





'''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

a   	=    logging.get_logger(__name__)







@add_end_docstrings(a__      )
class __a			( a__      ):





   def __init__(       self					:      List[str]					,*lowerCamelCase					:      Optional[Any]					,**lowerCamelCase					:      Any     ):

      '''simple docstring'''

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





   def __call__(       self					:      str					,lowerCamelCase					:      int					,**lowerCamelCase					:      int     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[Any]					,**lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      return {}, {}, {}





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							load_image(_snake_case     )
      __SCREAMING_SNAKE_CASE   				=							image.size
      __SCREAMING_SNAKE_CASE   				=							self.image_processor(images=_snake_case					,return_tensors=self.framework     )
      return model_inputs





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model(**_snake_case     )
      return model_outputs





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      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=_snake_case     )
      __SCREAMING_SNAKE_CASE   				=							prediction.squeeze().cpu().numpy()
      __SCREAMING_SNAKE_CASE   				=							(output * 255 / np.max(_snake_case     )).astype("""uint8"""     )
      __SCREAMING_SNAKE_CASE   				=							Image.fromarray(_snake_case     )
      __SCREAMING_SNAKE_CASE   				=							{}
      __SCREAMING_SNAKE_CASE   				=							predicted_depth
      __SCREAMING_SNAKE_CASE   				=							depth
      return output_dict












"""simple docstring"""
from __future__ import annotations

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list[float]			):
 lowerCAmelCase    				= 0.00
 lowerCAmelCase    				= 0
 for resistor in resistors:
  if resistor <= 0:
   lowerCAmelCase    				= F'Resistor at index {index} has a negative or zero value!'
   raise ValueError(_UpperCAmelCase			)
  first_sum += 1 / float(_UpperCAmelCase			)
  index += 1
 return 1 / first_sum

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    list[float]			):
 lowerCAmelCase    				= 0.00
 lowerCAmelCase    				= 0
 for resistor in resistors:
  sum_r += resistor
  if resistor < 0:
   lowerCAmelCase    				= F'Resistor at index {index} has a negative value!'
   raise ValueError(_UpperCAmelCase			)
  index += 1
 return sum_r


if __name__ == "__main__":
       import doctest

       doctest.testmod()





