



'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


a     :    int      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase	)   ->    List[List[ImageInput]]:
							if isinstance(_lowercase			,       (list, tuple)	) and isinstance(videos[0]			,       (list, tuple)	) and is_valid_image(videos[0][0]	):
														return videos

							elif isinstance(_lowercase			,       (list, tuple)	) and is_valid_image(videos[0]	):
														return [videos]

							elif is_valid_image(_lowercase	):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}'''	)


class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				['pixel_values']





	def __init__(						self	,	A = True	,	A = None	,	A = PILImageResampling.BILINEAR	,	A = True	,	A = None	,	A = True	,	A = 1 / 255	,	A = True	,	A = None	,	A = None	,	**A	,	)    ->    None:
								super().__init__(**A					)
								UpperCAmelCase  :	Dict							  =	size if size is not None else {"""shortest_edge""": 224}
								UpperCAmelCase  :	List[Any]							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	Union[str, Any]							  =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase  :	Any							  =	get_size_dict(A	,	param_name="""crop_size"""					)

								UpperCAmelCase  :	str							  =	do_resize
								UpperCAmelCase  :	str							  =	size
								UpperCAmelCase  :	Optional[Any]							  =	do_center_crop
								UpperCAmelCase  :	Union[str, Any]							  =	crop_size
								UpperCAmelCase  :	Dict							  =	resample
								UpperCAmelCase  :	str							  =	do_rescale
								UpperCAmelCase  :	Tuple							  =	rescale_factor
								UpperCAmelCase  :	Optional[int]							  =	do_normalize
								UpperCAmelCase  :	str							  =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase  :	Any							  =	image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BILINEAR	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A	,	default_to_square=A					)
								if "shortest_edge" in size:
															UpperCAmelCase  :	List[str]							  =	get_resize_output_image_size(A	,	size["""shortest_edge"""]	,	default_to_square=A					)
								elif "height" in size and "width" in size:
															UpperCAmelCase  :	List[str]							  =	(size["""height"""], size["""width"""])
								else:
															raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''					)
								return resize(A	,	size=A	,	resample=A	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	List[str]							  =	get_size_dict(A					)
								if "height" not in size or "width" not in size:
															raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''					)
								return center_crop(A	,	size=(size["""height"""], size["""width"""])	,	data_format=A	,	**A					)





	def 				_lowercase(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    List[str]:
								return rescale(A	,	scale=A	,	data_format=A	,	**A					)





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





	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 = ChannelDimension.FIRST	,	)    ->    np.ndarray:
								if do_resize and size is None or resample is None:
															raise ValueError("""Size and resample 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.
								UpperCAmelCase  :	List[str]							  =	to_numpy_array(A					)

								if do_resize:
															UpperCAmelCase  :	str							  =	self.resize(image=A	,	size=A	,	resample=A					)

								if do_center_crop:
															UpperCAmelCase  :	Tuple							  =	self.center_crop(A	,	size=A					)

								if do_rescale:
															UpperCAmelCase  :	Optional[Any]							  =	self.rescale(image=A	,	scale=A					)

								if do_normalize:
															UpperCAmelCase  :	List[Any]							  =	self.normalize(image=A	,	mean=A	,	std=A					)

								UpperCAmelCase  :	int							  =	to_channel_dimension_format(A	,	A					)
								return image






	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 = ChannelDimension.FIRST	,	**A	,	)    ->    PIL.Image.Image:
								UpperCAmelCase  :	Any							  =	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase  :	int							  =	resample if resample is not None else self.resample
								UpperCAmelCase  :	Union[str, Any]							  =	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase  :	Tuple							  =	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase  :	Optional[Any]							  =	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase  :	Tuple							  =	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase  :	Optional[int]							  =	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase  :	Tuple							  =	image_std if image_std is not None else self.image_std

								UpperCAmelCase  :	Optional[int]							  =	size if size is not None else self.size
								UpperCAmelCase  :	Union[str, Any]							  =	get_size_dict(A	,	default_to_square=A					)
								UpperCAmelCase  :	Optional[Any]							  =	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase  :	Dict							  =	get_size_dict(A	,	param_name="""crop_size"""					)

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

								UpperCAmelCase  :	List[Any]							  =	make_batched(A					)

								UpperCAmelCase  :	Dict							  =	[
								    [
								        self._preprocess_image(
								            image=A	,	do_resize=A	,	size=A	,	resample=A	,	do_center_crop=A	,	crop_size=A	,	do_rescale=A	,	rescale_factor=A	,	do_normalize=A	,	image_mean=A	,	image_std=A	,	data_format=A	,	)
								        for img in video
								    ]
								    for video in videos
								]

								UpperCAmelCase  :	int							  =	{"""pixel_values""": videos}
								return BatchFeature(data=A	,	tensor_type=A					)






'''simple docstring'''




a     :    List[Any]      						=					"""Alexander Joslin"""

import operator as op

from .stack import Stack



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

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

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

																					UpperCAmelCase  :	List[Any]							  =	operators[opr](_lowercase			,       _lowercase	)
																					operand_stack.push(_lowercase	)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
							a     :    Tuple      						=					"""(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')

