




'''simple docstring'''






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





     @property
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           torch.manual_seed(0			)
           snake_case__       :   List[str]	      =    UNetaDModel(
               sample_size=(32, 64)     ,							in_channels=1     ,							out_channels=1     ,							layers_per_block=2     ,							block_out_channels=(128, 128)     ,							down_block_types=('AttnDownBlock2D', 'DownBlock2D')     ,							up_block_types=('UpBlock2D', 'AttnUpBlock2D')     ,							)
           return model





     @property
     def 			_lowerCAmelCase      (     self     :					List[str]			):
           torch.manual_seed(0			)
           snake_case__       :   List[str]	      =    UNetaDConditionModel(
               sample_size=(64, 32)     ,							in_channels=1     ,							out_channels=1     ,							layers_per_block=2     ,							block_out_channels=(128, 128)     ,							down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')     ,							up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')     ,							cross_attention_dim=10     ,							)
           return model





     @property
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           torch.manual_seed(0			)
           snake_case__       :   str	      =    AutoencoderKL(
               sample_size=(128, 64)     ,							in_channels=1     ,							out_channels=1     ,							latent_channels=1     ,							layers_per_block=2     ,							block_out_channels=(128, 128)     ,							down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')     ,							up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')     ,							)
           snake_case__       :   int	      =    UNetaDModel(
               sample_size=(64, 32)     ,							in_channels=1     ,							out_channels=1     ,							layers_per_block=2     ,							block_out_channels=(128, 128)     ,							down_block_types=('AttnDownBlock2D', 'DownBlock2D')     ,							up_block_types=('UpBlock2D', 'AttnUpBlock2D')     ,							)
           return vqvae, unet





     @slow
     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   List[Any]	      =    "cpu"  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   Dict	      =    Mel(
               x_res=self.dummy_unet.config.sample_size[1]     ,							y_res=self.dummy_unet.config.sample_size[0]     ,							)

           snake_case__       :   str	      =    DDPMScheduler()
           snake_case__       :   Any	      =    AudioDiffusionPipeline(vqvae=snake_case__     ,							unet=self.dummy_unet     ,							mel=snake_case__     ,							scheduler=snake_case__			)
           snake_case__       :   int	      =    pipe.to(snake_case__			)
           pipe.set_progress_bar_config(disable=snake_case__			)

           snake_case__       :   Optional[Any]	      =    torch.Generator(device=snake_case__			).manual_seed(42			)
           snake_case__       :   Dict	      =    pipe(generator=snake_case__     ,							steps=4			)
           snake_case__       :   Dict	      =    output.audios[0]
           snake_case__       :   str	      =    output.images[0]

           snake_case__       :   Any	      =    torch.Generator(device=snake_case__			).manual_seed(42			)
           snake_case__       :   Dict	      =    pipe(generator=snake_case__     ,							steps=4     ,							return_dict=snake_case__			)
           snake_case__       :   Any	      =    output[0][0]

           assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
           assert (
               image.height == self.dummy_unet.config.sample_size[0]
               and image.width == self.dummy_unet.config.sample_size[1]
           )
           snake_case__       :   Optional[Any]	      =    np.frombuffer(image.tobytes()     ,							dtype='uint8'			)[:10]
           snake_case__       :   Tuple	      =    np.frombuffer(image_from_tuple.tobytes()     ,							dtype='uint8'			)[:10]
           snake_case__       :   List[Any]	      =    np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]			)

           assert np.abs(image_slice.flatten() - expected_slice			).max() == 0
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() == 0

           snake_case__       :   Union[str, Any]	      =    Mel(
               x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]     ,							y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]     ,							)

           snake_case__       :   str	      =    DDIMScheduler()
           snake_case__       :   Optional[int]	      =    self.dummy_vqvae_and_unet
           snake_case__       :   Tuple	      =    AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]     ,							unet=dummy_vqvae_and_unet[1]     ,							mel=snake_case__     ,							scheduler=snake_case__			)
           snake_case__       :   Optional[Any]	      =    pipe.to(snake_case__			)
           pipe.set_progress_bar_config(disable=snake_case__			)

           np.random.seed(0			)
           snake_case__       :   Tuple	      =    np.random.uniform(-1     ,							1     ,							((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
           snake_case__       :   Union[str, Any]	      =    torch.Generator(device=snake_case__			).manual_seed(42			)
           snake_case__       :   Optional[Any]	      =    pipe(raw_audio=snake_case__     ,							generator=snake_case__     ,							start_step=5     ,							steps=10			)
           snake_case__       :   str	      =    output.images[0]

           assert (
               image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
               and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
           )
           snake_case__       :   Dict	      =    np.frombuffer(image.tobytes()     ,							dtype='uint8'			)[:10]
           snake_case__       :   Any	      =    np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]			)

           assert np.abs(image_slice.flatten() - expected_slice			).max() == 0

           snake_case__       :   Tuple	      =    self.dummy_unet_condition
           snake_case__       :   Union[str, Any]	      =    AudioDiffusionPipeline(
               vqvae=self.dummy_vqvae_and_unet[0]     ,							unet=snake_case__     ,							mel=snake_case__     ,							scheduler=snake_case__			)
           snake_case__       :   int	      =    pipe.to(snake_case__			)
           pipe.set_progress_bar_config(disable=snake_case__			)

           np.random.seed(0			)
           snake_case__       :   Dict	      =    torch.rand((1, 1, 10)			)
           snake_case__       :   Dict	      =    pipe(generator=snake_case__     ,							encoding=snake_case__			)
           snake_case__       :   Union[str, Any]	      =    output.images[0]
           snake_case__       :   Any	      =    np.frombuffer(image.tobytes()     ,							dtype='uint8'			)[:10]
           snake_case__       :   Optional[int]	      =    np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]			)

           assert np.abs(image_slice.flatten() - expected_slice			).max() == 0






@slow
@require_torch_gpu
class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					str			):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   List[str]	      =    torch_device

           snake_case__       :   str	      =    DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'			)
           snake_case__       :   Optional[Any]	      =    pipe.to(snake_case__			)
           pipe.set_progress_bar_config(disable=snake_case__			)

           snake_case__       :   Optional[int]	      =    torch.Generator(device=snake_case__			).manual_seed(42			)
           snake_case__       :   Tuple	      =    pipe(generator=snake_case__			)
           snake_case__       :   Any	      =    output.audios[0]
           snake_case__       :   List[str]	      =    output.images[0]

           assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
           assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
           snake_case__       :   Union[str, Any]	      =    np.frombuffer(image.tobytes()     ,							dtype='uint8'			)[:10]
           snake_case__       :   List[Any]	      =    np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]			)

           assert np.abs(image_slice.flatten() - expected_slice			).max() == 0








"""simple docstring"""



from __future__ import annotations

from math import pow, sqrt


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : float			,					snake_case       : float			,					snake_case       : float				)->		dict[str, float]:






     '''simple docstring'''



     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()


