




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor




class 		__A      (			unittest.TestCase			):

						def __init__(		self   :Dict    , __snake_case   :Dict    , __snake_case   :int=7    , __snake_case   :Optional[int]=3    , __snake_case   :str=18    , __snake_case   :Tuple=30    , __snake_case   :str=4_00    , __snake_case   :List[Any]=True    , __snake_case   :int=None    , __snake_case   :List[str]=True    , __snake_case   :int=None    , __snake_case   :Union[str, Any]=True    , __snake_case   :Tuple=[0.5, 0.5, 0.5]    , __snake_case   :str=[0.5, 0.5, 0.5]    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=size if size is not None else {"""shortest_edge""": 18}
										__magic_name__	:	List[Any]								=crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										__magic_name__	:	Any								=parent
										__magic_name__	:	Optional[Any]								=batch_size
										__magic_name__	:	List[Any]								=num_channels
										__magic_name__	:	int								=image_size
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	str								=max_resolution
										__magic_name__	:	Optional[int]								=do_resize
										__magic_name__	:	Optional[Any]								=size
										__magic_name__	:	Tuple								=do_center_crop
										__magic_name__	:	Tuple								=crop_size
										__magic_name__	:	List[str]								=do_normalize
										__magic_name__	:	Dict								=image_mean
										__magic_name__	:	str								=image_std


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

										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "do_center_crop": self.do_center_crop,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					LevitImageProcessor if is_vision_available() else None

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

										__magic_name__	:	Tuple								=LevitImageProcessingTester(self )

						@property
						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

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

										__magic_name__	:	Union[str, Any]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_center_crop""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

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

										__magic_name__	:	Tuple								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18} )
										self.assertEqual(image_processor.crop_size    , {"""height""": 18, """width""": 18} )

										__magic_name__	:	Any								=self.image_processing_class.from_dict(self.image_processor_dict    , size=42    , crop_size=84 )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 42} )
										self.assertEqual(image_processor.crop_size    , {"""height""": 84, """width""": 84} )

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

										pass

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

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	str								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

										# Test not batched input
										__magic_name__	:	Tuple								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	Optional[int]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )

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

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	int								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

										# Test not batched input
										__magic_name__	:	Any								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	Tuple								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )


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

										__magic_name__	:	Union[str, Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	List[Any]								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

										# Test not batched input
										__magic_name__	:	Dict								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )

										# Test batched
										__magic_name__	:	List[Any]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values
										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )    , )










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)")




