


'''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        __magic_name__  :

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase=13 ,    lowerCamelCase=32 ,    lowerCamelCase=2 ,    lowerCamelCase=3 ,    lowerCamelCase=16 ,    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=10 ,    lowerCamelCase=8 ,    lowerCamelCase=["stage1", "stage2"] ,    lowerCamelCase=[1, 2] ,    ):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

          __A  :					str						     =       self.get_config()

          return config, pixel_values, labels


     def lowerCAmelCase__     (      self		):


          '''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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[Any]						     =       SwinModel(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A  :					List[Any]						     =       model(lowerCamelCase		)

          __A  :					Optional[int]						     =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths		) - 1))
          __A  :					Tuple						     =       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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       SwinBackbone(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A  :					List[str]						     =       model(lowerCamelCase		)

          # 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
          __A  :					Tuple						     =       None
          __A  :					Dict						     =       SwinBackbone(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A  :					Dict						     =       model(lowerCamelCase		)

          # 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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       SwinForMaskedImageModeling(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A  :					Optional[int]						     =       model(lowerCamelCase		)
          self.parent.assertEqual(
              result.logits.shape ,    (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

          # test greyscale images
          __A  :					List[str]						     =       1
          __A  :					str						     =       SwinForMaskedImageModeling(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A  :					List[str]						     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A  :					List[str]						     =       model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape ,    (self.batch_size, 1, self.image_size, self.image_size)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       self.type_sequence_label_size
          __A  :					Union[str, Any]						     =       SwinForImageClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A  :					Tuple						     =       model(lowerCamelCase ,    labels=lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          __A  :					Tuple						     =       1
          __A  :					int						     =       SwinForImageClassification(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          __A  :					Union[str, Any]						     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A  :					Tuple						     =       model(lowerCamelCase		)
          self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.type_sequence_label_size)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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







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

     """simple docstring"""






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

     lowerCamelCase__						=							False
     lowerCamelCase__						=							False
     lowerCamelCase__						=							False


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       SwinModelTester(self		)
          __A  :					List[str]						     =       ConfigTester(self ,    config_class=lowerCamelCase ,    embed_dim=37		)


     def lowerCAmelCase__     (      self		):


          '''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 lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Optional[Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."		)
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          super().test_training_gradient_checkpointing()


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase		)


     @unittest.skip(reason="Swin does not use inputs_embeds"		)
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          pass


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


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A  :					Union[str, Any]						     =       model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings() ,    (nn.Module)		)
               __A  :					Union[str, Any]						     =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase ,    nn.Linear		)		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

          for model_class in self.all_model_classes:
               __A  :					str						     =       model_class(lowerCamelCase		)
               __A  :					Optional[int]						     =       inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A  :					Tuple						     =       [*signature.parameters.keys()]

               __A  :					Optional[Any]						     =       ["pixel_values"]
               self.assertListEqual(arg_names[:1] ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.model_tester.prepare_config_and_inputs_for_common()
          __A  :					List[Any]						     =       True

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

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

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

               # Check attention is always last and order is fine
               __A  :					str						     =       True
               __A  :					Any						     =       True
               __A  :					Optional[Any]						     =       model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               with torch.no_grad():
                    __A  :					int						     =       model(**self._prepare_for_class(lowerCamelCase ,    lowerCamelCase		)		)

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

               __A  :					Optional[int]						     =       outputs.attentions

               self.assertEqual(len(lowerCamelCase		) ,    lowerCamelCase		)

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       model_class(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()

          with torch.no_grad():
               __A  :					int						     =       model(**self._prepare_for_class(lowerCamelCase ,    lowerCamelCase		)		)

          __A  :					Tuple						     =       outputs.hidden_states

          __A  :					str						     =       getattr(
              self.model_tester ,    "expected_num_hidden_layers" ,    len(self.model_tester.depths		) + 1		)
          self.assertEqual(len(lowerCamelCase		) ,    lowerCamelCase		)

          # Swin has a different seq_length
          __A  :					List[str]						     =       (
              config.patch_size
              if isinstance(config.patch_size ,    collections.abc.Iterable		)
              else (config.patch_size, config.patch_size)
          )

          __A  :					Any						     =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

          if not model_class.__name__ == "SwinBackbone":
               __A  :					Optional[Any]						     =       outputs.reshaped_hidden_states
               self.assertEqual(len(lowerCamelCase		) ,    lowerCamelCase		)

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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					str						     =       self.model_tester.prepare_config_and_inputs_for_common()

          __A  :					Tuple						     =       (
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size ,    collections.abc.Iterable		)
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
               __A  :					List[str]						     =       True
               self.check_hidden_states_output(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

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

               self.check_hidden_states_output(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.model_tester.prepare_config_and_inputs_for_common()
          __A  :					Dict						     =       3

          __A  :					Optional[Any]						     =       (
              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)
          )
          __A  :					int						     =       (
              config.patch_size
              if isinstance(config.patch_size ,    collections.abc.Iterable		)
              else (config.patch_size, config.patch_size)
          )

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

          for model_class in self.all_model_classes:
               __A  :					Dict						     =       True
               self.check_hidden_states_output(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    (padded_height, padded_width)		)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __A  :					Optional[int]						     =       True
               self.check_hidden_states_output(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    (padded_height, padded_width)		)


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A  :					Any						     =       SwinModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					int						     =       self.model_tester.prepare_config_and_inputs_for_common()

          __A  :					List[str]						     =       _config_zero_init(lowerCamelCase		)
          for model_class in self.all_model_classes:
               __A  :					Dict						     =       model_class(config=lowerCamelCase		)
               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        __magic_name__  (					unittest.TestCase   ):

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     @slow
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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

          __A  :					Union[str, Any]						     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
          __A  :					str						     =       image_processor(images=lowerCamelCase ,    return_tensors="pt"		).to(lowerCamelCase		)

          # forward pass
          with torch.no_grad():
               __A  :					str						     =       model(**lowerCamelCase		)

          # verify the logits
          __A  :					Union[str, Any]						     =       torch.Size((1, 1000)		)
          self.assertEqual(outputs.logits.shape ,    lowerCamelCase		)
          __A  :					Optional[int]						     =       torch.tensor([-0.0948, -0.6454, -0.0921]		).to(lowerCamelCase		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3] ,    lowerCamelCase ,    atol=1E-4		)		)







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

     """simple docstring"""






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


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					Tuple						     =       SwinModelTester(self		)





'''simple docstring'''


from collections import defaultdict


def 		_lowercase   (SCREAMING_SNAKE_CASE = 5       ):




     '''simple docstring'''





     __A  :					int						     =       defaultdict(SCREAMING_SNAKE_CASE       )
     __A  :					Optional[Any]						     =       0

     while True:
          __A  :					List[str]						     =       get_digits(SCREAMING_SNAKE_CASE       )
          freqs[digits].append(SCREAMING_SNAKE_CASE       )

          if len(freqs[digits]       ) == max_base:
               __A  :					Optional[int]						     =       freqs[digits][0] ** 3
               return base

          num += 1


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     return "".join(sorted(str(num**3       )       )       )


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

