def 				_lowerCamelCase(					__snake_case    ,    __snake_case						)	-> Dict:



		if a < 0 or b < 0:
				raise ValueError("the value of both inputs must be positive"						)

		__snake_case									=  str(bin(__a						)						)[2:]  # remove the leading "0b"
		__snake_case									=  str(bin(__a						)						)[2:]  # remove the leading "0b"

		__snake_case									=  max(len(__a						)    ,    len(__a						)						)

		return "0b" + "".join(
		    str(int(char_a == "1" and char_b == "1"						)						)
		    for char_a, char_b in zip(a_binary.zfill(__a						)    ,    b_binary.zfill(__a						)						)						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()










"""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=0.0_2				,   A=None				,   ):
						_lowerCamelCase					:					Dict	       =    parent
						_lowerCamelCase					:					List[Any]	       =    batch_size
						_lowerCamelCase					:					int	       =    image_size
						_lowerCamelCase					:					List[Any]	       =    patch_size
						_lowerCamelCase					:					Optional[int]	       =    num_channels
						_lowerCamelCase					:					Union[str, Any]	       =    is_training
						_lowerCamelCase					:					Optional[Any]	       =    use_labels
						_lowerCamelCase					:					Dict	       =    hidden_size
						_lowerCamelCase					:					Tuple	       =    num_hidden_layers
						_lowerCamelCase					:					List[Any]	       =    num_attention_heads
						_lowerCamelCase					:					str	       =    intermediate_size
						_lowerCamelCase					:					str	       =    hidden_act
						_lowerCamelCase					:					str	       =    hidden_dropout_prob
						_lowerCamelCase					:					int	       =    attention_probs_dropout_prob
						_lowerCamelCase					:					Any	       =    type_sequence_label_size
						_lowerCamelCase					:					int	       =    initializer_range
						_lowerCamelCase					:					Union[str, Any]	       =    scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_lowerCamelCase					:					Dict	       =    (image_size // patch_size) ** 2
						_lowerCamelCase					:					Any	       =    num_patches + 1
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

						_lowerCamelCase					:					Dict	       =    self.get_config()

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self							):
						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    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					int	       =    ViTMSNModel(config=A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					List[Any]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					List[Any]	       =    self.type_sequence_label_size
						_lowerCamelCase					:					List[str]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					Optional[Any]	       =    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
						_lowerCamelCase					:					Optional[Any]	       =    1
						_lowerCamelCase					:					List[Any]	       =    ViTMSNForImageClassification(A							)
						model.to(A							)
						model.eval()

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

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.prepare_config_and_inputs()
						_lowerCamelCase       ,							_lowerCamelCase       ,							_lowerCamelCase					:					Tuple	       =    config_and_inputs
						_lowerCamelCase					:					List[str]	       =    {'pixel_values': pixel_values}
						return config, inputs_dict







@require_torch
class 					A_(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			Union[str, Any]												=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			a_       :			Union[str, Any]												=			(
			    {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			a_       :			Tuple												=			False
			a_       :			Union[str, Any]												=			False
			a_       :			Tuple												=			False
			a_       :			List[str]												=			False
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    ViTMSNModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37							)
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			@unittest.skip(reason='ViTMSN does not use inputs_embeds'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Optional[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					List[str]	       =    model_class(A							)
									self.assertIsInstance(model.get_input_embeddings()				,   (nn.Module)							)
									_lowerCamelCase					:					Dict	       =    model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,   nn.Linear							)							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase       ,							_lowerCamelCase					:					List[str]	       =    self.model_tester.prepare_config_and_inputs_for_common()

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

									_lowerCamelCase					:					Union[str, Any]	       =    ['pixel_values']
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)

			@slow
			def    _lowerCAmelCase		(	self							):
						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCamelCase					:					Any	       =    ViTMSNModel.from_pretrained(A							)
									self.assertIsNotNone(A							)






def     UpperCAmelCase_					(				):




			'''simple docstring'''



			_lowerCamelCase					:					Optional[Any]	       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
			return image







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







			"""simple docstring"""



			@cached_property
			def    _lowerCAmelCase		(	self							):
						return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'							) if is_vision_available() else None

			@slow
			def    _lowerCAmelCase		(	self							):
						torch.manual_seed(2							)
						_lowerCamelCase					:					Tuple	       =    ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'							).to(A							)

						_lowerCamelCase					:					int	       =    self.default_image_processor
						_lowerCamelCase					:					Optional[int]	       =    prepare_img()
						_lowerCamelCase					:					int	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

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

						# verify the logits
						_lowerCamelCase					:					Optional[Any]	       =    torch.Size((1, 1000)							)
						self.assertEqual(outputs.logits.shape				,   A							)

						_lowerCamelCase					:					Any	       =    torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]							).to(A							)

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





