



"""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      ):





     '''simple docstring'''



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





     @property
     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Tuple											=    UNetaDModel(
             sample_size=(3_2, 6_4)       ,	in_channels=1       ,	out_channels=1       ,	layers_per_block=2       ,	block_out_channels=(1_2_8, 1_2_8)       ,	down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,	up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,	)
         return model





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             sample_size=(6_4, 3_2)       ,	in_channels=1       ,	out_channels=1       ,	layers_per_block=2       ,	block_out_channels=(1_2_8, 1_2_8)       ,	down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')       ,	up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')       ,	cross_attention_dim=1_0       ,	)
         return model





     @property
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoencoderKL(
             sample_size=(1_2_8, 6_4)       ,	in_channels=1       ,	out_channels=1       ,	latent_channels=1       ,	layers_per_block=2       ,	block_out_channels=(1_2_8, 1_2_8)       ,	down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')       ,	up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')       ,	)
         _UpperCAmelCase							:  Tuple											=    UNetaDModel(
             sample_size=(6_4, 3_2)       ,	in_channels=1       ,	out_channels=1       ,	layers_per_block=2       ,	block_out_channels=(1_2_8, 1_2_8)       ,	down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')       ,	up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')       ,	)
         return vqvae, unet





     @slow
     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  Optional[int]											=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _UpperCAmelCase							:  Any											=    Mel(
             x_res=self.dummy_unet.config.sample_size[1]       ,	y_res=self.dummy_unet.config.sample_size[0]       ,	)

         _UpperCAmelCase							:  Tuple											=    DDPMScheduler()
         _UpperCAmelCase							:  str											=    AudioDiffusionPipeline(vqvae=A       ,	unet=self.dummy_unet       ,	mel=A       ,	scheduler=A		)
         _UpperCAmelCase							:  Optional[int]											=    pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Tuple											=    torch.Generator(device=A		).manual_seed(4_2		)
         _UpperCAmelCase							:  Optional[int]											=    pipe(generator=A       ,	steps=4		)
         _UpperCAmelCase							:  Dict											=    output.audios[0]
         _UpperCAmelCase							:  Tuple											=    output.images[0]

         _UpperCAmelCase							:  Optional[Any]											=    torch.Generator(device=A		).manual_seed(4_2		)
         _UpperCAmelCase							:  List[str]											=    pipe(generator=A       ,	steps=4       ,	return_dict=A		)
         _UpperCAmelCase							:  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]
         )
         _UpperCAmelCase							:  Optional[Any]											=    np.frombuffer(image.tobytes()       ,	dtype='''uint8'''		)[:1_0]
         _UpperCAmelCase							:  Optional[Any]											=    np.frombuffer(image_from_tuple.tobytes()       ,	dtype='''uint8'''		)[:1_0]
         _UpperCAmelCase							:  Optional[int]											=    np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]		)

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

         _UpperCAmelCase							:  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]       ,	)

         _UpperCAmelCase							:  Any											=    DDIMScheduler()
         _UpperCAmelCase							:  Optional[int]											=    self.dummy_vqvae_and_unet
         _UpperCAmelCase							:  Optional[int]											=    AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]       ,	unet=dummy_vqvae_and_unet[1]       ,	mel=A       ,	scheduler=A		)
         _UpperCAmelCase							:  str											=    pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         np.random.seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    np.random.uniform(-1       ,	1       ,	((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)		)
         _UpperCAmelCase							:  Optional[int]											=    torch.Generator(device=A		).manual_seed(4_2		)
         _UpperCAmelCase							:  List[str]											=    pipe(raw_audio=A       ,	generator=A       ,	start_step=5       ,	steps=1_0		)
         _UpperCAmelCase							:  Dict											=    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							:  Dict											=    np.frombuffer(image.tobytes()       ,	dtype='''uint8'''		)[:1_0]
         _UpperCAmelCase							:  List[Any]											=    np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]		)

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

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

         np.random.seed(0		)
         _UpperCAmelCase							:  Dict											=    torch.rand((1, 1, 1_0)		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe(generator=A       ,	encoding=A		)
         _UpperCAmelCase							:  Optional[int]											=    output.images[0]
         _UpperCAmelCase							:  int											=    np.frombuffer(image.tobytes()       ,	dtype='''uint8'''		)[:1_0]
         _UpperCAmelCase							:  Tuple											=    np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]		)

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



@slow
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Union[str, Any]											=    torch_device

         _UpperCAmelCase							:  int											=    DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''		)
         _UpperCAmelCase							:  Any											=    pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Tuple											=    torch.Generator(device=A		).manual_seed(4_2		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe(generator=A		)
         _UpperCAmelCase							:  Dict											=    output.audios[0]
         _UpperCAmelCase							:  Optional[int]											=    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							:  str											=    np.frombuffer(image.tobytes()       ,	dtype='''uint8'''		)[:1_0]
         _UpperCAmelCase							:  Dict											=    np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]		)

         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


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Any     =					[
      'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ViTMSNModel',
      'ViTMSNForImageClassification',
      'ViTMSNPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


