

'''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

lowerCamelCase			:				List[Any] 			=					logging.get_logger(__name__)







@add_end_docstrings(A__  )
class 							A__   (	A__  ):





  def __init__(		self				:     Union[str, Any]							, *_a				:     str							, **_a				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






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





  def __call__(		self				:     List[Any]							, _a				:     Union[str, List[str], "Image.Image", List["Image.Image"]]							, **_a				:     List[Any]			)     ->   List[Any]:





       '''simple docstring'''






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





  def 						A  (		self				:     Tuple							, **_a				:     List[str]			)     ->   int:





       '''simple docstring'''






       return {}, {}, {}





  def 						A  (		self				:     Dict							, _a				:     Optional[int]			)     ->   List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =load_image(_a			)
       _SCREAMING_SNAKE_CASE							   =image.size
       _SCREAMING_SNAKE_CASE							   =self.image_processor(images=_a							, return_tensors=self.framework			)
       return model_inputs





  def 						A  (		self				:     List[str]							, _a				:     Dict			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.model(**_a			)
       return model_outputs





  def 						A  (		self				:     List[str]							, _a				:     Any			)     ->   Optional[Any]:





       '''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=_a			)
       _SCREAMING_SNAKE_CASE							   =prediction.squeeze().cpu().numpy()
       _SCREAMING_SNAKE_CASE							   =(output * 255 / np.max(_a			)).astype('uint8'			)
       _SCREAMING_SNAKE_CASE							   =Image.fromarray(_a			)
       _SCREAMING_SNAKE_CASE							   ={}
       _SCREAMING_SNAKE_CASE							   =predicted_depth
       _SCREAMING_SNAKE_CASE							   =depth
       return output_dict



'''simple docstring'''
import os


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(os.path.realpath(_UpperCamelCase    )    )
     _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'triangle.txt'    )

     with open(_UpperCamelCase    ) as f:
          _SCREAMING_SNAKE_CASE							   =f.readlines()

     _SCREAMING_SNAKE_CASE							   =[]
     for line in triangle:
          _SCREAMING_SNAKE_CASE							   =[]
          for number in line.strip().split(' '    ):
               numbers_from_line.append(int(_UpperCamelCase    )    )
          a.append(_UpperCamelCase    )

     for i in range(1							,			len(_UpperCamelCase    )    ):
          for j in range(len(a[i]    )    ):
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j] if j != len(a[i - 1]    ) else 0
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_UpperCamelCase							,			_UpperCamelCase    )
     return max(a[-1]    )


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