



"""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       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=13						,	SCREAMING_SNAKE_CASE__=30						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=37						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=None						,	)      ->	List[str]:
				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   snake_case__	(      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   snake_case__	(      self		)      ->	List[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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					ViTMSNModel(config=SCREAMING_SNAKE_CASE__		)
				model.to(SCREAMING_SNAKE_CASE__		)
				model.eval()
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					self.type_sequence_label_size
				A__											=					ViTMSNForImageClassification(SCREAMING_SNAKE_CASE__		)
				model.to(SCREAMING_SNAKE_CASE__		)
				model.eval()
				A__											=					model(SCREAMING_SNAKE_CASE__						,	labels=SCREAMING_SNAKE_CASE__		)
				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(SCREAMING_SNAKE_CASE__		)
				model.to(SCREAMING_SNAKE_CASE__		)
				model.eval()

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







			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					self.prepare_config_and_inputs()
				A__							,							A__							,							A__											=					config_and_inputs
				A__											=					{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


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

			A__	:					Dict							  =     False
			A__	:					Optional[int]							  =     False
			A__	:					Union[str, Any]							  =     False
			A__	:					int							  =     False


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					ViTMSNModelTester(self		)
				A__											=					ConfigTester(self						,	config_class=SCREAMING_SNAKE_CASE__						,	has_text_modality=SCREAMING_SNAKE_CASE__						,	hidden_size=37		)


			def   snake_case__	(      self		)      ->	Dict:
				self.config_tester.run_common_tests()


			@unittest.skip(reason="ViTMSN does not use inputs_embeds"		)
			def   snake_case__	(      self		)      ->	List[Any]:
				pass


			def   snake_case__	(      self		)      ->	List[str]:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

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


			def   snake_case__	(      self		)      ->	Tuple:
				A__							,							A__											=					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					A__											=					model_class(SCREAMING_SNAKE_CASE__		)
					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]						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__		)







			@slow
			def   snake_case__	(      self		)      ->	Tuple:
				for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					A__											=					ViTMSNModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)



def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
	return image






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

			"""simple docstring"""


			@cached_property
			def   snake_case__	(      self		)      ->	List[Any]:
				return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"		) if is_vision_available() else None







			@slow
			def   snake_case__	(      self		)      ->	Any:
				torch.manual_seed(2		)
				A__											=					ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"		).to(SCREAMING_SNAKE_CASE__		)

				A__											=					self.default_image_processor
				A__											=					prepare_img()
				A__											=					image_processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		).to(SCREAMING_SNAKE_CASE__		)

				# forward pass
				with torch.no_grad():
					A__											=					model(**SCREAMING_SNAKE_CASE__		)

				# verify the logits
				A__											=					torch.Size((1, 1000)		)
				self.assertEqual(outputs.logits.shape						,	SCREAMING_SNAKE_CASE__		)

				A__											=					torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]		).to(SCREAMING_SNAKE_CASE__		)

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












"""simple docstring"""
import operator as op





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    []
				_SCREAMING_SNAKE_CASE   :		Dict       		=    lambda lowerCamelCase,						lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8       ),						'Action'.center(12       ),						'Stack',						sep=' | '       )
				print('-' * (30 + len(lowerCamelCase       ))       )

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowerCamelCase       )  # append x to stack
												# output in tabular format
												print(x.rjust(8       ),						('push(' + x + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												_SCREAMING_SNAKE_CASE   :		Tuple       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + a + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												stack.append(
												    str(opr[x](int(lowerCamelCase       ),						int(lowerCamelCase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8       ),						('push(' + a + x + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | ',						)

				return int(stack[0]       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
			print('\n\tResult = ', solve(Postfix))

