



'''simple docstring'''





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 __A    :



	'''simple docstring'''







	def __init__(self				,     A				,     A=13				,     A=30				,     A=2				,     A=3				,     A=True				,     A=True				,     A=32				,     A=5				,     A=4				,     A=37				,     A="gelu"				,     A=0.1				,     A=0.1				,     A=10				,     A=0.02				,     A=None				,     )					->  int:


						"""simple docstring"""



						_a											=						parent
						_a											=						batch_size
						_a											=						image_size
						_a											=						patch_size
						_a											=						num_channels
						_a											=						is_training
						_a											=						use_labels
						_a											=						hidden_size
						_a											=						num_hidden_layers
						_a											=						num_attention_heads
						_a											=						intermediate_size
						_a											=						hidden_act
						_a											=						hidden_dropout_prob
						_a											=						attention_probs_dropout_prob
						_a											=						type_sequence_label_size
						_a											=						initializer_range
						_a											=						scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_a											=						(image_size // patch_size) ** 2
						_a											=						num_patches + 1



	def       a__						(self )					->  str:


						"""simple docstring"""



						_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       a__						(self )					->  Optional[Any]:


						"""simple docstring"""



						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				,     A				,     A				,     A )					->  List[str]:


						"""simple docstring"""



						_a											=						ViTMSNModel(config=A )
						model.to(A )
						model.eval()
						_a											=						model(A )
						self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



	def       a__						(self				,     A				,     A				,     A )					->  Optional[Any]:


						"""simple docstring"""



						_a											=						self.type_sequence_label_size
						_a											=						ViTMSNForImageClassification(A )
						model.to(A )
						model.eval()
						_a											=						model(A				,     labels=A )
						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
						_a											=						1
						_a											=						ViTMSNForImageClassification(A )
						model.to(A )
						model.eval()

						_a											=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
						_a											=						model(A )
						self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size) )



	def       a__						(self )					->  Union[str, Any]:


						"""simple docstring"""



						_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    (						A     ,     A     ,     unittest.TestCase		):



	'''simple docstring'''







	__lowerCamelCase					: str    = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	__lowerCamelCase					: int    = (
	    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__lowerCamelCase					: int    = False
	__lowerCamelCase					: List[str]    = False
	__lowerCamelCase					: Tuple    = False
	__lowerCamelCase					: str    = False



	def       a__						(self )					->  List[Any]:


						"""simple docstring"""



						_a											=						ViTMSNModelTester(self )
						_a											=						ConfigTester(self				,     config_class=A				,     has_text_modality=A				,     hidden_size=37 )



	def       a__						(self )					->  Dict:


						"""simple docstring"""



						self.config_tester.run_common_tests()



	@unittest.skip(reason='''ViTMSN does not use inputs_embeds''' )
	def       a__						(self )					->  Any:


						"""simple docstring"""



						pass



	def       a__						(self )					->  List[Any]:


						"""simple docstring"""



						_a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											_a											=						model_class(A )
											self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module) )
											_a											=						model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(A				,     nn.Linear ) )



	def       a__						(self )					->  int:


						"""simple docstring"""



						_a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											_a											=						model_class(A )
											_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]				,     A )



	def       a__						(self )					->  List[str]:


						"""simple docstring"""



						_a											=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A )



	def       a__						(self )					->  Dict:


						"""simple docstring"""



						_a											=						self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A )



	@slow
	def       a__						(self )					->  Tuple:


						"""simple docstring"""



						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_a											=						ViTMSNModel.from_pretrained(A )
											self.assertIsNotNone(A )





def lowerCAmelCase						():



					"""simple docstring"""



					_a											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
					return image






@require_torch
@require_vision
class __A    (						unittest.TestCase		):



	'''simple docstring'''







	@cached_property
	def       a__						(self )					->  List[str]:


						"""simple docstring"""



						return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''' ) if is_vision_available() else None



	@slow
	def       a__						(self )					->  Union[str, Any]:


						"""simple docstring"""



						torch.manual_seed(2 )
						_a											=						ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''' ).to(A )

						_a											=						self.default_image_processor
						_a											=						prepare_img()
						_a											=						image_processor(images=A				,     return_tensors='''pt''' ).to(A )

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

						# verify the logits
						_a											=						torch.Size((1, 1_000) )
						self.assertEqual(outputs.logits.shape				,     A )

						_a											=						torch.tensor([-0.0803, -0.4454, -0.2375] ).to(A )

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








'''simple docstring'''





def lowerCAmelCase						(__A = 1_000):



     """simple docstring"""



     _a											=						2**power
     _a											=						str(__A)
     _a											=						list(__A)
     _a											=						0

     for i in list_num:
          sum_of_num += int(__A)

     return sum_of_num


if __name__ == "__main__":
  lowercase_	 =						int(input("Enter the power of 2: ").strip())
  print("2 ^ ", power, " = ", 2**power)
  lowercase_	 =						solution(power)
  print("Sum of the digits is: ", result)



