




from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''pixel_values''']




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	bool = True     ,			snake_case_						:	Dict[str, int] = None     ,			snake_case_						:	PILImageResampling = PILImageResampling.BICUBIC     ,			snake_case_						:	bool = True     ,			snake_case_						:	Dict[str, int] = None     ,			snake_case_						:	bool = True     ,			snake_case_						:	Union[int, float] = 1 / 255     ,			snake_case_						:	bool = True     ,			snake_case_						:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,			snake_case_						:	Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,			**snake_case_						:	Any     ,			)				->							None:




     '''simple docstring'''

     super().__init__(**snake_case_				)
     A__	=    size if size is not None else {"shortest_edge": 224}
     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_				)
     A__	=    crop_size if crop_size is not None else {"height": 224, "width": 224}
     A__	=    get_size_dict(snake_case_     ,			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_DEFAULT_MEAN
     A__	=    image_std if image_std is not None else IMAGENET_DEFAULT_STD




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Dict[str, int]     ,			snake_case_						:	PILImageResampling = PILImageResampling.BICUBIC     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	int     ,			)				->							np.ndarray:




     '''simple docstring'''

     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_				)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
      A__	=    int((256 / 224) * size["shortest_edge"]				)
      A__	=    get_resize_output_image_size(snake_case_     ,			size=snake_case_     ,			default_to_square=snake_case_				)
      A__	=    {"height": output_size[0], "width": output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
      raise ValueError(
          F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""				)
     return resize(
         snake_case_     ,			size=(size_dict["height"], size_dict["width"])     ,			resample=snake_case_     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Dict[str, int]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	Dict     ,			)				->							np.ndarray:




     '''simple docstring'''

     A__	=    get_size_dict(snake_case_				)
     if "height" not in size or "width" not in size:
      raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""				)
     return center_crop(snake_case_     ,			size=(size["height"], size["width"])     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Union[int, float]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	int     ,			)				->							np.ndarray:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Union[float, List[float]]     ,			snake_case_						:	Union[float, List[float]]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	Optional[int]     ,			)				->							np.ndarray:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	ImageInput     ,			snake_case_						:	Optional[bool] = None     ,			snake_case_						:	Optional[Dict[str, int]] = None     ,			snake_case_						:	PILImageResampling = None     ,			snake_case_						:	Optional[bool] = None     ,			snake_case_						:	Optional[Dict[str, int]] = None     ,			snake_case_						:	Optional[bool] = None     ,			snake_case_						:	Optional[float] = None     ,			snake_case_						:	Optional[bool] = None     ,			snake_case_						:	Optional[Union[float, Iterable[float]]] = None     ,			snake_case_						:	Optional[Union[float, Iterable[float]]] = None     ,			snake_case_						:	Optional[TensorType] = None     ,			snake_case_						:	ChannelDimension = ChannelDimension.FIRST     ,			**snake_case_						:	int     ,			)				->							BatchFeature:




     '''simple docstring'''

     A__	=    do_resize if do_resize is not None else self.do_resize
     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__	=    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__	=    size if size is not None else self.size
     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_				)
     A__	=    crop_size if crop_size is not None else self.crop_size
     A__	=    get_size_dict(snake_case_     ,			param_name="crop_size"				)

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

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

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

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

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

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

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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"Alexander Joslin"

import operator as op

from .stack import Stack







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 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(lowercase_						)						)
  elif i in operators:
   # RULE 2
   operator_stack.push(lowercase_						)
  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](lowercase_      ,			lowercase_						)
   operand_stack.push(lowercase_						)

    # RULE 5
 return operand_stack.peek()


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





