




import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
							from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor




class 		__A      (			UpperCamelCase__			):

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.config_class(**self.inputs_dict )
										self.parent.assertTrue(hasattr(__A    , """tf_padding""" ) )
										self.parent.assertTrue(hasattr(__A    , """depth_multiplier""" ) )




class 		__A      :

						def __init__(		self   :Any    , __snake_case   :Tuple    , __snake_case   :Optional[int]=13    , __snake_case   :Optional[Any]=3    , __snake_case   :Optional[Any]=32    , __snake_case   :Optional[Any]=0.25    , __snake_case   :Optional[Any]=8    , __snake_case   :Dict=8    , __snake_case   :str=6    , __snake_case   :Union[str, Any]=32    , __snake_case   :Any=True    , __snake_case   :int=True    , __snake_case   :List[Any]=True    , __snake_case   :Union[str, Any]="relu6"    , __snake_case   :Dict=12_80    , __snake_case   :Dict=0.1    , __snake_case   :List[str]=0.02    , __snake_case   :List[str]=True    , __snake_case   :List[Any]=True    , __snake_case   :Optional[Any]=10    , __snake_case   :Union[str, Any]=None    , ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=parent
										__magic_name__	:	Any								=batch_size
										__magic_name__	:	Union[str, Any]								=num_channels
										__magic_name__	:	Optional[int]								=image_size
										__magic_name__	:	str								=depth_multiplier
										__magic_name__	:	List[str]								=depth_divisible_by
										__magic_name__	:	Union[str, Any]								=min_depth
										__magic_name__	:	List[Any]								=expand_ratio
										__magic_name__	:	Tuple								=tf_padding
										__magic_name__	:	Optional[Any]								=output_stride
										__magic_name__	:	Any								=first_layer_is_expansion
										__magic_name__	:	Dict								=finegrained_output
										__magic_name__	:	int								=hidden_act
										__magic_name__	:	int								=last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
										__magic_name__	:	Tuple								=classifier_dropout_prob
										__magic_name__	:	List[str]								=use_labels
										__magic_name__	:	Optional[Any]								=is_training
										__magic_name__	:	Union[str, Any]								=num_labels
										__magic_name__	:	Dict								=initializer_range
										__magic_name__	:	Union[str, Any]								=scope

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

										__magic_name__	:	Union[str, Any]								=None
										__magic_name__	:	str								=None
										if self.use_labels:
														__magic_name__	:	List[str]								=ids_tensor([self.batch_size]    , self.num_labels )
														__magic_name__	:	Union[str, Any]								=ids_tensor([self.batch_size, self.image_size, self.image_size]    , self.num_labels )

										__magic_name__	:	str								=self.get_config()

										return config, pixel_values, labels, pixel_labels

						def     A__						(		self   :str ):
										'''simple docstring'''

										return MobileNetVaConfig(
										    num_channels=self.num_channels    , image_size=self.image_size    , depth_multiplier=self.depth_multiplier    , depth_divisible_by=self.depth_divisible_by    , min_depth=self.min_depth    , expand_ratio=self.expand_ratio    , output_stride=self.output_stride    , first_layer_is_expansion=self.first_layer_is_expansion    , finegrained_output=self.finegrained_output    , hidden_act=self.hidden_act    , tf_padding=self.tf_padding    , classifier_dropout_prob=self.classifier_dropout_prob    , initializer_range=self.initializer_range    , )

						def     A__						(		self   :List[str]    , __snake_case   :Union[str, Any]    , __snake_case   :List[Any]    , __snake_case   :int    , __snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	str								=MobileNetVaModel(config=__A )
										model.to(__A )
										model.eval()
										__magic_name__	:	List[str]								=model(__A )
										self.parent.assertEqual(
										    result.last_hidden_state.shape    , (
										        self.batch_size,
										        self.last_hidden_size,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )    , )
										self.parent.assertEqual(
										    result.pooler_output.shape    , (self.batch_size, self.last_hidden_size)    , )

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[int]    , __snake_case   :Union[str, Any]    , __snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.num_labels
										__magic_name__	:	Union[str, Any]								=MobileNetVaForImageClassification(__A )
										model.to(__A )
										model.eval()
										__magic_name__	:	str								=model(__A    , labels=__A )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )

						def     A__						(		self   :Optional[int]    , __snake_case   :Tuple    , __snake_case   :str    , __snake_case   :Dict    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.num_labels
										__magic_name__	:	str								=MobileNetVaForSemanticSegmentation(__A )
										model.to(__A )
										model.eval()
										__magic_name__	:	str								=model(__A )
										self.parent.assertEqual(
										    result.logits.shape    , (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )    , )
										__magic_name__	:	List[Any]								=model(__A    , labels=__A )
										self.parent.assertEqual(
										    result.logits.shape    , (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )    , )


