



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__					  =  logging.get_logger(__name__)


if is_vision_available():
    import PIL

class 				snake_case__(_UpperCamelCase      ):

     """simple docstring"""






     lowercase_								=		["""pixel_values"""]







     def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BICUBIC				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     bool = True				,       **SCREAMING_SNAKE_CASE :     Optional[Any]				,       ):
         super().__init__(**SCREAMING_SNAKE_CASE					)
         lowercase__					:   List[Any]													=					size if size is not None else {"shortest_edge": 224}
         lowercase__					:   Any													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)
         lowercase__					:   Union[str, Any]													=					crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowercase__					:   Tuple													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE				,       param_name="crop_size"					)

         lowercase__					:   List[Any]													=					do_resize
         lowercase__					:   List[Any]													=					size
         lowercase__					:   Dict													=					resample
         lowercase__					:   int													=					do_center_crop
         lowercase__					:   List[str]													=					crop_size
         lowercase__					:   Dict													=					do_rescale
         lowercase__					:   Any													=					rescale_factor
         lowercase__					:   Optional[int]													=					do_normalize
         lowercase__					:   List[Any]													=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__					:   List[str]													=					image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__					:   List[str]													=					do_convert_rgb







     def 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BICUBIC				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
         lowercase__					:   Any													=					get_size_dict(SCREAMING_SNAKE_CASE				,       default_to_square=SCREAMING_SNAKE_CASE					)
         if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
         lowercase__					:   Union[str, Any]													=					get_resize_output_image_size(SCREAMING_SNAKE_CASE				,       size=size["shortest_edge"]				,       default_to_square=SCREAMING_SNAKE_CASE					)
         return resize(SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







     def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
         lowercase__					:   Tuple													=					get_size_dict(SCREAMING_SNAKE_CASE					)
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
         return center_crop(SCREAMING_SNAKE_CASE				,       size=(size["height"], size["width"])				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







     def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[int, float]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     str				,       ):
         return rescale(SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







     def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     int				,       ):
         return normalize(SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE				,       data_format=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)







     def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     Optional[ChannelDimension] = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]				,       ):
         lowercase__					:   Optional[Any]													=					do_resize if do_resize is not None else self.do_resize
         lowercase__					:   Dict													=					size if size is not None else self.size
         lowercase__					:   Union[str, Any]													=					get_size_dict(SCREAMING_SNAKE_CASE				,       param_name="size"				,       default_to_square=SCREAMING_SNAKE_CASE					)
         lowercase__					:   Optional[Any]													=					resample if resample is not None else self.resample
         lowercase__					:   Optional[Any]													=					do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__					:   str													=					crop_size if crop_size is not None else self.crop_size
         lowercase__					:   Any													=					get_size_dict(SCREAMING_SNAKE_CASE				,       param_name="crop_size"				,       default_to_square=SCREAMING_SNAKE_CASE					)
         lowercase__					:   Any													=					do_rescale if do_rescale is not None else self.do_rescale
         lowercase__					:   int													=					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__					:   str													=					do_normalize if do_normalize is not None else self.do_normalize
         lowercase__					:   Optional[int]													=					image_mean if image_mean is not None else self.image_mean
         lowercase__					:   Dict													=					image_std if image_std is not None else self.image_std
         lowercase__					:   Optional[Any]													=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__					:   List[Any]													=					make_list_of_images(SCREAMING_SNAKE_CASE					)

         if not valid_images(SCREAMING_SNAKE_CASE					):
             raise ValueError(
                 "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                 "torch.Tensor, tf.Tensor or jax.ndarray."					)

         if do_resize and size is None:
             raise ValueError("Size must be specified if do_resize is True."					)

         if do_center_crop and crop_size is None:
             raise ValueError("Crop size must be specified if do_center_crop is True."					)

         if do_rescale and rescale_factor is None:
             raise ValueError("Rescale factor must be specified if do_rescale is True."					)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError("Image mean and std must be specified if do_normalize is True."					)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
             lowercase__					:   str													=					[convert_to_rgb(SCREAMING_SNAKE_CASE					) for image in images]

         # All transformations expect numpy arrays.
         lowercase__					:   str													=					[to_numpy_array(SCREAMING_SNAKE_CASE					) for image in images]

         if do_resize:
             lowercase__					:   Union[str, Any]													=					[self.resize(image=SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE				,       resample=SCREAMING_SNAKE_CASE					) for image in images]

         if do_center_crop:
             lowercase__					:   Dict													=					[self.center_crop(image=SCREAMING_SNAKE_CASE				,       size=SCREAMING_SNAKE_CASE					) for image in images]

         if do_rescale:
             lowercase__					:   Union[str, Any]													=					[self.rescale(image=SCREAMING_SNAKE_CASE				,       scale=SCREAMING_SNAKE_CASE					) for image in images]

         if do_normalize:
             lowercase__					:   List[str]													=					[self.normalize(image=SCREAMING_SNAKE_CASE				,       mean=SCREAMING_SNAKE_CASE				,       std=SCREAMING_SNAKE_CASE					) for image in images]

         lowercase__					:   Union[str, Any]													=					[to_channel_dimension_format(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) for image in images]

         lowercase__					:   Any													=					{"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE				,       tensor_type=SCREAMING_SNAKE_CASE					)









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					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__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

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


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




