





'''simple docstring'''

from typing import Dict, List, Optional, Tuple, 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, is_torch_available, is_torch_tensor, logging


if is_torch_available():
  import torch


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






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""pixel_values"""]
 def __init__(							self :List[Any]		,			lowercase_ :bool = True		,			lowercase_ :Optional[Dict[str, int]] = None		,			lowercase_ :PILImageResampling = PILImageResampling.BILINEAR		,			lowercase_ :bool = True		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :bool = True		,			lowercase_ :Union[int, float] = 1 / 2_55		,			lowercase_ :bool = True		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			**lowercase_ :Dict		,			)->				None:
     super().__init__(**lowercase_				)
     A__							=     size if size is not None else {"shortest_edge": 2_56}
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     A__							=     crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
     A__							=     get_size_dict(lowercase_		,			param_name="crop_size"				)
     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 if image_mean is not None else IMAGENET_STANDARD_MEAN
     A__							=     image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	UpperCAmelCase_				(							self :Optional[Any]		,			lowercase_ :np.ndarray		,			lowercase_ :Dict[str, int]		,			lowercase_ :PILImageResampling = PILImageResampling.BICUBIC		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :Optional[Any]		,			)->				np.ndarray:
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     if "shortest_edge" not in size:
         raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
     A__							=     get_resize_output_image_size(lowercase_		,			size=size["shortest_edge"]		,			default_to_square=lowercase_				)
     return resize(lowercase_		,			size=lowercase_		,			resample=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :np.ndarray		,			lowercase_ :Dict[str, int]		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :str		,			)->				np.ndarray:
     A__							=     get_size_dict(lowercase_				)
     if "height" not in size or "width" not in size:
         raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"				)
     return center_crop(lowercase_		,			size=(size["height"], size["width"])		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :np.ndarray		,			lowercase_ :float		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :Optional[int]				)->				np.ndarray:
     return rescale(lowercase_		,			scale=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :np.ndarray		,			lowercase_ :Union[float, List[float]]		,			lowercase_ :Union[float, List[float]]		,			lowercase_ :Optional[Union[str, ChannelDimension]] = None		,			**lowercase_ :List[str]		,			)->				np.ndarray:
     return normalize(lowercase_		,			mean=lowercase_		,			std=lowercase_		,			data_format=lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :ImageInput		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :PILImageResampling = None		,			lowercase_ :bool = None		,			lowercase_ :Dict[str, int] = None		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Optional[float] = None		,			lowercase_ :Optional[bool] = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :Optional[Union[float, List[float]]] = None		,			lowercase_ :Optional[Union[str, TensorType]] = None		,			lowercase_ :Union[str, ChannelDimension] = ChannelDimension.FIRST		,			**lowercase_ :int		,			)->				Any:
     A__							=     do_resize if do_resize is not None else self.do_resize
     A__							=     size if size is not None else self.size
     A__							=     get_size_dict(lowercase_		,			default_to_square=lowercase_				)
     A__							=     resample if resample is not None else self.resample
     A__							=     do_center_crop if do_center_crop is not None else self.do_center_crop
     A__							=     crop_size if crop_size is not None else self.crop_size
     A__							=     get_size_dict(lowercase_		,			param_name="crop_size"				)
     A__							=     do_rescale if do_rescale is not None else self.do_rescale
     A__							=     rescale_factor if rescale_factor is not None else self.rescale_factor
     A__							=     do_normalize if do_normalize is not None else self.do_normalize
     A__							=     image_mean if image_mean is not None else self.image_mean
     A__							=     image_std if image_std is not None else self.image_std

     A__							=     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.
     A__							=     [to_numpy_array(lowercase_				) for image in images]

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

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

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

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

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

     A__							=     {"pixel_values": images}
     return BatchFeature(data=lowercase_		,			tensor_type=lowercase_				)



 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :Any		,			lowercase_ :List[Tuple] = None				)->				Any:
     A__							=     outputs.logits

     # Resize logits and compute semantic segmentation maps
     if target_sizes is not None:
         if len(lowercase_				) != len(lowercase_				):
             raise ValueError(
                 "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

         if is_torch_tensor(lowercase_				):
             A__							=     target_sizes.numpy()

         A__							=     []

         for idx in range(len(lowercase_				)				):
             A__							=     torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0				)		,			size=target_sizes[idx]		,			mode="bilinear"		,			align_corners=lowercase_				)
             A__							=     resized_logits[0].argmax(dim=0				)
             semantic_segmentation.append(lowercase_				)
     else:
         A__							=     logits.argmax(dim=1				)
         A__							=     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

     return semantic_segmentation










'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

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

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


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



