





"""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 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							@property
							def        __a  (  self )      ->					str:
								torch.manual_seed(0 )
								a :						Union[str, Any]      =							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        __a  (  self )      ->					Optional[Any]:
								torch.manual_seed(0 )
								a :						Dict      =							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        __a  (  self )      ->					Tuple:
								torch.manual_seed(0 )
								a :						List[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") ,   )
								a :						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        __a  (  self )      ->					Any:
								a :						str      =							'cpu'  # ensure determinism for the device-dependent torch.Generator
								a :						Optional[int]      =							Mel(
								    x_res=self.dummy_unet.config.sample_size[1] ,   y_res=self.dummy_unet.config.sample_size[0] ,   )

								a :						List[str]      =							DDPMScheduler()
								a :						List[Any]      =							AudioDiffusionPipeline(vqvae=lowerCAmelCase__ ,   unet=self.dummy_unet ,   mel=lowerCAmelCase__ ,   scheduler=lowerCAmelCase__ )
								a :						List[Any]      =							pipe.to(lowerCAmelCase__ )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

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

								a :						Dict      =							torch.Generator(device=lowerCAmelCase__ ).manual_seed(42 )
								a :						Dict      =							pipe(generator=lowerCAmelCase__ ,   steps=4 ,   return_dict=lowerCAmelCase__ )
								a :						List[str]      =							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]
								)
								a :						Optional[Any]      =							np.frombuffer(image.tobytes() ,   dtype="uint8" )[:10]
								a :						Dict      =							np.frombuffer(image_from_tuple.tobytes() ,   dtype="uint8" )[:10]
								a :						List[str]      =							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

								a :						List[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] ,   )

								a :						Dict      =							DDIMScheduler()
								a :						Tuple      =							self.dummy_vqvae_and_unet
								a :						List[str]      =							AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0] ,   unet=dummy_vqvae_and_unet[1] ,   mel=lowerCAmelCase__ ,   scheduler=lowerCAmelCase__ )
								a :						Tuple      =							pipe.to(lowerCAmelCase__ )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

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

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

								a :						Dict      =							self.dummy_unet_condition
								a :						Optional[int]      =							AudioDiffusionPipeline(
								    vqvae=self.dummy_vqvae_and_unet[0] ,   unet=lowerCAmelCase__ ,   mel=lowerCAmelCase__ ,   scheduler=lowerCAmelCase__ )
								a :						Dict      =							pipe.to(lowerCAmelCase__ )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

								np.random.seed(0 )
								a :						Optional[int]      =							torch.rand((1, 1, 10) )
								a :						Optional[Any]      =							pipe(generator=lowerCAmelCase__ ,   encoding=lowerCAmelCase__ )
								a :						Dict      =							output.images[0]
								a :						Tuple      =							np.frombuffer(image.tobytes() ,   dtype="uint8" )[:10]
								a :						List[str]      =							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		):






							def        __a  (  self )      ->					Tuple:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def        __a  (  self )      ->					List[str]:
								a :						Optional[Any]      =							torch_device

								a :						Dict      =							DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
								a :						List[str]      =							pipe.to(lowerCAmelCase__ )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__ )

								a :						int      =							torch.Generator(device=lowerCAmelCase__ ).manual_seed(42 )
								a :						Tuple      =							pipe(generator=lowerCAmelCase__ )
								a :						Dict      =							output.audios[0]
								a :						Dict      =							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]
								a :						Optional[int]      =							np.frombuffer(image.tobytes() ,   dtype="uint8" )[:10]
								a :						int      =							np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )

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








"""simple docstring"""






def 	_snake_case     (							lowercase__ = 1				,       lowercase__ = 1000						):
				_lowerCamelCase	:     Optional[int]       =       1
				_lowerCamelCase	:     List[Any]       =       0

				for divide_by_number in range(lowercase__				,       digit + 1						):
								_lowerCamelCase	:     list[int]       =       []
								_lowerCamelCase	:     Dict       =       numerator
								for _ in range(1				,       digit + 1						):
												if now_divide in has_been_divided:
																if longest_list_length < len(lowercase__						):
																				_lowerCamelCase	:     Any       =       len(lowercase__						)
																				_lowerCamelCase	:     Any       =       divide_by_number
												else:
																has_been_divided.append(lowercase__						)
																_lowerCamelCase	:     int       =       now_divide * 10 % divide_by_number

				return the_digit


# Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()