


'''simple docstring'''






def 	_A				(lowerCAmelCase__  :int = 60_08_51_47_51_43		)   ->  int:




					'''simple docstring'''



					try:
										_a			      =							int(lowerCAmelCase__		)
					except (TypeError, ValueError):
										raise TypeError('Parameter n must be int or castable to int.'		)
					if n <= 0:
										raise ValueError('Parameter n must be greater than or equal to one.'		)
					_a			      =							2
					_a			      =							0
					if n == 2:
										return 2
					while n > 2:
										while n % i != 0:
															i += 1
										_a			      =							i
										while n % i == 0:
															_a			      =							n // i
										i += 1
					return int(lowerCAmelCase__		)


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





'''simple docstring'''






import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




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

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

      _a			      =							self.get_config()

      return config, pixel_values, labels
 def 							__UpperCAmelCase		( self						)					->							int:
      return SwinvaConfig(
          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      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							SwinvaModel(config=__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)

      _a			      =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
      _a			      =							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      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							str:
      _a			      =							SwinvaForMaskedImageModeling(config=__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(
          result.logits.shape      ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

      # test greyscale images
      _a			      =							1
      _a			      =							SwinvaForMaskedImageModeling(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()

      _a			      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
      _a			      =							model(__magic_name__						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, 1, self.image_size, self.image_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      _a			      =							self.type_sequence_label_size
      _a			      =							SwinvaForImageClassification(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()
      _a			      =							model(__magic_name__      ,				labels=__magic_name__						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)						)




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





@require_torch
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	(
     (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
 )
 _lowerCAmelCase 					=	(
     {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							SwinvaModelTester(self						)
      _a			      =							ConfigTester(self      ,				config_class=__magic_name__      ,				embed_dim=37						)
 def 							__UpperCAmelCase		( self						)					->							int:
      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						)					->							Optional[Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__						)
 @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      pass
 @unittest.skip(reason='Swinv2 does not use inputs_embeds'						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      pass
 def 							__UpperCAmelCase		( self						)					->							str:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a			      =							model_class(__magic_name__						)
           self.assertIsInstance(model.get_input_embeddings()      ,				(nn.Module)						)
           _a			      =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__magic_name__      ,				nn.Linear						)						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

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

           _a			      =							['pixel_values']
           self.assertListEqual(arg_names[:1]      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a			      =							True

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

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

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

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

           if hasattr(self.model_tester      ,				'num_hidden_states_types'						):
                _a			      =							self.model_tester.num_hidden_states_types
           else:
                # also another +1 for reshaped_hidden_states
                _a			      =							2
           self.assertEqual(out_len + added_hidden_states      ,				len(__magic_name__						)						)

           _a			      =							outputs.attentions

           self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

           self.assertListEqual(
               list(self_attentions[0].shape[-3:]						)      ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,				)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							List[Any]:
      _a			      =							model_class(__magic_name__						)
      model.to(__magic_name__						)
      model.eval()

      with torch.no_grad():
           _a			      =							model(**self._prepare_for_class(__magic_name__      ,				__magic_name__						)						)

      _a			      =							outputs.hidden_states

      _a			      =							getattr(
          self.model_tester      ,				'expected_num_hidden_layers'      ,				len(self.model_tester.depths						) + 1						)
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

      # Swinv2 has a different seq_length
      _a			      =							(
          config.patch_size
          if isinstance(config.patch_size      ,				collections.abc.Iterable						)
          else (config.patch_size, config.patch_size)
      )

      _a			      =							(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]      ,				)

      _a			      =							outputs.reshaped_hidden_states
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)

      _a      ,   _a      ,   _a      ,   _a			      =							reshaped_hidden_states[0].shape
      _a			      =							(
          reshaped_hidden_states[0].view(__magic_name__      ,				__magic_name__      ,				height * width						).permute(0      ,				2      ,				1						)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]						)      ,				[num_patches, self.model_tester.embed_dim]      ,				)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      _a			      =							(
          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			      =							True
           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)

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

           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()
      _a			      =							3

      _a			      =							(
          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			      =							(
          config.patch_size
          if isinstance(config.patch_size      ,				collections.abc.Iterable						)
          else (config.patch_size, config.patch_size)
      )

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

      for model_class in self.all_model_classes:
           _a			      =							True
           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				(padded_height, padded_width)						)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _a			      =							True
           self.check_hidden_states_output(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				(padded_height, padded_width)						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__magic_name__						)
 @slow
 def 							__UpperCAmelCase		( self						)					->							str:
      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a			      =							SwinvaModel.from_pretrained(__magic_name__						)
           self.assertIsNotNone(__magic_name__						)




 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a      ,   _a			      =							self.model_tester.prepare_config_and_inputs_for_common()

      _a			      =							_config_zero_init(__magic_name__						)
      for model_class in self.all_model_classes:
           _a			      =							model_class(config=__magic_name__						)
           for name, param in model.named_parameters():
                if "embeddings" not in name and "logit_scale" 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 							__UpperCAmelCase		( self						)					->							Optional[int]:
      return (
          AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						)
          if is_vision_available()
          else None
      )




 @slow
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						).to(
          __magic_name__						)
      _a			      =							self.default_image_processor

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

      # forward pass
      with torch.no_grad():
           _a			      =							model(**__magic_name__						)

      # verify the logits
      _a			      =							torch.Size((1, 10_00)						)
      self.assertEqual(outputs.logits.shape      ,				__magic_name__						)
      _a			      =							torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]						).to(__magic_name__						)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				__magic_name__      ,				atol=1e-4						)						)

