




"""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 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=1_3					,				lowerCamelCase_=3_2					,				lowerCamelCase_=2					,				lowerCamelCase_=3					,				lowerCamelCase_=1_6					,				lowerCamelCase_=[1, 2, 1]					,				lowerCamelCase_=[2, 2, 4]					,				lowerCamelCase_=2					,				lowerCamelCase_=2.0					,				lowerCamelCase_=True					,				lowerCamelCase_=0.0					,				lowerCamelCase_=0.0					,				lowerCamelCase_=0.1					,				lowerCamelCase_="gelu"					,				lowerCamelCase_=False					,				lowerCamelCase_=True					,				lowerCamelCase_=0.02					,				lowerCamelCase_=1e-5					,				lowerCamelCase_=True					,				lowerCamelCase_=None					,				lowerCamelCase_=True					,				lowerCamelCase_=1_0					,				lowerCamelCase_=8					,				lowerCamelCase_=["stage1", "stage2"]					,				lowerCamelCase_=[1, 2]					,				)							->      Optional[Any]:
       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      UpperCAmelCase__ (		self)							->      Dict:
       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      UpperCAmelCase__ (		self)							->      List[str]:
       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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  SwinModel(config=snake_case_)
       model.to(snake_case_)
       model.eval()
       UpperCamelCase										=  model(snake_case_)

       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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  SwinBackbone(config=snake_case_)
       model.to(snake_case_)
       model.eval()
       UpperCamelCase										=  model(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], 1_6, 1_6])

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

       # verify backbone works with out_features=None
       UpperCamelCase										=  None
       UpperCamelCase										=  SwinBackbone(config=snake_case_)
       model.to(snake_case_)
       model.eval()
       UpperCamelCase										=  model(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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  SwinForMaskedImageModeling(config=snake_case_)
       model.to(snake_case_)
       model.eval()
       UpperCamelCase										=  model(snake_case_)
       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(snake_case_)
       model.to(snake_case_)
       model.eval()

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  self.type_sequence_label_size
       UpperCamelCase										=  SwinForImageClassification(snake_case_)
       model.to(snake_case_)
       model.eval()
       UpperCamelCase										=  model(snake_case_					,				labels=snake_case_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size))

       # test greyscale images
       UpperCamelCase										=  1
       UpperCamelCase										=  SwinForImageClassification(snake_case_)
       model.to(snake_case_)
       model.eval()

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







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase										=  self.prepare_config_and_inputs()
       (
           UpperCamelCase							
       )										=  config_and_inputs
       UpperCamelCase										=  {'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_torch
class 							snake_case_ (				UpperCamelCase__  ,   UpperCamelCase__  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   A_										=					(
       {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   A_										=					True

   A_										=					False
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  SwinModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=snake_case_					,				embed_dim=3_7)







   def      UpperCAmelCase__ (		self)							->      Optional[int]:
       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      UpperCAmelCase__ (		self)							->      Any:
       return







   def      UpperCAmelCase__ (		self)							->      Tuple:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_)







   @unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.''')
   def      UpperCAmelCase__ (		self)							->      int:
       pass







   def      UpperCAmelCase__ (		self)							->      Any:
       super().test_training_gradient_checkpointing()







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_)







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_)







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_)







   @unittest.skip(reason='''Swin does not use inputs_embeds''')
   def      UpperCAmelCase__ (		self)							->      Dict:
       pass







   @unittest.skip(reason='''Swin Transformer does not use feedforward chunking''')
   def      UpperCAmelCase__ (		self)							->      List[str]:
       pass







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(snake_case_)
           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]					,				snake_case_)







   def      UpperCAmelCase__ (		self)							->      int:
       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(snake_case_)
           model.to(snake_case_)
           model.eval()
           with torch.no_grad():
               UpperCamelCase										=  model(**self._prepare_for_class(snake_case_					,				snake_case_))
           UpperCamelCase										=  outputs.attentions
           UpperCamelCase										=  len(self.model_tester.depths)
           self.assertEqual(len(snake_case_)					,				snake_case_)

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

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

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

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

           UpperCamelCase										=  outputs.attentions

           self.assertEqual(len(snake_case_)					,				snake_case_)

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[str]:
       UpperCamelCase										=  model_class(snake_case_)
       model.to(snake_case_)
       model.eval()

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

       UpperCamelCase										=  outputs.hidden_states

       UpperCamelCase										=  getattr(
           self.model_tester					,				'''expected_num_hidden_layers'''					,				len(self.model_tester.depths) + 1)
       self.assertEqual(len(snake_case_)					,				snake_case_)

       # 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(snake_case_)					,				snake_case_)

           UpperCamelCase										=  reshaped_hidden_states[0].shape
           UpperCamelCase										=  (
               reshaped_hidden_states[0].view(snake_case_					,				snake_case_					,				height * width).permute(0					,				2					,				1)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])					,				[num_patches, self.model_tester.embed_dim]					,				)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       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(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_)

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

           self.check_hidden_states_output(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       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(snake_case_					,				snake_case_					,				snake_case_					,				(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(snake_case_					,				snake_case_					,				snake_case_					,				(padded_height, padded_width))







   @slow
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  SwinModel.from_pretrained(snake_case_)
           self.assertIsNotNone(snake_case_)







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase										=  _config_zero_init(snake_case_)
       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(config=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 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




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







   @slow
   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''').to(snake_case_)
       UpperCamelCase										=  self.default_image_processor

       UpperCamelCase										=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
       UpperCamelCase										=  image_processor(images=snake_case_					,				return_tensors='''pt''').to(snake_case_)

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

       # verify the logits
       UpperCamelCase										=  torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape					,				snake_case_)
       UpperCamelCase										=  torch.tensor([-0.0948, -0.6454, -0.0921]).to(snake_case_)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]					,				snake_case_					,				atol=1e-4))

@require_torch
class 							snake_case_ (				unittest.TestCase  ,   UpperCamelCase__ ):







   """simple docstring"""




   A_										=					(SwinBackbone,) if is_torch_available() else ()
   A_										=					SwinConfig







   def      UpperCAmelCase__ (		self)							->      Any:
       UpperCamelCase										=  SwinModelTester(self)





"""simple docstring"""







from math import pi





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return degree / (180 / pi)


if __name__ == "__main__":
  from doctest import testmod

  testmod()