'''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     _lowercase					:


 '''simple docstring'''






 def __init__(				self       :     str				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Any=13				,	SCREAMING_SNAKE_CASE__       :     int=32				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=2				,	SCREAMING_SNAKE_CASE__       :     int=3				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=16				,	SCREAMING_SNAKE_CASE__       :     Dict=[1, 2, 1]				,	SCREAMING_SNAKE_CASE__       :     List[Any]=[2, 2, 4]				,	SCREAMING_SNAKE_CASE__       :     List[str]=2				,	SCREAMING_SNAKE_CASE__       :     str=2.0				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=True				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=0.0				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=0.0				,	SCREAMING_SNAKE_CASE__       :     List[str]=0.1				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]="gelu"				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=False				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=True				,	SCREAMING_SNAKE_CASE__       :     int=0.0_2				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=1e-5				,	SCREAMING_SNAKE_CASE__       :     str=True				,	SCREAMING_SNAKE_CASE__       :     str=None				,	SCREAMING_SNAKE_CASE__       :     List[Any]=True				,	SCREAMING_SNAKE_CASE__       :     List[str]=10				,	SCREAMING_SNAKE_CASE__       :     Tuple=8				,	SCREAMING_SNAKE_CASE__       :     List[Any]=["stage1", "stage2"]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=[1, 2]				,	)  ->   Dict:
  __lowerCAmelCase    							=						parent
  __lowerCAmelCase    							=						batch_size
  __lowerCAmelCase    							=						image_size
  __lowerCAmelCase    							=						patch_size
  __lowerCAmelCase    							=						num_channels
  __lowerCAmelCase    							=						embed_dim
  __lowerCAmelCase    							=						depths
  __lowerCAmelCase    							=						num_heads
  __lowerCAmelCase    							=						window_size
  __lowerCAmelCase    							=						mlp_ratio
  __lowerCAmelCase    							=						qkv_bias
  __lowerCAmelCase    							=						hidden_dropout_prob
  __lowerCAmelCase    							=						attention_probs_dropout_prob
  __lowerCAmelCase    							=						drop_path_rate
  __lowerCAmelCase    							=						hidden_act
  __lowerCAmelCase    							=						use_absolute_embeddings
  __lowerCAmelCase    							=						patch_norm
  __lowerCAmelCase    							=						layer_norm_eps
  __lowerCAmelCase    							=						initializer_range
  __lowerCAmelCase    							=						is_training
  __lowerCAmelCase    							=						scope
  __lowerCAmelCase    							=						use_labels
  __lowerCAmelCase    							=						type_sequence_label_size
  __lowerCAmelCase    							=						encoder_stride
  __lowerCAmelCase    							=						out_features
  __lowerCAmelCase    							=						out_indices
 def a (				self       :     Any							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

  __lowerCAmelCase    							=						self.get_config()

  return config, pixel_values, labels
 def a (				self       :     Tuple							)  ->   List[Any]:
  return SwinConfig(
      image_size=self.image_size				,	patch_size=self.patch_size				,	num_channels=self.num_channels				,	embed_dim=self.embed_dim				,	depths=self.depths				,	num_heads=self.num_heads				,	window_size=self.window_size				,	mlp_ratio=self.mlp_ratio				,	qkv_bias=self.qkv_bias				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	drop_path_rate=self.drop_path_rate				,	hidden_act=self.hidden_act				,	use_absolute_embeddings=self.use_absolute_embeddings				,	path_norm=self.patch_norm				,	layer_norm_eps=self.layer_norm_eps				,	initializer_range=self.initializer_range				,	encoder_stride=self.encoder_stride				,	out_features=self.out_features				,	out_indices=self.out_indices				,	)
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						SwinModel(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
  __lowerCAmelCase    							=						int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

  self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, expected_seq_len, expected_dim)							)
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   List[str]:
  __lowerCAmelCase    							=						SwinBackbone(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							)

  # verify hidden states
  self.parent.assertEqual(len(result.feature_maps							)				,	len(config.out_features							)							)
  self.parent.assertListEqual(list(result.feature_maps[0].shape							)				,	[self.batch_size, model.channels[0], 16, 16]							)

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

  # verify backbone works with out_features=None
  __lowerCAmelCase    							=						None
  __lowerCAmelCase    							=						SwinBackbone(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							)

  # verify feature maps
  self.parent.assertEqual(len(result.feature_maps							)				,	1							)
  self.parent.assertListEqual(list(result.feature_maps[0].shape							)				,	[self.batch_size, model.channels[-1], 4, 4]							)

  # verify channels
  self.parent.assertEqual(len(model.channels							)				,	1							)
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						SwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(
      result.logits.shape				,	(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

  # test greyscale images
  __lowerCAmelCase    							=						1
  __lowerCAmelCase    							=						SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

