





from __future__ import annotations






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int	)				->    int:
   # Checks if the entire collection has been sorted
   if len(snake_case_	) <= 1 or n <= 1:
      return

   insert_next(snake_case_	,				n - 1	)
   rec_insertion_sort(snake_case_	,				n - 1	)






def lowerCamelCase__   (							snake_case_     :  list	,				snake_case_     :  int	)				->    List[str]:
   # Checks order between adjacent elements
   if index >= len(snake_case_	) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   __snake_case	,     __snake_case								=       (
       collection[index],
       collection[index - 1],
   )

   insert_next(snake_case_	,				index + 1	)


if __name__ == "__main__":
    snake_case_           =      input('Enter integers separated by spaces: ')
    snake_case_           =      [int(num) for num in numbers.split()]
    rec_insertion_sort(number_list, len(number_list))
    print(number_list)








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

