

"""simple docstring"""




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


SCREAMING_SNAKE_CASE_     :			int	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			b.T
   lowerCamelCase__						=			np.sum(np.square(A__   )	,    axis=1   )
   lowerCamelCase__						=			np.sum(np.square(A__   )	,    axis=0   )
   lowerCamelCase__						=			np.matmul(A__	,    A__   )
   lowerCamelCase__						=			aa[:, None] - 2 * ab + ba[None, :]
   return d



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   lowerCamelCase__						=			x.reshape(-1	,    3   )
   lowerCamelCase__						=			squared_euclidean_distance(A__	,    A__   )
   return np.argmin(A__	,    axis=1   )


class 		_A       (			__a					):
      __a								=	['pixel_values']





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = True    ,						SCREAMING_SNAKE_CASE__ = True    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					None:
         super().__init__(**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			size if size is not None else {"height": 256, "width": 256}
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		) if clusters is not None else None
         lowerCamelCase__						=			do_resize
         lowerCamelCase__						=			size
         lowerCamelCase__						=			resample
         lowerCamelCase__						=			do_normalize
         lowerCamelCase__						=			do_color_quantize





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR    ,						SCREAMING_SNAKE_CASE__ = None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         if "height" not in size or "width" not in size:
            raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}'		)
         return resize(
             SCREAMING_SNAKE_CASE__    ,						size=(size["height"], size["width"])    ,						resample=SCREAMING_SNAKE_CASE__    ,						data_format=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						)   ->					np.ndarray:
         lowerCamelCase__						=			rescale(image=SCREAMING_SNAKE_CASE__    ,						scale=1 / 1_27.5    ,						data_format=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			image - 1
         return image





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					PIL.Image.Image:
         lowerCamelCase__						=			do_resize if do_resize is not None else self.do_resize
         lowerCamelCase__						=			size if size is not None else self.size
         lowerCamelCase__						=			get_size_dict(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			resample if resample is not None else self.resample
         lowerCamelCase__						=			do_normalize if do_normalize is not None else self.do_normalize
         lowerCamelCase__						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         lowerCamelCase__						=			clusters if clusters is not None else self.clusters
         lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			make_list_of_images(SCREAMING_SNAKE_CASE__		)

         if not valid_images(SCREAMING_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 or resample is None:
            raise ValueError("Size and resample must be specified if do_resize is True."		)

         if do_color_quantize and clusters is None:
            raise ValueError("Clusters must be specified if do_color_quantize is True."		)

         # All transformations expect numpy arrays.
         lowerCamelCase__						=			[to_numpy_array(SCREAMING_SNAKE_CASE__		) for image in images]

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

         if do_normalize:
            lowerCamelCase__						=			[self.normalize(image=SCREAMING_SNAKE_CASE__		) for image in images]

         if do_color_quantize:
            lowerCamelCase__						=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__    ,						ChannelDimension.LAST		) for image in images]
            # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
            lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			color_quantize(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		).reshape(images.shape[:-1]		)

            # flatten to (batch_size, height*width)
            lowerCamelCase__						=			images.shape[0]
            lowerCamelCase__						=			images.reshape(SCREAMING_SNAKE_CASE__    ,						-1		)

            # We need to convert back to a list of images to keep consistent behaviour across processors.
            lowerCamelCase__						=			list(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			[to_channel_dimension_format(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) for image in images]

         lowerCamelCase__						=			{"input_ids": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__    ,						tensor_type=SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": op.pow,
       "*": op.mul,
       "/": div,
       "+": op.add,
       "-": op.sub,
   }  # operators & their respective operation

   # print table header
   print("Symbol".center(8   )	,    "Action".center(12   )	,    "Stack"	,    sep=" | "   )
   print("-" * (30 + len(A__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(A__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )	,    ("push(" + x + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )
      else:
         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         stack.append(
             str(opr[x](int(A__   )	,    int(A__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )	,    ("push(" + a + x + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "	,    )

   return int(stack[0]   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
     print('''\n\tResult = ''', solve(Postfix))




