

"""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(lowerCAmelCase__   )
class       lowerCamelCase	(       lowerCAmelCase__   ):


  '''simple docstring'''







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


        """simple docstring"""

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



  def __call__(self							,       _lowerCamelCase							,       **_lowerCamelCase	):


        """simple docstring"""

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



  def  _a      (self							,       **_lowerCamelCase	):


        """simple docstring"""

        return {}, {}, {}



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							Optional[int]							=    load_image(_lowerCamelCase	)
        UpperCAmelCase__			:							Any							=    image.size
        UpperCAmelCase__			:							Optional[Any]							=    self.image_processor(images=_lowerCamelCase							,       return_tensors=self.framework	)
        return model_inputs



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    self.model(**_lowerCamelCase	)
        return model_outputs




  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[Any]							=    model_outputs.predicted_depth
        UpperCAmelCase__			:							Any							=    torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1	)							,       size=self.image_size[::-1]							,       mode="""bicubic"""							,       align_corners=_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    prediction.squeeze().cpu().numpy()
        UpperCAmelCase__			:							Optional[int]							=    (output * 255 / np.max(_lowerCamelCase	)).astype("""uint8"""	)
        UpperCAmelCase__			:							List[Any]							=    Image.fromarray(_lowerCamelCase	)
        UpperCAmelCase__			:							int							=    {}
        UpperCAmelCase__			:							Union[str, Any]							=    predicted_depth
        UpperCAmelCase__			:							Union[str, Any]							=    depth
        return output_dict




"""simple docstring"""
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""    )

      UpperCAmelCase__			:							Dict							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"
      UpperCAmelCase__			:							Any							=    str(bin(lowerCAmelCase    )    )[2:]  # remove the leading "0b"

      UpperCAmelCase__			:							Optional[Any]							=    max(len(lowerCAmelCase    )			,	len(lowerCAmelCase    )    )

      return "0b" + "".join(
          str(int(char_a != char_b    )    )
          for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase    )			,	b_binary.zfill(lowerCAmelCase    )    )    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

