





def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   0

 # if input_string is "aba" than new_input_string become "a|b|a"
 UpperCamelCase	:					Any       	=   ""
 UpperCamelCase	:					List[Any]       	=   ""

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(_lowerCAmelCase     ) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   0, 0

 # length[i] shows the length of palindromic substring with center i
 UpperCamelCase	:					Any       	=   [1 for i in range(len(_lowerCAmelCase     )     )]

 # for each character in new_string find corresponding palindromic string
 UpperCamelCase	:					Optional[Any]       	=   0
 for j in range(len(_lowerCAmelCase     )     ):
  UpperCamelCase	:					str       	=   1 if j > r else min(length[l + r - j] // 2							,   r - j + 1     )
  while (
      j - k >= 0
      and j + k < len(_lowerCAmelCase     )
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  UpperCamelCase	:					Optional[Any]       	=   2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   UpperCamelCase	:					List[str]       	=   j - k + 1  # noqa: E741
   UpperCamelCase	:					Union[str, Any]       	=   j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   UpperCamelCase	:					Union[str, Any]       	=   length[j]
   UpperCamelCase	:					List[Any]       	=   j

    # create that string
 UpperCamelCase	:					Optional[Any]       	=   new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
    import doctest

    doctest.testmod()







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       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



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


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





         """simple docstring"""



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


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


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





         """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       :      int				,			a__       :      List[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

