
"""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			):




				def __init__(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							super().__init__(*UpperCAmelCase							,						**UpperCAmelCase   )
							requires_backends(self							,						'''vision'''   )
							self.check_model_type(UpperCAmelCase   )




				def __call__(				self							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							return super().__call__(UpperCAmelCase							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							return {}, {}, {}




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  load_image(UpperCAmelCase   )
							lowerCamelCase_					=  image.size
							lowerCamelCase_					=  self.image_processor(images=UpperCAmelCase							,						return_tensors=self.framework   )
							return model_inputs




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.model(**UpperCAmelCase   )
							return model_outputs





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











from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





