





"""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

__UpperCamelCase     :						Dict						       =  logging.get_logger(__name__)







@add_end_docstrings(a__  )
class       a							(   a__  ):



 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    UpperCamelCase__  (     self				,       **_snake_case     ):







  """simple docstring"""






  return {}, {}, {}



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= model_outputs.predicted_depth
  lowerCAmelCase    				= torch.nn.functional.interpolate(
      predicted_depth.unsqueeze(1     )				,       size=self.image_size[::-1]				,       mode='bicubic'				,       align_corners=_snake_case     )
  lowerCAmelCase    				= prediction.squeeze().cpu().numpy()
  lowerCAmelCase    				= (output * 2_55 / np.max(_snake_case     )).astype('uint8'     )
  lowerCAmelCase    				= Image.fromarray(_snake_case     )
  lowerCAmelCase    				= {}
  lowerCAmelCase    				= predicted_depth
  lowerCAmelCase    				= depth
  return output_dict












"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





