"""simple docstring"""






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

__A			   = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase  )
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			*_UpperCAmelCase    ,			**_UpperCAmelCase							):
       super().__init__(*_UpperCAmelCase    ,			**_UpperCAmelCase							)
       requires_backends(self    ,			'''vision'''							)
       self.check_model_type(_UpperCAmelCase							)






    def __call__(   self    ,			_UpperCAmelCase    ,			**_UpperCAmelCase							):
       return super().__call__(_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       return {}, {}, {}






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      load_image(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      image.size
       lowercase__:   int					=      self.image_processor(images=_UpperCAmelCase    ,			return_tensors=self.framework							)
       return model_inputs






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   int					=      self.model(**_UpperCAmelCase							)
       return model_outputs






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      model_outputs.predicted_depth
       lowercase__:   int					=      torch.nn.functional.interpolate(
           predicted_depth.unsqueeze(1							)    ,			size=self.image_size[::-1]    ,			mode='''bicubic'''    ,			align_corners=_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      prediction.squeeze().cpu().numpy()
       lowercase__:   List[Any]					=      (output * 255 / np.max(_UpperCAmelCase							)).astype('''uint8'''							)
       lowercase__:   str					=      Image.fromarray(_UpperCAmelCase							)
       lowercase__:   str					=      {}
       lowercase__:   Any					=      predicted_depth
       lowercase__:   Optional[Any]					=      depth
       return output_dict

"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:
   lowercase__:   Optional[Any]					=      0
   lowercase__:   List[Any]					=      len(__UpperCAmelCase ) - 1

   while left <= right:
      # avoid divided by 0 during interpolation
      if sorted_collection[left] == sorted_collection[right]:
         if sorted_collection[left] == item:
            return left
         else:
            return None

      lowercase__:   Tuple					=      left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(__UpperCAmelCase ):
         return None

      lowercase__:   Optional[int]					=      sorted_collection[point]
      if current_item == item:
         return point
      else:
         if point < left:
            lowercase__:   List[Any]					=      left
            lowercase__:   int					=      point
         elif point > right:
            lowercase__:   Dict					=      right
            lowercase__:   List[str]					=      point
         else:
            if item < current_item:
               lowercase__:   int					=      point - 1
            else:
               lowercase__:   int					=      point + 1
   return None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[str]:

   # avoid divided by 0 during interpolation
   if sorted_collection[left] == sorted_collection[right]:
      if sorted_collection[left] == item:
         return left
      else:
         return None

   lowercase__:   Optional[int]					=      left + ((item - sorted_collection[left]) * (right - left)) // (
       sorted_collection[right] - sorted_collection[left]
   )

   # out of range check
   if point < 0 or point >= len(__UpperCAmelCase ):
      return None

   if sorted_collection[point] == item:
      return point
   elif point < left:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   elif point > right:
      return interpolation_search_by_recursion(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )
   else:
      if sorted_collection[point] > item:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    point - 1 )
      else:
         return interpolation_search_by_recursion(
             __UpperCAmelCase       ,    __UpperCAmelCase       ,    point + 1       ,    __UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  List[Any]:
   if collection != sorted(__UpperCAmelCase ):
      raise ValueError('''Collection must be ascending sorted''' )
   return True


if __name__ == "__main__":
       import sys
       __A			   = 0
       if debug == 1:
              __A			   = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
              try:
                     __assert_sorted(collection)
              except ValueError:
                     sys.exit("Sequence must be ascending sorted to apply interpolation search")
              __A			   = 6_7

       __A			   = interpolation_search(collection, target)
       if result is not None:
              print(f'''{target} found at positions: {result}''')
       else:
              print("Not found")
