





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

__lowerCAmelCase :str     =    logging.get_logger(__name__)






@add_end_docstrings(__A    )
class        _a(    __A    ):







		def __init__(    self ,						*__snake_case ,						**__snake_case				)      ->      int:


							'''simple docstring'''

							super().__init__(*__snake_case ,						**__snake_case				)
							requires_backends(self ,						"vision"				)
							self.check_model_type(__snake_case				)







		def __call__(    self ,						__snake_case ,						**__snake_case				)      ->      Optional[int]:


							'''simple docstring'''

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







		def        lowercase       (    self ,						**__snake_case				)      ->      int:


							'''simple docstring'''

							return {}, {}, {}







		def        lowercase       (    self ,						__snake_case				)      ->      int:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   load_image(__snake_case				)
							_snake_case       :					Optional[Any]				       =   image.size
							_snake_case       :					int				       =   self.image_processor(images=__snake_case ,						return_tensors=self.framework				)
							return model_inputs







		def        lowercase       (    self ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.model(**__snake_case				)
							return model_outputs







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

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












def    A				(							UpperCAmelCase				):
					return str(UpperCAmelCase				) == str(UpperCAmelCase				)[::-1]







def    A				(							UpperCAmelCase				):
					return int(UpperCAmelCase				) + int(str(UpperCAmelCase				)[::-1]				)







def    A				(							UpperCAmelCase = 10_000				):
					_snake_case       :					Optional[int]				       =   []
					for num in range(1  ,  UpperCAmelCase				):
										_snake_case       :					Optional[Any]				       =   0
										_snake_case       :					Optional[int]				       =   num
										while iterations < 50:
															_snake_case       :					Optional[Any]				       =   sum_reverse(UpperCAmelCase				)
															iterations += 1
															if is_palindrome(UpperCAmelCase				):
																				break
										else:
															lychrel_nums.append(UpperCAmelCase				)
					return len(UpperCAmelCase				)


if __name__ == "__main__":
							print(F'''{solution() = }''')