






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase       		=      {
    'configuration_upernet': ['UperNetConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase       		=      [
		    'UperNetForSemanticSegmentation',
		    'UperNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase       		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












'''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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






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

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

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

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, 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]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      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_		:  str											=      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_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

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

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

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      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_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

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




