"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE  (     unittest.TestCase						):







 """simple docstring"""


 @property
 def 							_lowerCAmelCase					( self		:   Tuple					)					->      int:




    '''simple docstring'''






    torch.manual_seed(0					)
    a__  					=		UNetaDModel(
        block_out_channels=(32, 64)       , layers_per_block=2       , sample_size=32       , in_channels=3       , out_channels=3       , down_block_types=('DownBlock2D', 'AttnDownBlock2D')       , up_block_types=('AttnUpBlock2D', 'UpBlock2D')       , )
    return model



 def 							_lowerCAmelCase					( self		:   Any					)					->      Any:




    '''simple docstring'''






    a__  					=		self.dummy_uncond_unet
    a__  					=		KarrasVeScheduler()

    a__  					=		KarrasVePipeline(unet=_a       , scheduler=_a					)
    pipe.to(_a					)
    pipe.set_progress_bar_config(disable=_a					)

    a__  					=		torch.manual_seed(0					)
    a__  					=		pipe(num_inference_steps=2       , generator=_a       , output_type='numpy'					).images

    a__  					=		torch.manual_seed(0					)
    a__  					=		pipe(num_inference_steps=2       , generator=_a       , output_type='numpy'       , return_dict=_a					)[0]

    a__  					=		image[0, -3:, -3:, -1]
    a__  					=		image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    a__  					=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

    assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2

@slow
@require_torch
class SCREAMING_SNAKE_CASE  (     unittest.TestCase						):







 """simple docstring"""


 def 							_lowerCAmelCase					( self		:   List[str]					)					->      Optional[Any]:




    '''simple docstring'''






    a__  					=		"google/ncsnpp-celebahq-256"
    a__  					=		UNetaDModel.from_pretrained(_a					)
    a__  					=		KarrasVeScheduler()

    a__  					=		KarrasVePipeline(unet=_a       , scheduler=_a					)
    pipe.to(_a					)
    pipe.set_progress_bar_config(disable=_a					)

    a__  					=		torch.manual_seed(0					)
    a__  					=		pipe(num_inference_steps=20       , generator=_a       , output_type='numpy'					).images

    a__  					=		image[0, -3:, -3:, -1]
    assert image.shape == (1, 256, 256, 3)
    a__  					=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

    assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


