





import inspect
import unittest

from transformers import ViTMSNConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class    lowerCAmelCase							:
 def __init__(      self  :      Optional[int]			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      Union[str, Any]=13			,     UpperCAmelCase  :      Dict=30			,     UpperCAmelCase  :      str=2			,     UpperCAmelCase  :      Tuple=3			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      Dict=True			,     UpperCAmelCase  :      Optional[Any]=32			,     UpperCAmelCase  :      Optional[Any]=5			,     UpperCAmelCase  :      List[Any]=4			,     UpperCAmelCase  :      Optional[int]=37			,     UpperCAmelCase  :      Dict="gelu"			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      Union[str, Any]=10			,     UpperCAmelCase  :      Union[str, Any]=0.0_2			,     UpperCAmelCase  :      Optional[Any]=None			,     )			->	Union[str, Any]:
     lowerCamelCase__    :      Any					 =		parent
     lowerCamelCase__    :      Optional[int]					 =		batch_size
     lowerCamelCase__    :      int					 =		image_size
     lowerCamelCase__    :      Tuple					 =		patch_size
     lowerCamelCase__    :      str					 =		num_channels
     lowerCamelCase__    :      Optional[int]					 =		is_training
     lowerCamelCase__    :      List[str]					 =		use_labels
     lowerCamelCase__    :      Dict					 =		hidden_size
     lowerCamelCase__    :      Optional[Any]					 =		num_hidden_layers
     lowerCamelCase__    :      Optional[int]					 =		num_attention_heads
     lowerCamelCase__    :      str					 =		intermediate_size
     lowerCamelCase__    :      int					 =		hidden_act
     lowerCamelCase__    :      Tuple					 =		hidden_dropout_prob
     lowerCamelCase__    :      List[Any]					 =		attention_probs_dropout_prob
     lowerCamelCase__    :      Optional[Any]					 =		type_sequence_label_size
     lowerCamelCase__    :      int					 =		initializer_range
     lowerCamelCase__    :      Optional[int]					 =		scope

     # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     lowerCamelCase__    :      Tuple					 =		(image_size // patch_size) ** 2
     lowerCamelCase__    :      Dict					 =		num_patches + 1
 def      A_	(      self  :      Optional[Any]	)			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

     lowerCamelCase__    :      Tuple					 =		None
     if self.use_labels:
         lowerCamelCase__    :      str					 =		ids_tensor([self.batch_size]			,     self.type_sequence_label_size	)

     lowerCamelCase__    :      Dict					 =		self.get_config()

     return config, pixel_values, labels
 def      A_	(      self  :      Union[str, Any]	)			->	Union[str, Any]:
     return ViTMSNConfig(
         image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     initializer_range=self.initializer_range			,     )
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      List[str]			,     UpperCAmelCase  :      Dict			,     UpperCAmelCase  :      Dict	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		ViTMSNModel(config=UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Dict					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Any	)			->	List[Any]:
     lowerCamelCase__    :      List[Any]					 =		self.type_sequence_label_size
     lowerCamelCase__    :      Dict					 =		ViTMSNForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()
     lowerCamelCase__    :      Tuple					 =		model(UpperCAmelCase			,     labels=UpperCAmelCase	)
     print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'	)
     print('Labels: {labels}'	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)

     # test greyscale images
     lowerCamelCase__    :      Optional[int]					 =		1
     lowerCamelCase__    :      Tuple					 =		ViTMSNForImageClassification(UpperCAmelCase	)
     model.to(UpperCAmelCase	)
     model.eval()

     lowerCamelCase__    :      str					 =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     lowerCamelCase__    :      Any					 =		model(UpperCAmelCase	)
     self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)	)







 def      A_	(      self  :      List[str]	)			->	List[Any]:
     lowerCamelCase__    :      List[Any]					 =		self.prepare_config_and_inputs()
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		config_and_inputs
     lowerCamelCase__    :      Optional[int]					 =		{'pixel_values': pixel_values}
     return config, inputs_dict