  __lowerCAmelCase    							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape				,	(self.batch_size, 1, self.image_size, self.image_size)							)
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   int:
  __lowerCAmelCase    							=						self.type_sequence_label_size
  __lowerCAmelCase    							=						SwinForImageClassification(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()
  __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__				,	labels=SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size)							)

  # test greyscale images
  __lowerCAmelCase    							=						1
  __lowerCAmelCase    							=						SwinForImageClassification(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

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





 def a (				self       :     Any							)  ->   Any:
  __lowerCAmelCase    							=						self.prepare_config_and_inputs()
  (
      (
      __lowerCAmelCase
  )							,	(
      __lowerCAmelCase
  )							,	(
      __lowerCAmelCase
  )							,	
  )    							=						config_and_inputs
  __lowerCAmelCase    							=						{"""pixel_values""": pixel_values}
  return config, inputs_dict







@require_torch
class     _lowercase					(    UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase      ):


 '''simple docstring'''






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

 _SCREAMING_SNAKE_CASE					:	str				     = False
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = False
 _SCREAMING_SNAKE_CASE					:	Optional[int]				     = False
 def a (				self       :     List[Any]							)  ->   List[str]:
  __lowerCAmelCase    							=						SwinModelTester(self							)
  __lowerCAmelCase    							=						ConfigTester(self				,	config_class=SCREAMING_SNAKE_CASE__				,	embed_dim=37							)
 def a (				self       :     List[str]							)  ->   str:
  self.create_and_test_config_common_properties()
  self.config_tester.create_and_test_config_to_json_string()
  self.config_tester.create_and_test_config_to_json_file()
  self.config_tester.create_and_test_config_from_and_save_pretrained()
  self.config_tester.create_and_test_config_with_num_labels()
  self.config_tester.check_config_can_be_init_without_params()
  self.config_tester.check_config_arguments_init()
 def a (				self       :     Any							)  ->   Optional[int]:
  return
 def a (				self       :     Union[str, Any]							)  ->   Tuple:
  __lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""							)
 def a (				self       :     Optional[int]							)  ->   int:
  pass
 def a (				self       :     List[str]							)  ->   List[str]:
  super().test_training_gradient_checkpointing()
 def a (				self       :     int							)  ->   Tuple:
  __lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[Any]							)  ->   Any:
  __lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)
 @unittest.skip(reason="""Swin does not use inputs_embeds"""							)
 def a (				self       :     Tuple							)  ->   Optional[int]:
  pass
 @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""							)
 def a (				self       :     Optional[Any]							)  ->   Optional[int]:
  pass
 def a (				self       :     List[Any]							)  ->   int:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowerCAmelCase    							=						model_class(SCREAMING_SNAKE_CASE__							)
   self.assertIsInstance(model.get_input_embeddings()				,	(nn.Module)							)
   __lowerCAmelCase    							=						model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__				,	nn.Linear							)							)
 def a (				self       :     Optional[int]							)  ->   str:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()

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

   __lowerCAmelCase    							=						["""pixel_values"""]
   self.assertListEqual(arg_names[:1]				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[Any]							)  ->   List[str]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase    							=						True

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

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

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

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

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

   __lowerCAmelCase    							=						outputs.attentions

   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)				,	SCREAMING_SNAKE_CASE__							)

   self.assertListEqual(
       list(self_attentions[0].shape[-3:]							)				,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,	)
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   str:
  __lowerCAmelCase    							=						model_class(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.eval()

  with torch.no_grad():
   __lowerCAmelCase    							=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)							)

  __lowerCAmelCase    							=						outputs.hidden_states

  __lowerCAmelCase    							=						getattr(
      self.model_tester				,	"""expected_num_hidden_layers"""				,	len(self.model_tester.depths							) + 1							)
  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)				,	SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

  self.assertListEqual(
      list(hidden_states[0].shape[-2:]							)				,	[num_patches, self.model_tester.embed_dim]				,	)

  if not model_class.__name__ == "SwinBackbone":
   __lowerCAmelCase    							=						outputs.reshaped_hidden_states
   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)				,	SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						reshaped_hidden_states[0].shape
   __lowerCAmelCase    							=						(
       reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	height * width							).permute(0				,	2				,	1							)
   )
   self.assertListEqual(
       list(reshaped_hidden_states.shape[-2:]							)				,	[num_patches, self.model_tester.embed_dim]				,	)
 def a (				self       :     Any							)  ->   Optional[int]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()

  __lowerCAmelCase    							=						(
      self.model_tester.image_size
      if isinstance(self.model_tester.image_size				,	collections.abc.Iterable							)
      else (self.model_tester.image_size, self.model_tester.image_size)
  )

  for model_class in self.all_model_classes:
   __lowerCAmelCase    							=						True
   self.check_hidden_states_output(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   __lowerCAmelCase    							=						True

   self.check_hidden_states_output(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Union[str, Any]							)  ->   Union[str, Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()
  __lowerCAmelCase    							=						3

