





"""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(a			)
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       def __init__(       self       ,					*__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def __call__(       self       ,					__UpperCAmelCase       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

         return {}, {}, {}






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				load_image(__UpperCAmelCase )
         lowerCAmelCase__     :List[str]											=				image.size
         lowerCAmelCase__     :Optional[Any]											=				self.image_processor(images=__UpperCAmelCase       ,					return_tensors=self.framework )
         return model_inputs






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				self.model(**__UpperCAmelCase )
         return model_outputs






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				model_outputs.predicted_depth
         lowerCAmelCase__     :Dict											=				torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1 )       ,					size=self.image_size[::-1]       ,					mode='bicubic'       ,					align_corners=__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				prediction.squeeze().cpu().numpy()
         lowerCAmelCase__     :int											=				(output * 2_5_5 / np.max(__UpperCAmelCase )).astype('uint8' )
         lowerCAmelCase__     :List[Any]											=				Image.fromarray(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{}
         lowerCAmelCase__     :Tuple											=				predicted_depth
         lowerCAmelCase__     :Dict											=				depth
         return output_dict


def   lowerCamelCase__							(							__lowerCAmelCase    :    list							):




			"""simple docstring"""


			for i in range(len(__lowerCAmelCase							) - 1       ,				0       ,				-1							):
						lowerCAmelCase_         =      False

						for j in range(__lowerCAmelCase       ,				0       ,				-1							):
									if unsorted[j] < unsorted[j - 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j - 1], unsorted[j]
												lowerCAmelCase_         =      True

						for j in range(__lowerCAmelCase							):
									if unsorted[j] > unsorted[j + 1]:
												lowerCAmelCase_      ,    lowerCAmelCase_         =      unsorted[j + 1], unsorted[j]
												lowerCAmelCase_         =      True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			_A  =	input("Enter numbers separated by a comma:\n").strip()
			_A  =	[int(item) for item in user_input.split(",")]
			print(f"""{cocktail_shaker_sort(unsorted) = }""")



