
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(UpperCAmelCase							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							*SCREAMING_SNAKE_CASE_  :			int       ,							**SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					super().__init__(*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					requires_backends(self       ,							'''vision'''       )
					self.check_model_type(SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, List[str], "Image.Image", List["Image.Image"]]       ,							**SCREAMING_SNAKE_CASE_  :			Dict       )       ->     Optional[Any]:
					return super().__call__(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Optional[Any]       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Any:
					return {}, {}, {}
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     str:
					lowercase_       =					load_image(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					image.size
					lowercase_       =					self.image_processor(images=SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
					return model_inputs
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Dict:
					lowercase_       =					self.model(**SCREAMING_SNAKE_CASE_       )
					return model_outputs




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






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
						print(solution())




