
'''simple docstring'''


import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 3_2 ,   lowerCamelCase__ = True ,   lowerCamelCase__ = 1 / 2_5_5 ,   lowerCamelCase__ = True ,   lowerCamelCase__ = True ,   lowerCamelCase__ = [0.48145466, 0.4578275, 0.40821073] ,   lowerCamelCase__ = [0.26862954, 0.26130258, 0.27577711] ,   lowerCamelCase__ = True ,   lowerCamelCase__=7 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=3 ,   ):

      '''simple docstring'''







      UpperCamelCase       =  parent
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 2_8_8}
      UpperCamelCase       =  size_divisor
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_factor
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  do_center_crop
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std
      UpperCamelCase       =  do_pad
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      if not batched:
         UpperCamelCase       =  self.size['''shortest_edge''']
         UpperCamelCase       =  image_inputs[0]
         if isinstance(lowerCamelCase__ ,   Image.Image   ):
            UpperCamelCase     , UpperCamelCase       =  image.size
         else:
            UpperCamelCase     , UpperCamelCase       =  image.shape[1], image.shape[2]
         UpperCamelCase       =  size / min(lowerCamelCase__ ,   lowerCamelCase__   )
         if h < w:
            UpperCamelCase     , UpperCamelCase       =  size, scale * w
         else:
            UpperCamelCase     , UpperCamelCase       =  scale * h, size

         UpperCamelCase       =  int((1_3_3_3 / 8_0_0) * size   )
         if max(lowerCamelCase__ ,   lowerCamelCase__   ) > max_size:
            UpperCamelCase       =  max_size / max(lowerCamelCase__ ,   lowerCamelCase__   )
            UpperCamelCase       =  newh * scale
            UpperCamelCase       =  neww * scale

         UpperCamelCase     , UpperCamelCase       =  int(newh + 0.5   ), int(neww + 0.5   )
         UpperCamelCase     , UpperCamelCase       =  (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         UpperCamelCase       =  []
         for image in image_inputs:
            UpperCamelCase     , UpperCamelCase       =  self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
         UpperCamelCase       =  max(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[0]   )[0]
         UpperCamelCase       =  max(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[1]   )[1]

      return expected_height, expected_width




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							BridgeTowerImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  BridgeTowerImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_mean'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''image_std'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_normalize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''do_resize'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size'''   )   )
      self.assertTrue(hasattr(lowerCamelCase__ ,   '''size_divisor'''   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   Image.Image   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   numpify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   np.ndarray   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase__ ,   torchify=lowerCamelCase__   )
      for image in image_inputs:
         self.assertIsInstance(lowerCamelCase__ ,   torch.Tensor   )

      # Test not batched input
      UpperCamelCase       =  image_processing(image_inputs[0] ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,   )

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          ) ,   )





'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  []
   UpperCamelCase       =  []
   UpperCamelCase       =  {
       '''^''': 3,
       '''*''': 2,
       '''/''': 2,
       '''%''': 2,
       '''+''': 1,
       '''-''': 1,
   }  # Priority of each operator
   UpperCamelCase       =  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  __snake_case     (    _UpperCAmelCase					:       str):
   UpperCamelCase       =  list(infix[::-1])  # reverse the infix equation

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

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


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





