



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

UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)






@add_end_docstrings(_UpperCAmelCase     )
class      UpperCamelCase    (						_UpperCAmelCase     ):



	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 			__A							(       self			,	**UpperCAmelCase__		):
							return {}, {}, {}



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



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       self.model(**UpperCAmelCase__		)
							return model_outputs



	def 			__A							(       self			,	UpperCAmelCase__		):
							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










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





