





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


_A					:				List[str]				=			logging.get_logger(__name__)


if is_vision_available():
  import PIL



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	["""pixel_values"""]



    def __init__(       self   ,				A_ = True   ,				A_ = None   ,				A_ = PILImageResampling.BICUBIC   ,				A_ = True   ,				A_ = None   ,				A_ = True   ,				A_ = 1 / 2_55   ,				A_ = True   ,				A_ = None   ,				A_ = None   ,				A_ = True   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			size if size is not None else {'''shortest_edge''': 2_24}
        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_   ,				default_to_square=UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_   ,				default_to_square=UpperCamelCase_   ,				param_name='''crop_size'''     )

        SCREAMING_SNAKE_CASE__											=			do_resize
        SCREAMING_SNAKE_CASE__											=			size
        SCREAMING_SNAKE_CASE__											=			resample
        SCREAMING_SNAKE_CASE__											=			do_center_crop
        SCREAMING_SNAKE_CASE__											=			crop_size
        SCREAMING_SNAKE_CASE__											=			do_rescale
        SCREAMING_SNAKE_CASE__											=			rescale_factor
        SCREAMING_SNAKE_CASE__											=			do_normalize
        SCREAMING_SNAKE_CASE__											=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE__											=			image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE__											=			do_convert_rgb



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = PILImageResampling.BICUBIC   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_   ,				default_to_square=UpperCamelCase_     )
        if "shortest_edge" not in size:
            raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
        SCREAMING_SNAKE_CASE__											=			get_resize_output_image_size(UpperCamelCase_   ,				size=size['''shortest_edge''']   ,				default_to_square=UpperCamelCase_     )
        return resize(UpperCamelCase_   ,				size=UpperCamelCase_   ,				resample=UpperCamelCase_   ,				data_format=UpperCamelCase_   ,				**UpperCamelCase_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_     )
        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(UpperCamelCase_   ,				size=(size['''height'''], size['''width'''])   ,				data_format=UpperCamelCase_   ,				**UpperCamelCase_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        return rescale(UpperCamelCase_   ,				scale=UpperCamelCase_   ,				data_format=UpperCamelCase_   ,				**UpperCamelCase_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_ = None   ,				**A_   ,				):





        '''simple docstring'''

        return normalize(UpperCamelCase_   ,				mean=UpperCamelCase_   ,				std=UpperCamelCase_   ,				data_format=UpperCamelCase_   ,				**UpperCamelCase_     )




    def  lowercase_					(       self   ,				A_   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = None   ,				A_ = ChannelDimension.FIRST   ,				**A_   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE__											=			size if size is not None else self.size
        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_   ,				param_name='''size'''   ,				default_to_square=UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE__											=			do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE__											=			crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE__											=			get_size_dict(UpperCamelCase_   ,				param_name='''crop_size'''   ,				default_to_square=UpperCamelCase_     )
        SCREAMING_SNAKE_CASE__											=			do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE__											=			rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE__											=			do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE__											=			image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE__											=			image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE__											=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE__											=			make_list_of_images(UpperCamelCase_     )

        if not valid_images(UpperCamelCase_     ):
            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:
            SCREAMING_SNAKE_CASE__											=			[convert_to_rgb(UpperCamelCase_     ) for image in images]

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

        if do_resize:
            SCREAMING_SNAKE_CASE__											=			[self.resize(image=UpperCamelCase_   ,				size=UpperCamelCase_   ,				resample=UpperCamelCase_     ) for image in images]

        if do_center_crop:
            SCREAMING_SNAKE_CASE__											=			[self.center_crop(image=UpperCamelCase_   ,				size=UpperCamelCase_     ) for image in images]

        if do_rescale:
            SCREAMING_SNAKE_CASE__											=			[self.rescale(image=UpperCamelCase_   ,				scale=UpperCamelCase_     ) for image in images]

        if do_normalize:
            SCREAMING_SNAKE_CASE__											=			[self.normalize(image=UpperCamelCase_   ,				mean=UpperCamelCase_   ,				std=UpperCamelCase_     ) for image in images]

        SCREAMING_SNAKE_CASE__											=			[to_channel_dimension_format(UpperCamelCase_   ,				UpperCamelCase_     ) for image in images]

        SCREAMING_SNAKE_CASE__											=			{'''pixel_values''': images}
        return BatchFeature(data=UpperCamelCase_   ,				tensor_type=UpperCamelCase_     )







'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		elif i == ")":
			# RULE 4
			snake_case__	       =   operator_stack.peek()
			operator_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
			a__					:			Any          =     '''(5 + ((4 * 2) * (2 + 3)))'''
			# answer = 45
			print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



