






'''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 a_     :







   def __init__(self       , __a       , __a=1_3       , __a=3_2       , __a=2       , __a=3       , __a=1_6       , __a=[1, 2, 1]       , __a=[2, 2, 4]       , __a=2       , __a=2.0       , __a=True       , __a=0.0       , __a=0.0       , __a=0.1       , __a="gelu"       , __a=False       , __a=True       , __a=0.02       , __a=1E-5       , __a=True       , __a=None       , __a=True       , __a=1_0       , __a=8       , __a=["stage1", "stage2"]       , __a=[1, 2]       , )      ->   Any:
      """simple docstring"""






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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   Tuple  				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

      __snake_case					:   Union[str, Any]  				=			self.get_config()

      return config, pixel_values, labels







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return SwinConfig(
          image_size=self.image_size       , patch_size=self.patch_size       , num_channels=self.num_channels       , embed_dim=self.embed_dim       , depths=self.depths       , num_heads=self.num_heads       , window_size=self.window_size       , mlp_ratio=self.mlp_ratio       , qkv_bias=self.qkv_bias       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , drop_path_rate=self.drop_path_rate       , hidden_act=self.hidden_act       , use_absolute_embeddings=self.use_absolute_embeddings       , path_norm=self.patch_norm       , layer_norm_eps=self.layer_norm_eps       , initializer_range=self.initializer_range       , encoder_stride=self.encoder_stride       , out_features=self.out_features       , out_indices=self.out_indices       , )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Dict  				=			SwinModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a)

      __snake_case					:   Optional[Any]  				=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
      __snake_case					:   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      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			SwinBackbone(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a)

      # 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
      __snake_case					:   Dict  				=			None
      __snake_case					:   Optional[int]  				=			SwinBackbone(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Dict  				=			model(__a)

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

      # verify channels
      self.parent.assertEqual(len(model.channels)       , 1)







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






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

      # test greyscale images
      __snake_case					:   Union[str, Any]  				=			1
      __snake_case					:   int  				=			SwinForMaskedImageModeling(__a)
      model.to(__a)
      model.eval()

      __snake_case					:   Any  				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      __snake_case					:   List[Any]  				=			model(__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, 1, self.image_size, self.image_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.type_sequence_label_size
      __snake_case					:   Any  				=			SwinForImageClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Dict  				=			model(__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      __snake_case					:   Union[str, Any]  				=			1
      __snake_case					:   List[str]  				=			SwinForImageClassification(__a)
      model.to(__a)
      model.eval()

      __snake_case					:   Dict  				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      __snake_case					:   List[Any]  				=			model(__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Dict  				=			config_and_inputs
      __snake_case					:   List[str]  				=			{'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				(
       {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   _snake_case	   =				True

   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			SwinModelTester(self)
      __snake_case					:   Optional[Any]  				=			ConfigTester(self       , config_class=__a       , embed_dim=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      return







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.')
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      pass







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      super().test_training_gradient_checkpointing()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Any  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__a)







   @unittest.skip(reason='Swin does not use inputs_embeds')
   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      pass







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






      pass







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      __snake_case				,__snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __snake_case					:   Optional[int]  				=			model_class(__a)
         self.assertIsInstance(model.get_input_embeddings()       , (nn.Module))
         __snake_case					:   int  				=			model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__a       , nn.Linear))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case				,__snake_case					:   Any  				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __snake_case					:   Union[str, Any]  				=			model_class(__a)
         __snake_case					:   Dict  				=			inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __snake_case					:   Dict  				=			[*signature.parameters.keys()]

         __snake_case					:   str  				=			['pixel_values']
         self.assertListEqual(arg_names[:1]       , __a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case				,__snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs_for_common()
      __snake_case					:   str  				=			True

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

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

         self.assertListEqual(
             list(attentions[0].shape[-3:])       , [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       , )
         __snake_case					:   List[Any]  				=			len(__a)

         # Check attention is always last and order is fine
         __snake_case					:   int  				=			True
         __snake_case					:   Dict  				=			True
         __snake_case					:   int  				=			model_class(__a)
         model.to(__a)
         model.eval()
         with torch.no_grad():
            __snake_case					:   Any  				=			model(**self._prepare_for_class(__a       , __a))