@require_torch
class    lowerCAmelCase							(					__UpperCamelCase,  __UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
 UpperCAmelCase__													=		(
     {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 UpperCAmelCase__													=		False
 def      A_	(      self  :      Any	)			->	Union[str, Any]:
     lowerCamelCase__    :      Any					 =		ViTMSNModelTester(self	)
     lowerCamelCase__    :      List[str]					 =		ConfigTester(self			,     config_class=UpperCAmelCase			,     has_text_modality=UpperCAmelCase			,     hidden_size=37	)
 def      A_	(      self  :      int	)			->	str:
     self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMSN does not use inputs_embeds'	)
 def      A_	(      self  :      Optional[int]	)			->	Any:
     pass
 def      A_	(      self  :      Optional[int]	)			->	str:
     lowerCamelCase__ ,							lowerCamelCase__    :      Any					 =		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         lowerCamelCase__    :      int					 =		model_class(UpperCAmelCase	)
         self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)	)
         lowerCamelCase__    :      Union[str, Any]					 =		model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(UpperCAmelCase			,     nn.Linear	)	)
 def      A_	(      self  :      List[str]	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		self.model_tester.prepare_config_and_inputs_for_common()

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

         lowerCamelCase__    :      str					 =		['pixel_values']
         self.assertListEqual(arg_names[:1]			,     UpperCAmelCase	)
 def      A_	(      self  :      Tuple	)			->	Dict:
     lowerCamelCase__    :      int					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCAmelCase	)
 def      A_	(      self  :      Union[str, Any]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)







 @slow
 def      A_	(      self  :      int	)			->	Any:
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCamelCase__    :      List[Any]					 =		ViTMSNModel.from_pretrained(UpperCAmelCase	)
         self.assertIsNotNone(UpperCAmelCase	)


def        SCREAMING_SNAKE_CASE      (       )			->     Union[str, Any]:
    lowerCamelCase__    :      Optional[Any]					 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
    return image


@require_torch
@require_vision
class    lowerCAmelCase							(					unittest.TestCase			):
 @cached_property
 def      A_	(      self  :      Dict	)			->	List[str]:
     return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'	) if is_vision_available() else None







 @slow
 def      A_	(      self  :      Optional[int]	)			->	List[str]:
     torch.manual_seed(2	)
     lowerCamelCase__    :      Dict					 =		ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'	).to(UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		self.default_image_processor
     lowerCamelCase__    :      Any					 =		prepare_img()
     lowerCamelCase__    :      Tuple					 =		image_processor(images=UpperCAmelCase			,     return_tensors='pt'	).to(UpperCAmelCase	)

     # forward pass
     with torch.no_grad():
         lowerCamelCase__    :      str					 =		model(**UpperCAmelCase	)

     # verify the logits
     lowerCamelCase__    :      Any					 =		torch.Size((1, 1000)	)
     self.assertEqual(outputs.logits.shape			,     UpperCAmelCase	)

     lowerCamelCase__    :      int					 =		torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]	).to(UpperCAmelCase	)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     UpperCAmelCase			,     atol=1e-4	)	)









"""simple docstring"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  while b:
    lowerCAmelCase						,  lowerCAmelCase     :     Any								=							b, a % b
  return a
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNAKE_CASE				,      a % b				)
def 					a__			(				):


  '''simple docstring'''







  print(f"""euclidean_gcd(3, 5) = {euclidean_gcd(3				,      5				)}"""				)
  print(f"""euclidean_gcd(5, 3) = {euclidean_gcd(5				,      3				)}"""				)
  print(f"""euclidean_gcd(1, 3) = {euclidean_gcd(1				,      3				)}"""				)
  print(f"""euclidean_gcd(3, 6) = {euclidean_gcd(3				,      6				)}"""				)
  print(f"""euclidean_gcd(6, 3) = {euclidean_gcd(6				,      3				)}"""				)

  print(f"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3				,      5				)}"""				)
  print(f"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5				,      3				)}"""				)
  print(f"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1				,      3				)}"""				)
  print(f"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3				,      6				)}"""				)
  print(f"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6				,      3				)}"""				)


if __name__ == "__main__":
     main()





