




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				:		Optional[Any]     =   logging.get_logger(__name__)







@add_end_docstrings(__SCREAMING_SNAKE_CASE   )
class   A(						__SCREAMING_SNAKE_CASE   ):



 '''simple docstring'''


 def __init__(				self					:  Optional[int]						,							*A_					:  int						,							**A_					:  Optional[Any]  )   ->	str:

       """simple docstring"""






       super().__init__(*_a						,							**_a  )
       requires_backends(self						,							'vision'  )
       self.check_model_type(_a  )

 def __call__(				self					:  Optional[Any]						,							A_					:  Optional[Any]						,							**A_					:  Tuple  )   ->	Tuple:

       """simple docstring"""






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

 def 				a__ (				self					:  Optional[int]						,							**A_					:  Optional[Any]  )   ->	Dict:

       """simple docstring"""






       return {}, {}, {}

 def 				a__ (				self					:  Optional[Any]						,							A_					:  str  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    load_image(_a  )
       lowerCamelCase_		     =    image.size
       lowerCamelCase_		     =    self.image_processor(images=_a						,							return_tensors=self.framework  )
       return model_inputs

 def 				a__ (				self					:  List[Any]						,							A_					:  Union[str, Any]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.model(**_a  )
       return model_outputs





 def 				a__ (				self					:  Dict						,							A_					:  Any  )   ->	Optional[int]:

       """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=_a  )
       lowerCamelCase_		     =    prediction.squeeze().cpu().numpy()
       lowerCamelCase_		     =    (output * 255 / np.max(_a  )).astype('uint8'  )
       lowerCamelCase_		     =    Image.fromarray(_a  )
       lowerCamelCase_		     =    {}
       lowerCamelCase_		     =    predicted_depth
       lowerCamelCase_		     =    depth
       return output_dict







"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     int			) ->      str:
		if number > 0:
				raise ValueError('''input must be a negative integer'''			)
		__a           =			len(bin(lowerCAmelCase__			)[3:]			)
		__a           =			bin(abs(lowerCAmelCase__			) - (1 << binary_number_length)			)[3:]
		__a           =			(
		    (
		        '''1'''
		        + '''0''' * (binary_number_length - len(lowerCAmelCase__			))
		        + twos_complement_number
		    )
		    if number < 0
		    else '''0'''
		)
		return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()






