




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 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, logging


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




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Union[str, Any]			  =   ["""pixel_values"""]







    def __init__(					self  ,						UpperCamelCase__ = True  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = PILImageResampling.BILINEAR  ,						UpperCamelCase__ = True  ,						UpperCamelCase__ = 1 / 255  ,						UpperCamelCase__ = True  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						**UpperCamelCase__  ,						)  ->   None:
           super().__init__(**UpperCamelCase__  )
           lowerCamelCase     :   Dict    				=      size if size is not None else {"height": 224, "width": 224}
           lowerCamelCase     :   str    				=      get_size_dict(UpperCamelCase__  )
           lowerCamelCase     :   List[str]    				=      do_resize
           lowerCamelCase     :   Optional[Any]    				=      do_rescale
           lowerCamelCase     :   int    				=      do_normalize
           lowerCamelCase     :   Optional[int]    				=      size
           lowerCamelCase     :   int    				=      resample
           lowerCamelCase     :   Union[str, Any]    				=      rescale_factor
           lowerCamelCase     :   Tuple    				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase     :   List[Any]    				=      image_std if image_std is not None else IMAGENET_STANDARD_STD







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = PILImageResampling.BILINEAR  ,						UpperCamelCase__ = None  ,						**UpperCamelCase__  ,						)  ->   np.ndarray:
           lowerCamelCase     :   Optional[Any]    				=      get_size_dict(UpperCamelCase__  )
           if "height" not in size or "width" not in size:
                  raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''  )
           return resize(
               UpperCamelCase__  ,						size=(size["height"], size["width"])  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						**UpperCamelCase__  )  ->   np.ndarray:
           return rescale(UpperCamelCase__  ,						scale=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__  )







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







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = ChannelDimension.FIRST  ,						**UpperCamelCase__  ,						)  ->   List[str]:
           lowerCamelCase     :   Union[str, Any]    				=      do_resize if do_resize is not None else self.do_resize
           lowerCamelCase     :   List[Any]    				=      do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase     :   Optional[Any]    				=      do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase     :   List[Any]    				=      resample if resample is not None else self.resample
           lowerCamelCase     :   Dict    				=      rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase     :   List[str]    				=      image_mean if image_mean is not None else self.image_mean
           lowerCamelCase     :   int    				=      image_std if image_std is not None else self.image_std

           lowerCamelCase     :   Optional[int]    				=      size if size is not None else self.size
           lowerCamelCase     :   Optional[Any]    				=      get_size_dict(UpperCamelCase__  )

           lowerCamelCase     :   Any    				=      make_list_of_images(UpperCamelCase__  )

           if not valid_images(UpperCamelCase__  ):
                  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_rescale and rescale_factor is None:
                  raise ValueError("Rescale factor must be specified if do_rescale is True."  )

           # All transformations expect numpy arrays.
           lowerCamelCase     :   List[str]    				=      [to_numpy_array(UpperCamelCase__  ) for image in images]

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

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

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

           lowerCamelCase     :   Tuple    				=      [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__  ) for image in images]

           lowerCamelCase     :   Dict    				=      {"pixel_values": images}
           return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__  )












SCREAMING_SNAKE_CASE__ :   str						=     {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       return c.isdigit()

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[Any]:
       lowerCamelCase     :   Tuple    				=      []

       # iterate over the string in reverse order
       for c in expression.split()[::-1]:
              # push operand to stack
              if is_operand(_SCREAMING_SNAKE_CASE					):
                     stack.append(int(_SCREAMING_SNAKE_CASE					)					)

              else:
                     # pop values from stack can calculate the result
                     # push the result onto the stack again
                     lowerCamelCase     :   str    				=      stack.pop()
                     lowerCamelCase     :   Optional[int]    				=      stack.pop()
                     stack.append(calc[c](_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)					)

       return stack.pop()


# Driver code
if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   Optional[int]						=     '+ 9 * 2 6'
       print(evaluate(test_expression))

       SCREAMING_SNAKE_CASE__ :   Union[str, Any]						=     '/ * 10 2 + 4 1 '
       print(evaluate(test_expression))






