






'''simple docstring'''







def     A__      (					__lowerCAmelCase		:      int    ):
						lowerCamelCase__								=	[]
						lowerCamelCase__								=	[]
						lowerCamelCase__								=	{
						    """^""": 3,
						    """*""": 2,
						    """/""": 2,
						    """%""": 2,
						    """+""": 1,
						    """-""": 1,
						}  # Priority of each operator
						lowerCamelCase__								=	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     A__      (					__lowerCAmelCase		:      Optional[int]    ):
						lowerCamelCase__								=	list(infix[::-1]    )  # reverse the infix equation

						for i in range(len(__UpperCamelCase    )    ):
												if infix[i] == "(":
																		lowerCamelCase__								=	""")"""  # change "(" to ")"
												elif infix[i] == ")":
																		lowerCamelCase__								=	"""("""  # change ")" to "("

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


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





'''simple docstring'''
import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCAmelCase						=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=18			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      Dict:
            _A				=							size if size is not None else {'height': 20, 'width': 20}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							image_size
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							size
            _A				=							do_normalize
            _A				=							do_convert_rgb
            _A				=							[512, 1024, 2048, 4096]
            _A				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}






       def   UpperCamelCase						( self   )->      Dict:
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
            _A				=							Image.open(requests.get(_UpperCamelCase			,     stream=_UpperCamelCase   ).raw   ).convert('RGB'   )
            return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							PixaStructImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processor_tester.prepare_dummy_image()

            _A				=							self.image_processing_class(**self.image_processor_dict   )
            _A				=							2048

            _A				=							image_processor(_UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   )
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,     torch.tensor(0.0606   )			,     atol=1e-3			,     rtol=1e-3   )   )






       def   UpperCamelCase						( self   )->      int:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            _A				=							True

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(_UpperCamelCase   ):
                      _A				=							image_processor(
                          image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches

                 _A				=							'Hello'

                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     np.ndarray   )

            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     torch.Tensor   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      str:
            _A				=							PixaStructImageProcessingTester(self			,     num_channels=4   )
            _A				=							3






       @property
       def   UpperCamelCase						( self   )->      str:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )





