






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    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				@property
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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        lowercase__     (  self		:					str						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								torch.manual_seed(0						)
								lowercase__     				=    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'''),						)
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__     				=    Mel(
								    x_res=self.dummy_unet.config.sample_size[1],						y_res=self.dummy_unet.config.sample_size[0],						)

								lowercase__     				=    DDPMScheduler()
								lowercase__     				=    AudioDiffusionPipeline(vqvae=lowerCamelCase,						unet=self.dummy_unet,						mel=lowerCamelCase,						scheduler=lowerCamelCase						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)

								lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(42						)
								lowercase__     				=    pipe(generator=lowerCamelCase,						steps=4						)
								lowercase__     				=    output.audios[0]
								lowercase__     				=    output.images[0]

								lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(42						)
								lowercase__     				=    pipe(generator=lowerCamelCase,						steps=4,						return_dict=lowerCamelCase						)
								lowercase__     				=    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]
								)
								lowercase__     				=    np.frombuffer(image.tobytes(),						dtype='''uint8'''						)[:10]
								lowercase__     				=    np.frombuffer(image_from_tuple.tobytes(),						dtype='''uint8'''						)[:10]
								lowercase__     				=    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

								lowercase__     				=    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],						)

								lowercase__     				=    DDIMScheduler()
								lowercase__     				=    self.dummy_vqvae_and_unet
								lowercase__     				=    AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0],						unet=dummy_vqvae_and_unet[1],						mel=lowerCamelCase,						scheduler=lowerCamelCase						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)

								np.random.seed(0						)
								lowercase__     				=    np.random.uniform(-1,						1,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
								lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(42						)
								lowercase__     				=    pipe(raw_audio=lowerCamelCase,						generator=lowerCamelCase,						start_step=5,						steps=10						)
								lowercase__     				=    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]
								)
								lowercase__     				=    np.frombuffer(image.tobytes(),						dtype='''uint8'''						)[:10]
								lowercase__     				=    np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

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

								lowercase__     				=    self.dummy_unet_condition
								lowercase__     				=    AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0],						unet=lowerCamelCase,						mel=lowerCamelCase,						scheduler=lowerCamelCase						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)

								np.random.seed(0						)
								lowercase__     				=    torch.rand((1, 1, 10)						)
								lowercase__     				=    pipe(generator=lowerCamelCase,						encoding=lowerCamelCase						)
								lowercase__     				=    output.images[0]
								lowercase__     				=    np.frombuffer(image.tobytes(),						dtype='''uint8'''						)[:10]
								lowercase__     				=    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    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    torch_device

								lowercase__     				=    DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)

								lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(42						)
								lowercase__     				=    pipe(generator=lowerCamelCase						)
								lowercase__     				=    output.audios[0]
								lowercase__     				=    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]
								lowercase__     				=    np.frombuffer(image.tobytes(),						dtype='''uint8'''						)[:10]
								lowercase__     				=    np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

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












from __future__ import annotations






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								if resistor <= 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative or zero value!"""
												raise ValueError(lowerCamelCase_       )
								first_sum += 1 / float(lowerCamelCase_       )
								index += 1
				return 1 / first_sum






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    0.00
				lowercase__     				=    0
				for resistor in resistors:
								sum_r += resistor
								if resistor < 0:
												lowercase__     				=    F"""Resistor at index {index} has a negative value!"""
												raise ValueError(lowerCamelCase_       )
								index += 1
				return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()