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

         __snake_case					:   Optional[int]  				=			outputs.attentions

         self.assertEqual(len(__a)       , __a)

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







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   Tuple  				=			model_class(__a)
      model.to(__a)
      model.eval()

      with torch.no_grad():
         __snake_case					:   List[Any]  				=			model(**self._prepare_for_class(__a       , __a))

      __snake_case					:   Union[str, Any]  				=			outputs.hidden_states

      __snake_case					:   str  				=			getattr(
          self.model_tester       , 'expected_num_hidden_layers'       , len(self.model_tester.depths) + 1)
      self.assertEqual(len(__a)       , __a)

      # Swin has a different seq_length
      __snake_case					:   Optional[Any]  				=			(
          config.patch_size
          if isinstance(config.patch_size       , collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      __snake_case					:   Dict  				=			(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":
         __snake_case					:   int  				=			outputs.reshaped_hidden_states
         self.assertEqual(len(__a)       , __a)

         __snake_case				,__snake_case				,__snake_case				,__snake_case					:   Any  				=			reshaped_hidden_states[0].shape
         __snake_case					:   Union[str, Any]  				=			(
             reshaped_hidden_states[0].view(__a       , __a       , height * width).permute(0       , 2       , 1)
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:])       , [num_patches, self.model_tester.embed_dim]       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case				,__snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs_for_common()

      __snake_case					:   Optional[int]  				=			(
          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:
         __snake_case					:   Any  				=			True
         self.check_hidden_states_output(__a       , __a       , __a       , __a)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __snake_case					:   Dict  				=			True

         self.check_hidden_states_output(__a       , __a       , __a       , __a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case				,__snake_case					:   int  				=			self.model_tester.prepare_config_and_inputs_for_common()
      __snake_case					:   Tuple  				=			3

      __snake_case					:   int  				=			(
          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)
      )
      __snake_case					:   List[str]  				=			(
          config.patch_size
          if isinstance(config.patch_size       , collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

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

      for model_class in self.all_model_classes:
         __snake_case					:   Optional[int]  				=			True
         self.check_hidden_states_output(__a       , __a       , __a       , (padded_height, padded_width))

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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   int  				=			SwinModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case				,__snake_case					:   Dict  				=			self.model_tester.prepare_config_and_inputs_for_common()

      __snake_case					:   int  				=			_config_zero_init(__a)
      for model_class in self.all_model_classes:
         __snake_case					:   Optional[int]  				=			model_class(config=__a)
         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 a_     (     unittest.TestCase				):







   @cached_property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






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







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   List[str]  				=			SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224').to(__a)
      __snake_case					:   Union[str, Any]  				=			self.default_image_processor

      __snake_case					:   Tuple  				=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
      __snake_case					:   Dict  				=			image_processor(images=__a       , return_tensors='pt').to(__a)

      # forward pass
      with torch.no_grad():
         __snake_case					:   List[str]  				=			model(**__a)

      # verify the logits
      __snake_case					:   Any  				=			torch.Size((1, 1_0_0_0))
      self.assertEqual(outputs.logits.shape       , __a)
      __snake_case					:   Optional[int]  				=			torch.tensor([-0.0_948, -0.6_454, -0.0_921]).to(__a)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]       , __a       , atol=1E-4))




@require_torch
class a_     (     unittest.TestCase		,   UpperCamelCase_				):
   _snake_case	   =				(SwinBackbone,) if is_torch_available() else ()
   _snake_case	   =				SwinConfig







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			SwinModelTester(self)







'''simple docstring'''






def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(a + 1	,		4_00	):
         for c in range(b + 1	,		5_00	):
            if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
               return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(4_00	):
         __snake_case					:   Optional[Any]  				=			10_00 - a - b
         if a < b < c and (a**2) + (b**2) == (c**2):
            return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			None:







   """simple docstring"""
   import timeit

   print(
       timeit.timeit('solution()'	,		setup='from __main__ import solution'	,		number=10_00	)	)
   print(
       timeit.timeit(
           'solution_fast()'	,		setup='from __main__ import solution_fast'	,		number=10_00	)	)


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