
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

lowercase  : List[Any]						   =							logging.get_logger(__name__)

@add_end_docstrings(lowercase_					)
class __snake_case		(				lowercase_					):
    def __init__(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           super().__init__(*a__		,**a__				)
           requires_backends(self		,"""vision"""				)
           self.check_model_type(a__				)
    def __call__(  self		,snake_case		,**snake_case				):



           '''simple docstring'''







           return super().__call__(a__		,**a__				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return {}, {}, {}
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				load_image(a__				)
           lowercase							:					List[Any]	     =				image.size
           lowercase							:					int	     =				self.image_processor(images=a__		,return_tensors=self.framework				)
           return model_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.model(**a__				)
           return model_outputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







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




from bisect import bisect
from itertools import accumulate







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Dict	     =				sorted(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     key=lambda SCREAMING_SNAKE_CASE__    : x[0] / x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Optional[Any]	     =				[i[0] for i in r], [i[1] for i in r]
       lowercase							:					Any	     =				list(accumulate(SCREAMING_SNAKE_CASE__    )    )
       lowercase							:					int	     =				bisect(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       return (
           0
           if k == 0
           else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]    )
       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
