




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

SCREAMING_SNAKE_CASE__     :     Dict		    =       logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__     )
class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



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





	def __call__(							self      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



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





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				return {}, {}, {}





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						load_image(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						image.size
				SCREAMING_SNAKE_CASE_								=						self.image_processor(images=SCREAMING_SNAKE_CASE      ,     return_tensors=self.framework      )
				return model_inputs





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.model(**SCREAMING_SNAKE_CASE      )
				return model_outputs





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



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






def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       input("Enter numbers separated by a comma:\n").strip()
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [int(item) for item in user_input.split(",")]
				print(pancake_sort(unsorted))
