





"""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_     :						Union[str, Any]						=					logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase__		)
class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """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_  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      **snake_case_  ):

        """simple docstring"""

        return {}, {}, {}

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  load_image(snake_case_  )
        A_  :       Union[str, Any]										=  image.size
        A_  :       Optional[int]										=  self.image_processor(images=snake_case_					,      return_tensors=self.framework  )
        return model_inputs

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[int]										=  self.model(**snake_case_  )
        return model_outputs

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

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






"""simple docstring"""




import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_     :						List[str]						=					[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Union[str, Any]										=  os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
  A_  :       Tuple										=  os.path.join(_UpperCAmelCase ,					'words.txt'					)

  A_  :       List[Any]										=  ''
  with open(_UpperCAmelCase					) as f:
    A_  :       int										=  f.readline()

  A_  :       Optional[Any]										=  [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
  A_  :       Dict										=  [
      word
      for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
      if word in TRIANGULAR_NUMBERS
  ]
  return len(_UpperCAmelCase					)


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