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.prepare_config_and_inputs()
										__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Dict								=config_and_inputs
										__magic_name__	:	Any								={"""pixel_values""": pixel_values}
										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase     						=					(
						    {
						        """feature-extraction""": MobileNetVaModel,
						        """image-classification""": MobileNetVaForImageClassification,
						        """image-segmentation""": MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=MobileNetVaModelTester(self )
										__magic_name__	:	List[str]								=MobileNetVaConfigTester(self    , config_class=__A    , has_text_modality=__A )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds""" )
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings""" )
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""MobileNetV2 does not output attentions""" )
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__				, __magic_name__	:	Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()

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

														__magic_name__	:	int								=["""pixel_values"""]
														self.assertListEqual(arg_names[:1]    , __A )

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__A )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										def check_hidden_states_output(__snake_case   :Tuple    , __snake_case   :List[str]    , __snake_case   :Optional[Any] ):
														__magic_name__	:	str								=model_class(__A )
														model.to(__A )
														model.eval()

														with torch.no_grad():
																		__magic_name__	:	Union[str, Any]								=model(**self._prepare_for_class(__A    , __A ) )

														__magic_name__	:	str								=outputs.hidden_states

														__magic_name__	:	Optional[Any]								=16
														self.assertEqual(len(__A )    , __A )

										__magic_name__				, __magic_name__	:	List[str]								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__	:	List[Any]								=True
														check_hidden_states_output(__A    , __A    , __A )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														__magic_name__	:	int								=True

														check_hidden_states_output(__A    , __A    , __A )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__A )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__A )


						@slow
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__	:	Dict								=MobileNetVaModel.from_pretrained(__A )
														self.assertIsNotNone(__A )




def 		lowerCAmelCase_   (	):
				__magic_name__	:	str								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image




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

						@cached_property
						def     A__						(		self   :int ):
										'''simple docstring'''

										return (
										    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224""" ) if is_vision_available() else None
										)

						@slow
						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224""" ).to(__A )

										__magic_name__	:	Optional[int]								=self.default_image_processor
										__magic_name__	:	str								=prepare_img()
										__magic_name__	:	str								=image_processor(images=__A    , return_tensors="""pt""" ).to(__A )

										# forward pass
										with torch.no_grad():
														__magic_name__	:	Optional[int]								=model(**__A )

										# verify the logits
										__magic_name__	:	Union[str, Any]								=torch.Size((1, 10_01) )
										self.assertEqual(outputs.logits.shape    , __A )

										__magic_name__	:	Optional[Any]								=torch.tensor([0.2445, -1.1993, 0.1905] ).to(__A )

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


						@slow
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""" )
										__magic_name__	:	Dict								=model.to(__A )

										__magic_name__	:	List[Any]								=MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513""" )

										__magic_name__	:	Optional[int]								=prepare_img()
										__magic_name__	:	Dict								=image_processor(images=__A    , return_tensors="""pt""" ).to(__A )

										# forward pass
										with torch.no_grad():
														__magic_name__	:	Tuple								=model(**__A )
										__magic_name__	:	Union[str, Any]								=outputs.logits

										# verify the logits
										__magic_name__	:	Dict								=torch.Size((1, 21, 65, 65) )
										self.assertEqual(logits.shape    , __A )

										__magic_name__	:	int								=torch.tensor(
										    [
										        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
										        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
										        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
										    ]    , device=__A    , )

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









def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				__magic_name__	:	int								=[]
				__magic_name__	:	str								={
				    """^""": 3,
				    """*""": 2,
				    """/""": 2,
				    """%""": 2,
				    """+""": 1,
				    """-""": 1,
				}  # Priority of each operator
				__magic_name__	:	Dict								=len(lowerCamelCase   ) if (len(lowerCamelCase   ) > 7) else 7

				# Print table header for output
				print(
				    """Symbol""".center(8   )	,     """Stack""".center(lowerCamelCase   )	,     """Postfix""".center(lowerCamelCase   )	,     sep=""" | """	,     )
				print("""-""" * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase   ) == 0:
																stack.append(lowerCamelCase   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(lowerCamelCase   )  # push x to stack

								print(
								    x.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				while len(lowerCamelCase   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    """ """.center(8   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     ("""""".join(lowerCamelCase   )).ljust(lowerCamelCase   )	,     sep=""" | """	,     )  # Output in tabular format

				return "".join(lowerCamelCase   )  # return Postfix as str




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Union[str, Any]								=list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(lowerCamelCase   )   ):
								if infix[i] == "(":
												__magic_name__	:	str								=""")"""  # change "(" to ")"
								elif infix[i] == ")":
												__magic_name__	:	int								="""("""  # change ")" to "("

				return (infix_2_postfix("""""".join(lowerCamelCase   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     input("\nEnter an Infix Equation = ")  # Input an Infix equation
							UpperCAmelCase_		:				Dict									=     "".join(Infix.split())  # Remove spaces from the input
							print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")




