




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 __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   @property
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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		:						Optional[int]):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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		:						Any):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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''')					,						)
        UpperCAmelCase_          =							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		:						str):

        """simple docstring"""


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

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

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

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

        UpperCAmelCase_          =							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]					,						)

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

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

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

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

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







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							torch_device

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

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

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









'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def        _lowerCAmelCase					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )				)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'							, type=_UpperCamelCase							, default=1							, help='Number of TPU cores to use (1 or 8).'				)

     # positional
     parser.add_argument(
         'training_script'							, type=_UpperCamelCase							, help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )							, )

     # rest from the training program
     parser.add_argument('training_script_args'							, nargs=_UpperCamelCase				)

     return parser.parse_args()


def        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

     # Import training_script as a module.
     _SCREAMING_SNAKE_CASE							    =Path(args.training_script				)
     sys.path.append(str(script_fpath.parent.resolve()				)				)
     _SCREAMING_SNAKE_CASE							    =script_fpath.stem
     _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)

     # Patch sys.argv
     _SCREAMING_SNAKE_CASE							    =[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

     xmp.spawn(mod._mp_fn							, args=()							, nprocs=args.num_cores				)


if __name__ == "__main__":
     main()
