





import math






def lowerCamelCase__   (							snake_case_     :  Dict	,				snake_case_     :  str	)				->    Dict:
   if 0 not in (x, y):
      # We use the relation x^y = y*log10(x), where 10 is the base.
      return y * math.logaa(snake_case_	)
   else:
      if x == 0:  # 0 raised to any number is 0
         return 0
      elif y == 0:
         return 1  # any number raised to 0 is 1
   raise AssertionError('''This should never happen'''	)


if __name__ == "__main__":  # Main function
    # Read two numbers from input and typecast them to int using map function.
    # Here x is the base and y is the power.
    snake_case_           =      'Enter the base and the power separated by a comma: '
    snake_case_      ,		snake_case_           =      map(int, input(prompt).split(','))
    snake_case_      ,		snake_case_           =      map(int, input(prompt).split(','))

    # We find the log of each number, using the function res(), which takes two
    # arguments.
    snake_case_           =      res(xa, ya)
    snake_case_           =      res(xa, ya)

    # We check for the largest number
    if resa > resa:
        print('Largest number is', xa, '^', ya)
    elif resa > resa:
        print('Largest number is', xa, '^', ya)
    else:
        print('Both are equal')








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

