




'''simple docstring'''

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

import numpy as np

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


a    :							int										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):
				snake_case_           =						["pixel_values"]


				def __init__(						self       :   List[str]			,							lowercase_       :   bool = True			,							lowercase_       :   Dict[str, int] = None			,							lowercase_       :   PILImageResampling = PILImageResampling.BICUBIC			,							lowercase_       :   bool = True			,							lowercase_       :   Dict[str, int] = None			,							lowercase_       :   bool = True			,							lowercase_       :   Union[int, float] = 1 / 255			,							lowercase_       :   bool = True			,							lowercase_       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN			,							lowercase_       :   Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD			,							**lowercase_       :   str			,							):
									super().__init__(**lowercase_   )
									snake_case_		     =							size if size is not None else {'''shortest_edge''': 224}
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_   )
									snake_case_		     =							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''crop_size'''   )

									snake_case_		     =							do_resize
									snake_case_		     =							size
									snake_case_		     =							resample
									snake_case_		     =							do_center_crop
									snake_case_		     =							crop_size
									snake_case_		     =							do_rescale
									snake_case_		     =							rescale_factor
									snake_case_		     =							do_normalize
									snake_case_		     =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									snake_case_		     =							image_std if image_std is not None else IMAGENET_DEFAULT_STD


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   np.ndarray			,							lowercase_       :   Dict[str, int]			,							lowercase_       :   PILImageResampling = PILImageResampling.BICUBIC			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   List[str]			,							):
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_   )
									# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
									if "shortest_edge" in size:
														snake_case_		     =							int((256 / 224) * size['''shortest_edge''']   )
														snake_case_		     =							get_resize_output_image_size(lowercase_			,							size=lowercase_			,							default_to_square=lowercase_   )
														snake_case_		     =							{'''height''': output_size[0], '''width''': output_size[1]}
									if "height" not in size_dict or "width" not in size_dict:
														raise ValueError(
														    F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"   )
									return resize(
									    lowercase_			,							size=(size_dict['''height'''], size_dict['''width'''])			,							resample=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   np.ndarray			,							lowercase_       :   Dict[str, int]			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Dict			,							):
									snake_case_		     =							get_size_dict(lowercase_   )
									if "height" not in size or "width" not in size:
														raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}"   )
									return center_crop(lowercase_			,							size=(size['''height'''], size['''width'''])			,							data_format=lowercase_			,							**lowercase_   )


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


				def     A_					(						self       :   str			,							lowercase_       :   np.ndarray			,							lowercase_       :   Union[float, List[float]]			,							lowercase_       :   Union[float, List[float]]			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Optional[Any]			,							):
									return normalize(lowercase_			,							mean=lowercase_			,							std=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   ImageInput			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[Dict[str, int]] = None			,							lowercase_       :   PILImageResampling = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[Dict[str, int]] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[float] = None			,							lowercase_       :   Optional[bool] = None			,							lowercase_       :   Optional[Union[float, Iterable[float]]] = None			,							lowercase_       :   Optional[Union[float, Iterable[float]]] = None			,							lowercase_       :   Optional[TensorType] = None			,							lowercase_       :   ChannelDimension = ChannelDimension.FIRST			,							**lowercase_       :   int			,							):
									snake_case_		     =							do_resize if do_resize is not None else self.do_resize
									snake_case_		     =							resample if resample is not None else self.resample
									snake_case_		     =							do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_		     =							do_rescale if do_rescale is not None else self.do_rescale
									snake_case_		     =							rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_		     =							do_normalize if do_normalize is not None else self.do_normalize
									snake_case_		     =							image_mean if image_mean is not None else self.image_mean
									snake_case_		     =							image_std if image_std is not None else self.image_std

									snake_case_		     =							size if size is not None else self.size
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_   )
									snake_case_		     =							crop_size if crop_size is not None else self.crop_size
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''crop_size'''   )

									snake_case_		     =							make_list_of_images(lowercase_   )

									if not valid_images(lowercase_   ):
														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.'''   )

									# All transformations expect numpy arrays.
									snake_case_		     =							[to_numpy_array(lowercase_   ) for image in images]

									if do_resize:
														snake_case_		     =							[self.resize(lowercase_			,							lowercase_			,							lowercase_   ) for image in images]

									if do_center_crop:
														snake_case_		     =							[self.center_crop(lowercase_			,							lowercase_   ) for image in images]

									if do_rescale:
														snake_case_		     =							[self.rescale(lowercase_			,							lowercase_   ) for image in images]

									if do_normalize:
														snake_case_		     =							[self.normalize(lowercase_			,							lowercase_			,							lowercase_   ) for image in images]

									snake_case_		     =							[to_channel_dimension_format(lowercase_			,							lowercase_   ) for image in images]

									snake_case_		     =							{'''pixel_values''': images}
									return BatchFeature(data=lowercase_			,							tensor_type=lowercase_   )







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

