



'''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      UpperCamelCase_  (			self :    List[str]			):







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





	@property
	def      UpperCamelCase_  (			self :    int			):







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







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		Optional[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      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		Tuple			    =       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 :		str			    =       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      UpperCamelCase_  (			self :    Dict			):







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

							_UpperCamelCase :		Optional[int]			    =       DDPMScheduler()
							_UpperCamelCase :		List[Any]			    =       AudioDiffusionPipeline(vqvae=lowerCamelCase__			,unet=self.dummy_unet			,mel=lowerCamelCase__			,scheduler=lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Any			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(42			)
							_UpperCamelCase :		Tuple			    =       pipe(generator=lowerCamelCase__			,steps=4			)
							_UpperCamelCase :		int			    =       output.audios[0]
							_UpperCamelCase :		Any			    =       output.images[0]

							_UpperCamelCase :		int			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(42			)
							_UpperCamelCase :		Dict			    =       pipe(generator=lowerCamelCase__			,steps=4			,return_dict=lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       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 :		Union[str, Any]			    =       np.frombuffer(image.tobytes()			,dtype='uint8'			)[:10]
							_UpperCamelCase :		str			    =       np.frombuffer(image_from_tuple.tobytes()			,dtype='uint8'			)[:10]
							_UpperCamelCase :		Union[str, 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

							_UpperCamelCase :		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]			,)

							_UpperCamelCase :		str			    =       DDIMScheduler()
							_UpperCamelCase :		Dict			    =       self.dummy_vqvae_and_unet
							_UpperCamelCase :		Optional[Any]			    =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]			,unet=dummy_vqvae_and_unet[1]			,mel=lowerCamelCase__			,scheduler=lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

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

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

							_UpperCamelCase :		Dict			    =       self.dummy_unet_condition
							_UpperCamelCase :		Optional[Any]			    =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]			,unet=lowerCamelCase__			,mel=lowerCamelCase__			,scheduler=lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							np.random.seed(0			)
							_UpperCamelCase :		Tuple			    =       torch.rand((1, 1, 10)			)
							_UpperCamelCase :		Dict			    =       pipe(generator=lowerCamelCase__			,encoding=lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       output.images[0]
							_UpperCamelCase :		Dict			    =       np.frombuffer(image.tobytes()			,dtype='uint8'			)[:10]
							_UpperCamelCase :		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 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    Optional[int]			):







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





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       torch_device

							_UpperCamelCase :		Union[str, Any]			    =       DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'			)
							_UpperCamelCase :		Union[str, Any]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(42			)
							_UpperCamelCase :		str			    =       pipe(generator=lowerCamelCase__			)
							_UpperCamelCase :		Optional[Any]			    =       output.audios[0]
							_UpperCamelCase :		Optional[Any]			    =       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 :		Tuple			    =       np.frombuffer(image.tobytes()			,dtype='uint8'			)[:10]
							_UpperCamelCase :		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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Dict  	=  _LazyModule(__name__, globals()['__file__'], _import_structure)


