder_stride
   UpperCAmelCase__										=    out_features
   UpperCAmelCase__										=    out_indices


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

   UpperCAmelCase__										=    self.get_config()

   return config, pixel_values, labels


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinModel(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )

   UpperCAmelCase__										=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
   UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    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], 16, 16] )

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

   # verify backbone works with out_features=None
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    SwinBackbone(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	int						,		_UpperCAmelCase    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinForMaskedImageModeling(config=_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase )
   self.parent.assertEqual(
       result.logits.shape						,		(self.batch_size, self.num_channels, self.image_size, self.image_size) )

   # test greyscale images
   UpperCAmelCase__										=    1
   UpperCAmelCase__										=    SwinForMaskedImageModeling(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   UpperCAmelCase__										=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
   UpperCAmelCase__										=    model(_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, 1, self.image_size, self.image_size) )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[str]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    self.type_sequence_label_size
   UpperCAmelCase__										=    SwinForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()
   UpperCAmelCase__										=    model(_UpperCAmelCase						,		labels=_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.type_sequence_label_size) )

   # test greyscale images
   UpperCAmelCase__										=    1
   UpperCAmelCase__										=    SwinForImageClassification(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

   UpperCAmelCase__										=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
   UpperCAmelCase__										=    model(_UpperCAmelCase )
   self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.type_sequence_label_size) )



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.prepare_config_and_inputs()
   (
       (
       UpperCAmelCase__
   )							,			(
       UpperCAmelCase__
   )							,			(
       UpperCAmelCase__
   )							,			
   )										=    config_and_inputs
   UpperCAmelCase__										=    {"""pixel_values""": pixel_values}
   return config, inputs_dict

@require_torch
class 						lowerCAmelCase_   ( lowerCamelCase_			,  lowerCamelCase_			,  unittest.TestCase   ):







  '''simple docstring'''







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

  lowerCAmelCase_				:					Optional[int]				     =						False
  lowerCAmelCase_				:					List[Any]				     =						False
  lowerCAmelCase_				:					List[Any]				     =						False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinModelTester(self )
   UpperCAmelCase__										=    ConfigTester(self						,		config_class=_UpperCAmelCase						,		embed_dim=37 )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """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 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   return


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   pass


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   super().test_training_gradient_checkpointing()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_backbone(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	Any ):






   """simple docstring"""






   pass


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






   """simple docstring"""






   pass


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

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

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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	str ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__										=    True

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

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

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

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

    UpperCAmelCase__										=    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 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__										=    model_class(_UpperCAmelCase )
   model.to(_UpperCAmelCase )
   model.eval()

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

   UpperCAmelCase__										=    outputs.hidden_states

   UpperCAmelCase__										=    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
   UpperCAmelCase__										=    (
       config.patch_size
       if isinstance(config.patch_size						,		collections.abc.Iterable )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__										=    (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__										=    outputs.reshaped_hidden_states
    self.assertEqual(len(_UpperCAmelCase )						,		_UpperCAmelCase )

    UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    reshaped_hidden_states[0].shape
    UpperCAmelCase__										=    (
        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 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__										=    (
       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__										=    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"]
    UpperCAmelCase__										=    True

    self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__										=    3

   UpperCAmelCase__										=    (
       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__										=    (
       config.patch_size
       if isinstance(config.patch_size						,		collections.abc.Iterable )
       else (config.patch_size, config.patch_size)
   )

   UpperCAmelCase__										=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
   UpperCAmelCase__										=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

   for model_class in self.all_model_classes:
    UpperCAmelCase__										=    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"]
    UpperCAmelCase__										=    True
    self.check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		(padded_height, padded_width) )


  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str] ):






   """simple docstring"""






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



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__							,			UpperCAmelCase__										=    self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__										=    _config_zero_init(_UpperCAmelCase )
   for model_class in self.all_model_classes:
    UpperCAmelCase__										=    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 						lowerCAmelCase_   ( unittest.TestCase   ):







  '''simple docstring'''







  @cached_property
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






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



  @slow
  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224""" ).to(_UpperCAmelCase )
   UpperCAmelCase__										=    self.default_image_processor

   UpperCAmelCase__										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   UpperCAmelCase__										=    image_processor(images=_UpperCAmelCase						,		return_tensors="""pt""" ).to(_UpperCAmelCase )

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

   # verify the logits
   UpperCAmelCase__										=    torch.Size((1, 10_00) )
   self.assertEqual(outputs.logits.shape						,		_UpperCAmelCase )
   UpperCAmelCase__										=    torch.tensor([-0.0948, -0.6454, -0.0921] ).to(_UpperCAmelCase )
   self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		_UpperCAmelCase						,		atol=1E-4 ) )

@require_torch
class 						lowerCAmelCase_   ( unittest.TestCase			,  lowerCamelCase_   ):







  '''simple docstring'''







  lowerCAmelCase_				:					Dict				     =						(SwinBackbone,) if is_torch_available() else ()
  lowerCAmelCase_				:					Tuple				     =						SwinConfig


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






   """simple docstring"""






   UpperCAmelCase__										=    SwinModelTester(self )
