





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

UpperCamelCase__											= logging.get_logger(__name__)

@add_end_docstrings(_a       )
class        __SCREAMING_SNAKE_CASE					(     _a       ):






							def __init__(						self  , *__lowerCAmelCase  , **__lowerCAmelCase  ):
														super().__init__(*__lowerCAmelCase  , **__lowerCAmelCase  )
														requires_backends(self  , """vision"""  )
														self.check_model_type(__lowerCAmelCase  )






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






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														return {}, {}, {}






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			load_image(__lowerCAmelCase  )
														UpperCamelCase__				     =			image.size
														UpperCamelCase__				     =			self.image_processor(images=__lowerCAmelCase  , return_tensors=self.framework  )
														return model_inputs






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			self.model(**__lowerCAmelCase  )
														return model_outputs







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











def     _UpperCamelCase     (a__   :int       ):






							"""simple docstring"""





							if divisor % 5 == 0 or divisor % 2 == 0:
														return 0
							UpperCamelCase__				     =			1
							UpperCamelCase__				     =			1
							while repunit:
														UpperCamelCase__				     =			(10 * repunit + 1) % divisor
														repunit_index += 1
							return repunit_index





def     _UpperCamelCase     (a__   :int = 100_0000       ):






							"""simple docstring"""





							UpperCamelCase__				     =			limit - 1
							if divisor % 2 == 0:
														divisor += 1
							while least_divisible_repunit(a__       ) <= limit:
														divisor += 2
							return divisor


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