  __lowerCAmelCase    							=						(
      self.model_tester.image_size
      if isinstance(self.model_tester.image_size				,	collections.abc.Iterable							)
      else (self.model_tester.image_size, self.model_tester.image_size)
  )
  __lowerCAmelCase    							=						(
      config.patch_size
      if isinstance(config.patch_size				,	collections.abc.Iterable							)
      else (config.patch_size, config.patch_size)
  )

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

  for model_class in self.all_model_classes:
   __lowerCAmelCase    							=						True
   self.check_hidden_states_output(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	(padded_height, padded_width)							)

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   __lowerCAmelCase    							=						True
   self.check_hidden_states_output(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	(padded_height, padded_width)							)
 @slow
 def a (				self       :     Dict							)  ->   Any:
  for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __lowerCAmelCase    							=						SwinModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)





 def a (				self       :     Optional[Any]							)  ->   Union[str, Any]:
  __lowerCAmelCase							,	__lowerCAmelCase    							=						self.model_tester.prepare_config_and_inputs_for_common()

  __lowerCAmelCase    							=						_config_zero_init(SCREAMING_SNAKE_CASE__							)
  for model_class in self.all_model_classes:
   __lowerCAmelCase    							=						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     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 @cached_property
 def a (				self       :     Tuple							)  ->   List[Any]:
  return (
      AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
      if is_vision_available()
      else None
  )





 @slow
 def a (				self       :     Any							)  ->   Tuple:
  __lowerCAmelCase    							=						SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							).to(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						self.default_image_processor

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

  # forward pass
  with torch.no_grad():
   __lowerCAmelCase    							=						model(**SCREAMING_SNAKE_CASE__							)

  # verify the logits
  __lowerCAmelCase    							=						torch.Size((1, 10_00)							)
  self.assertEqual(outputs.logits.shape				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]							).to(SCREAMING_SNAKE_CASE__							)
  self.assertTrue(torch.allclose(outputs.logits[0, :3]				,	SCREAMING_SNAKE_CASE__				,	atol=1e-4							)							)







@require_torch
class     _lowercase					(    unittest.TestCase  ,							UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	List[str]				     = (SwinBackbone,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE					:	str				     = SwinConfig
 def a (				self       :     Union[str, Any]							)  ->   Tuple:
  __lowerCAmelCase    							=						SwinModelTester(self							)







'''simple docstring'''

import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class     _lowercase					(    nn.Linear      ):


 '''simple docstring'''






 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     str = "constant"				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     str = "topK"				,	)  ->   Any:
  super(SCREAMING_SNAKE_CASE__				,	self							).__init__(in_features=SCREAMING_SNAKE_CASE__				,	out_features=SCREAMING_SNAKE_CASE__				,	bias=SCREAMING_SNAKE_CASE__							)
  assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
  __lowerCAmelCase    							=						pruning_method

  if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
   __lowerCAmelCase    							=						mask_scale
   __lowerCAmelCase    							=						mask_init
   __lowerCAmelCase    							=						nn.Parameter(torch.empty(self.weight.size()							)							)
   self.init_mask()
 def a (				self       :     Union[str, Any]							)  ->   int:
  if self.mask_init == "constant":
   init.constant_(self.mask_scores				,	val=self.mask_scale							)
  elif self.mask_init == "uniform":
   init.uniform_(self.mask_scores				,	a=-self.mask_scale				,	b=self.mask_scale							)
  elif self.mask_init == "kaiming":
   init.kaiming_uniform_(self.mask_scores				,	a=math.sqrt(5							)							)





 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     torch.tensor				,	SCREAMING_SNAKE_CASE__       :     float							)  ->   Tuple:
  # Get the mask
  if self.pruning_method == "topK":
   __lowerCAmelCase    							=						TopKBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
   __lowerCAmelCase    							=						"""sigmoied""" in self.pruning_method
   __lowerCAmelCase    							=						ThresholdBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "magnitude":
   __lowerCAmelCase    							=						MagnitudeBinarizer.apply(self.weight				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "l0":
   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						-0.1, 1.1, 2 / 3
   if self.training:
    __lowerCAmelCase    							=						torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9							)
    __lowerCAmelCase    							=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
   else:
    __lowerCAmelCase    							=						torch.sigmoid(self.mask_scores							)
   __lowerCAmelCase    							=						s * (r - l) + l
   __lowerCAmelCase    							=						s_bar.clamp(min=0.0				,	max=1.0							)
  # Mask weights with computed mask
  __lowerCAmelCase    							=						mask * self.weight
  # Compute output (linear layer) with masked weights
  return nn.functional.linear(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.bias							)






