






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


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  ['''pixel_values''']







				def __init__(						self       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = PILImageResampling.BICUBIC       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = 1 / 2_5_5       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase     )
								__magic_name__		:int        =      size if size is not None else {'''shortest_edge''': 2_2_4}
								__magic_name__		:List[str]        =      get_size_dict(__lowerCAmelCase       ,					default_to_square=__lowerCAmelCase     )
								__magic_name__		:Any        =      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
								__magic_name__		:Optional[int]        =      get_size_dict(__lowerCAmelCase       ,					default_to_square=__lowerCAmelCase       ,					param_name='''crop_size'''     )

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = PILImageResampling.BICUBIC       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      get_size_dict(__lowerCAmelCase       ,					default_to_square=__lowerCAmelCase     )
								if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
								__magic_name__		:Dict        =      get_resize_output_image_size(__lowerCAmelCase       ,					size=size['''shortest_edge''']       ,					default_to_square=__lowerCAmelCase     )
								return resize(__lowerCAmelCase       ,					size=__lowerCAmelCase       ,					resample=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      get_size_dict(__lowerCAmelCase     )
								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(__lowerCAmelCase       ,					size=(size['''height'''], size['''width'''])       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								return rescale(__lowerCAmelCase       ,					scale=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								return normalize(__lowerCAmelCase       ,					mean=__lowerCAmelCase       ,					std=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = ChannelDimension.FIRST       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:Any        =      do_resize if do_resize is not None else self.do_resize
								__magic_name__		:List[Any]        =      size if size is not None else self.size
								__magic_name__		:int        =      get_size_dict(__lowerCAmelCase       ,					param_name='''size'''       ,					default_to_square=__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      resample if resample is not None else self.resample
								__magic_name__		:Dict        =      do_center_crop if do_center_crop is not None else self.do_center_crop
								__magic_name__		:List[Any]        =      crop_size if crop_size is not None else self.crop_size
								__magic_name__		:Any        =      get_size_dict(__lowerCAmelCase       ,					param_name='''crop_size'''       ,					default_to_square=__lowerCAmelCase     )
								__magic_name__		:Any        =      do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__		:int        =      rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__		:List[str]        =      do_normalize if do_normalize is not None else self.do_normalize
								__magic_name__		:str        =      image_mean if image_mean is not None else self.image_mean
								__magic_name__		:Optional[int]        =      image_std if image_std is not None else self.image_std
								__magic_name__		:Dict        =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__magic_name__		:Optional[Any]        =      make_list_of_images(__lowerCAmelCase     )

								if not valid_images(__lowerCAmelCase     ):
												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:
												__magic_name__		:List[Any]        =      [convert_to_rgb(__lowerCAmelCase     ) for image in images]

								# All transformations expect numpy arrays.
								__magic_name__		:Any        =      [to_numpy_array(__lowerCAmelCase     ) for image in images]

								if do_resize:
												__magic_name__		:Tuple        =      [self.resize(image=__lowerCAmelCase       ,					size=__lowerCAmelCase       ,					resample=__lowerCAmelCase     ) for image in images]

								if do_center_crop:
												__magic_name__		:int        =      [self.center_crop(image=__lowerCAmelCase       ,					size=__lowerCAmelCase     ) for image in images]

								if do_rescale:
												__magic_name__		:List[Any]        =      [self.rescale(image=__lowerCAmelCase       ,					scale=__lowerCAmelCase     ) for image in images]

								if do_normalize:
												__magic_name__		:int        =      [self.normalize(image=__lowerCAmelCase       ,					mean=__lowerCAmelCase       ,					std=__lowerCAmelCase     ) for image in images]

								__magic_name__		:List[str]        =      [to_channel_dimension_format(__lowerCAmelCase       ,					__lowerCAmelCase     ) for image in images]

								__magic_name__		:Tuple        =      {'''pixel_values''': images}
								return BatchFeature(data=__lowerCAmelCase       ,					tensor_type=__lowerCAmelCase     )














SCREAMING_SNAKE_CASE__      :			str							=		"""Alexander Joslin"""

import operator as op

from .stack import Stack

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

				__magic_name__		:Stack[int]        =      Stack()
				__magic_name__		:Stack[str]        =      Stack()

				for i in equation:
								if i.isdigit():
												# RULE 1
												operand_stack.push(int(snake_case ) )
								elif i in operators:
												# RULE 2
												operator_stack.push(snake_case )
								elif i == ")":
												# RULE 4
												__magic_name__		:Optional[int]        =      operator_stack.peek()
												operator_stack.pop()
												__magic_name__		:List[str]        =      operand_stack.peek()
												operand_stack.pop()
												__magic_name__		:Optional[Any]        =      operand_stack.peek()
												operand_stack.pop()

												__magic_name__		:Optional[int]        =      operators[opr](snake_case,  snake_case )
												operand_stack.push(snake_case )

    # RULE 5
				return operand_stack.peek()


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






