




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


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







class 						A(		a__     ):



 '''simple docstring'''


 UpperCamelCase		     =		["""pixel_values"""]

 def __init__(       self      :						List[Any]		,							A_      :						List[Any] = True		,							A_      :						int = None		,							A_      :						Union[str, Any] = PILImageResampling.BICUBIC		,							A_      :						Optional[Any] = True		,							A_      :						List[Any] = None		,							A_      :						List[str] = True		,							A_      :						Optional[int] = 1 / 255		,							A_      :						Any = True		,							A_      :						Union[str, Any] = IMAGENET_DEFAULT_MEAN		,							A_      :						Tuple = IMAGENET_DEFAULT_STD		,							**A_      :						Union[str, Any]		,							)     ->			None:

       """simple docstring"""






       super().__init__(**lowercase__						)
       lowerCamelCase_	  =					size if size is not None else {'''shortest_edge''': 224}
       lowerCamelCase_	  =					get_size_dict(lowercase__		,							default_to_square=lowercase__						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       lowerCamelCase_	  =					get_size_dict(lowercase__		,							param_name='crop_size'						)

       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size
       lowerCamelCase_	  =					resample
       lowerCamelCase_	  =					do_center_crop
       lowerCamelCase_	  =					crop_size
       lowerCamelCase_	  =					do_rescale
       lowerCamelCase_	  =					rescale_factor
       lowerCamelCase_	  =					do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       lowerCamelCase_	  =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

 def        a__				(       self      :						Tuple		,							A_      :						Dict		,							A_      :						List[str]		,							A_      :						str = PILImageResampling.BICUBIC		,							A_      :						str = None		,							**A_      :						Union[str, Any]		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(lowercase__		,							default_to_square=lowercase__						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
             lowerCamelCase_	  =					int((256 / 224) * size['shortest_edge']						)
             lowerCamelCase_	  =					get_resize_output_image_size(lowercase__		,							size=lowercase__		,							default_to_square=lowercase__						)
             lowerCamelCase_	  =					{'''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(
           lowercase__		,							size=(size_dict['height'], size_dict['width'])		,							resample=lowercase__		,							data_format=lowercase__		,							**lowercase__						)

 def        a__				(       self      :						str		,							A_      :						Any		,							A_      :						Dict		,							A_      :						str = None		,							**A_      :						Optional[int]		,							)     ->			np.ndarray:

       """simple docstring"""






       lowerCamelCase_	  =					get_size_dict(lowercase__						)
       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(lowercase__		,							size=(size['height'], size['width'])		,							data_format=lowercase__		,							**lowercase__						)

 def        a__				(       self      :						Any		,							A_      :						str		,							A_      :						Dict		,							A_      :						Tuple = None		,							**A_      :						Dict		,							)     ->			np.ndarray:

       """simple docstring"""






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

 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						Tuple		,							A_      :						Optional[Any]		,							A_      :						Any = None		,							**A_      :						List[str]		,							)     ->			np.ndarray:

       """simple docstring"""






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





 def        a__				(       self      :						List[str]		,							A_      :						Union[str, Any]		,							A_      :						List[str] = None		,							A_      :						Optional[Any] = None		,							A_      :						Union[str, Any] = None		,							A_      :						Dict = None		,							A_      :						Optional[int] = None		,							A_      :						Union[str, Any] = None		,							A_      :						Tuple = None		,							A_      :						Optional[int] = None		,							A_      :						Any = None		,							A_      :						Union[str, Any] = None		,							A_      :						List[str] = None		,							A_      :						Optional[int] = ChannelDimension.FIRST		,							**A_      :						Optional[Any]		,							)     ->			BatchFeature:

       """simple docstring"""






       lowerCamelCase_	  =					do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_	  =					resample if resample is not None else self.resample
       lowerCamelCase_	  =					do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_	  =					do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_	  =					rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_	  =					do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_	  =					image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_	  =					image_std if image_std is not None else self.image_std

       lowerCamelCase_	  =					size if size is not None else self.size
       lowerCamelCase_	  =					get_size_dict(lowercase__		,							default_to_square=lowercase__						)
       lowerCamelCase_	  =					crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_	  =					get_size_dict(lowercase__		,							param_name='crop_size'						)

       lowerCamelCase_	  =					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.
       lowerCamelCase_	  =					[to_numpy_array(lowercase__						) for image in images]

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

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

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

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

       lowerCamelCase_	  =					[to_channel_dimension_format(lowercase__		,							lowercase__						) for image in images]

       lowerCamelCase_	  =					{'''pixel_values''': images}
       return BatchFeature(data=lowercase__		,							tensor_type=lowercase__						)









lowerCamelCase       :	Dict									=     "Alexander Joslin"

import operator as op

from .stack import Stack

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str  ):


      '''simple docstring'''



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

      lowerCamelCase_	  =					Stack()
      lowerCamelCase_	  =					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
                  lowerCamelCase_	  =					operator_stack.peek()
                  operator_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()
                  lowerCamelCase_	  =					operand_stack.peek()
                  operand_stack.pop()

                  lowerCamelCase_	  =					operators[opr](lowercase			,							lowercase  )
                  operand_stack.push(lowercase  )

    # RULE 5
      return operand_stack.peek()


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



