




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

      """simple docstring"""

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

      lowerCAmelCase__	:       Union[str, Any]			=    False
      lowerCAmelCase__	:       Optional[int]			=    False
      lowerCAmelCase__	:       Tuple			=    False







      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  SwinModelTester(self )
          __UpperCamelCase     						=  ConfigTester(self       ,				config_class=snake_case       ,				embed_dim=37 )







      def 					snake_case		(   self    :       Dict ):
          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 					snake_case		(   self    :       Union[str, Any] ):
          return







      def 					snake_case		(   self    :       int ):
          __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 					snake_case		(   self    :       Optional[Any] ):
          pass







      def 					snake_case		(   self    :       List[Any] ):
          super().test_training_gradient_checkpointing()







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case )







      def 					snake_case		(   self    :       Dict ):
          __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 					snake_case		(   self    :       Optional[Any] ):
          pass







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







      def 					snake_case		(   self    :       List[Any] ):
          __UpperCamelCase						,					__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 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase						,					__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 					snake_case		(   self    :       Tuple ):
          __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(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 					snake_case		(   self    :       Any       ,				snake_case    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Dict ):
          __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						,					__UpperCamelCase						,					__UpperCamelCase						,					__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 					snake_case		(   self    :       Any ):
          __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(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 					snake_case		(   self    :       List[str] ):
          __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(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 					snake_case		(   self    :       Optional[Any] ):
          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase     						=  SwinModel.from_pretrained(snake_case )
              self.assertIsNotNone(snake_case )







      def 					snake_case		(   self    :       str ):
          __UpperCamelCase						,					__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        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      @cached_property
      def 					snake_case		(   self    :       int ):
          return (
              AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224''' )
              if is_vision_available()
              else None
          )







      @slow
      def 					snake_case		(   self    :       Union[str, Any] ):
          __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, 1000) )
          self.assertEqual(outputs.logits.shape       ,				snake_case )
          __UpperCamelCase     						=  torch.tensor([-0.09_48, -0.64_54, -0.09_21] ).to(snake_case )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				snake_case       ,				atol=1E-4 ) )







@require_torch
class        _lowerCamelCase  (	unittest.TestCase							,	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    (SwinBackbone,) if is_torch_available() else ()
      lowerCAmelCase__	:       Union[str, Any]			=    SwinConfig







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  SwinModelTester(self )












def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  1.0
    for i in range(1    ,   r + 1					):
        ret *= (n + 1 - i) / i
    return round(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return choose(8 + n    ,   lowercase_					) + choose(9 + n    ,   lowercase_					) - 10




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return sum(non_bouncy_exact(lowercase_					) for i in range(1    ,   n + 1					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_ = 1_00					)				->		int:

    """simple docstring"""


    return non_bouncy_upto(lowercase_					)


if __name__ == "__main__":
 print(f"""{solution() = }""")






