

'''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		       =    logging.get_logger(__name__)


@add_end_docstrings(_snake_case			)
class 					A__      (      _snake_case			):



    def __init__(					self		,				*UpperCamelCase__		,				**UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           super().__init__(*UpperCamelCase__		,				**UpperCamelCase__       )
           requires_backends(self		,				"""vision"""       )
           self.check_model_type(UpperCamelCase__       )



    def __call__(					self		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           return super().__call__(UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				**UpperCamelCase__       )						->      List[str]:





           '''simple docstring'''
           return {}, {}, {}



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      load_image(UpperCamelCase__       )
           A_						=      image.size
           A_						=      self.image_processor(images=UpperCamelCase__		,				return_tensors=self.framework       )
           return model_inputs



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      self.model(**UpperCamelCase__       )
           return model_outputs







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      model_outputs.predicted_depth
           A_						=      torch.nn.functional.interpolate(
               predicted_depth.unsqueeze(1       )		,				size=self.image_size[::-1]		,				mode="""bicubic"""		,				align_corners=UpperCamelCase__       )
           A_						=      prediction.squeeze().cpu().numpy()
           A_						=      (output * 255 / np.max(UpperCamelCase__       )).astype("""uint8"""       )
           A_						=      Image.fromarray(UpperCamelCase__       )
           A_						=      {}
           A_						=      predicted_depth
           A_						=      depth
           return output_dict



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       assert (
           isinstance(UpperCAmelCase__,       UpperCAmelCase__				) and number_of_steps > 0
       ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
       if number_of_steps == 1:
              return 1
       A_					,			A_						=      1, 1
       for _ in range(number_of_steps - 1				):
              A_					,			A_						=      current + previous, current
       return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()
