


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor
class 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_=7 ,       snake_case_=3 ,       snake_case_=1_8 ,       snake_case_=3_0 ,       snake_case_=4_0_0 ,       snake_case_=True ,       snake_case_=None ,       snake_case_=True ,       snake_case_=False ,       snake_case_=True ,       snake_case_=True ,       snake_case_=[0.5, 0.5, 0.5] ,       snake_case_=[0.5, 0.5, 0.5] ,       )   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   parent
								__lowercase 					=   batch_size
								__lowercase 					=   num_channels
								__lowercase 					=   image_size
								__lowercase 					=   min_resolution
								__lowercase 					=   max_resolution
								__lowercase 					=   do_resize
								__lowercase 					=   size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase 					=   do_thumbnail
								__lowercase 					=   do_align_axis
								__lowercase 					=   do_pad
								__lowercase 					=   do_normalize
								__lowercase 					=   image_mean
								__lowercase 					=   image_std






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







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 	lowerCamelCase_       (				lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				DonutImageProcessor if is_vision_available() else None






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   DonutImageProcessingTester(self						)






					@property
					def A      (				self						)   ->			Optional[Any]:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






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







								'''simple docstring'''

								__lowercase 					=   self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(snake_case_ ,       '''do_resize'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''size'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''do_thumbnail'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''do_align_long_axis'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''do_pad'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''do_normalize'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''image_mean'''						)						)
								self.assertTrue(hasattr(snake_case_ ,       '''image_std'''						)						)






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







								'''simple docstring'''

								__lowercase 					=   self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size ,       {'''height''': 1_8, '''width''': 2_0}						)

								__lowercase 					=   self.image_processing_class.from_dict(self.image_processor_dict ,       size=4_2						)
								self.assertEqual(image_processor.size ,       {'''height''': 4_2, '''width''': 4_2}						)

								# Previous config had dimensions in (width, height) order
								__lowercase 					=   self.image_processing_class.from_dict(self.image_processor_dict ,       size=(4_2, 8_4)						)
								self.assertEqual(image_processor.size ,       {'''height''': 8_4, '''width''': 4_2}						)






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







								'''simple docstring'''

								pass






					@is_flaky()
					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								__lowercase 					=   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
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )

								# Test batched
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )






					@is_flaky()
					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								__lowercase 					=   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
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )

								# Test batched
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )






					@is_flaky()
					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								__lowercase 					=   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
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )

								# Test batched
								__lowercase 					=   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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ) ,       )






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   {
			    '''^''': 3,
			    '''*''': 2,
			    '''/''': 2,
			    '''%''': 2,
			    '''+''': 1,
			    '''-''': 1,
			}  # Priority of each operator
			__lowercase 					=   len(_UpperCamelCase  ) if (len(_UpperCamelCase  ) > 7) else 7

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

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

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

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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   list(infix[::-1]  )  # reverse the infix equation

			for i in range(len(_UpperCamelCase  )  ):
						if infix[i] == "(":
									__lowercase 					=   ''')'''  # change "(" to ")"
						elif infix[i] == ")":
									__lowercase 					=   '''('''  # change ")" to "("

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


if __name__ == "__main__":
	a : Union[str, Any]		=				input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
	a : Optional[Any]		=				''''''.join(Infix.split())  # Remove spaces from the input
	print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')


