






"""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


A__					: Dict=	logging.get_logger(__name__)
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						=["""pixel_values"""]

   def __init__(					self ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = PILImageResampling.BILINEAR ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = True ,				snake_case_ = 1 / 255 ,				snake_case_ = True ,				snake_case_ = None ,				snake_case_ = None ,				**snake_case_ ,				)		->       None:

       '''simple docstring'''





       super().__init__(**snake_case_      )
       UpperCamelCase__         =    size if size is not None else {'shortest_edge': 256}
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				default_to_square=snake_case_      )
       UpperCamelCase__         =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
       UpperCamelCase__         =    get_size_dict(snake_case_ ,				param_name='crop_size'      )
       UpperCamelCase__         =    do_resize
       UpperCamelCase__         =    size
       UpperCamelCase__         =    resample
       UpperCamelCase__         =    do_center_crop
       UpperCamelCase__         =    crop_size
       UpperCamelCase__         =    do_rescale
       UpperCamelCase__         =    rescale_factor
       UpperCamelCase__         =    do_normalize
       UpperCamelCase__         =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase__         =    image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = PILImageResampling.BICUBIC ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:

       '''simple docstring'''





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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:

       '''simple docstring'''





       UpperCamelCase__         =    get_size_dict(snake_case_      )
       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(snake_case_ ,				size=(size['height'], size['width']) ,				data_format=snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				**snake_case_      )		->       np.ndarray:

       '''simple docstring'''





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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = None ,				**snake_case_ ,				)		->       np.ndarray:

       '''simple docstring'''





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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = None ,				snake_case_ = ChannelDimension.FIRST ,				**snake_case_ ,				)		->       Tuple:

       '''simple docstring'''





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

       UpperCamelCase__         =    make_list_of_images(snake_case_      )

       if not valid_images(snake_case_      ):
           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.
       UpperCamelCase__         =    [to_numpy_array(snake_case_      ) for image in images]

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

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

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

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

       UpperCamelCase__         =    [to_channel_dimension_format(snake_case_ ,				snake_case_      ) for image in images]

       UpperCamelCase__         =    {'pixel_values': images}
       return BatchFeature(data=snake_case_ ,				tensor_type=snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Union[str, Any]:

       '''simple docstring'''





       UpperCamelCase__         =    outputs.logits

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

           if is_torch_tensor(snake_case_      ):
               UpperCamelCase__         =    target_sizes.numpy()

           UpperCamelCase__         =    []

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

       return semantic_segmentation












"""simple docstring"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

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

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


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




