





'''simple docstring'''




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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  ['''pixel_values''']





	def __init__(		self      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = PILImageResampling.BILINEAR      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = 1 / 255      ,       UpperCamelCase_ = True      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		None:
		super().__init__(**UpperCamelCase_		)
		snake_case__	       =   size if size is not None else {'shortest_edge': 256}
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       default_to_square=UpperCamelCase_		)
		snake_case__	       =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
		snake_case__	       =   get_size_dict(UpperCamelCase_		)
		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_STANDARD_MEAN
		snake_case__	       =   image_std if image_std is not None else IMAGENET_STANDARD_STD





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = PILImageResampling.BICUBIC      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		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()}'''		)
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		snake_case__	       =   get_size_dict(UpperCamelCase_		)
		return center_crop(UpperCamelCase_      ,       size=(size['height'], size['width'])      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_		)      ->		np.ndarray:
		return rescale(UpperCamelCase_      ,       scale=UpperCamelCase_      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       **UpperCamelCase_      ,       )      ->		np.ndarray:
		return normalize(UpperCamelCase_      ,       mean=UpperCamelCase_      ,       std=UpperCamelCase_      ,       data_format=UpperCamelCase_      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = ChannelDimension.FIRST      ,       **UpperCamelCase_      ,       )      ->		List[str]:
		snake_case__	       =   do_resize if do_resize is not None else self.do_resize
		snake_case__	       =   size if size is not None else self.size
		snake_case__	       =   get_size_dict(UpperCamelCase_      ,       default_to_square=UpperCamelCase_		)
		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__	       =   crop_size if crop_size is not None else self.crop_size
		snake_case__	       =   get_size_dict(UpperCamelCase_		)
		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__	       =   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.'		)

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

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

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

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

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

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

		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)}""")



