






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							UNetaDModel
			__a							:							KarrasVeScheduler






			def __init__(   self							:						Optional[int] ,      lowercase							:						UNetaDModel ,      lowercase							:						KarrasVeScheduler			)							->    Union[str, Any]:

									'''simple docstring'''







									super().__init__()
									self.register_modules(unet=lowercase ,      scheduler=lowercase			)







			@torch.no_grad()
			def __call__(   self							:						int ,      lowercase							:						int = 1 ,      lowercase							:						int = 5_0 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      **lowercase							:						str ,      )							->    Union[Tuple, ImagePipelineOutput]:

									'''simple docstring'''







									UpperCamelCase__         = self.unet.config.sample_size
									UpperCamelCase__         = (batch_size, 3, img_size, img_size)

									UpperCamelCase__         = self.unet

									# sample x_0 ~ N(0, sigma_0^2 * I)
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=self.device			) * self.scheduler.init_noise_sigma

									self.scheduler.set_timesteps(lowercase			)

									for t in self.progress_bar(self.scheduler.timesteps			):
															# here sigma_t == t_i from the paper
															UpperCamelCase__         = self.scheduler.schedule[t]
															UpperCamelCase__         = self.scheduler.schedule[t - 1] if t > 0 else 0

															# 1. Select temporarily increased noise level sigma_hat
															# 2. Add new noise to move from sample_i to sample_hat
															UpperCamelCase__    ,   UpperCamelCase__         = self.scheduler.add_noise_to_input(lowercase ,      lowercase ,      generator=lowercase			)

															# 3. Predict the noise residual given the noise magnitude `sigma_hat`
															# The model inputs and output are adjusted by following eq. (213) in [1].
															UpperCamelCase__         = (sigma_hat / 2) * model((sample_hat + 1) / 2 ,      sigma_hat / 2			).sample

															# 4. Evaluate dx/dt at sigma_hat
															# 5. Take Euler step from sigma to sigma_prev
															UpperCamelCase__         = self.scheduler.step(lowercase ,      lowercase ,      lowercase ,      lowercase			)

															if sigma_prev != 0:
																					# 6. Apply 2nd order correction
																					# The model inputs and output are adjusted by following eq. (213) in [1].
																					UpperCamelCase__         = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 ,      sigma_prev / 2			).sample
																					UpperCamelCase__         = self.scheduler.step_correct(
																					    lowercase ,      lowercase ,      lowercase ,      lowercase ,      step_output.prev_sample ,      step_output["""derivative"""] ,      )
															UpperCamelCase__         = step_output.prev_sample

									UpperCamelCase__         = (sample / 2 + 0.5).clamp(0 ,      1			)
									UpperCamelCase__         = sample.cpu().permute(0 ,      2 ,      3 ,      1			).numpy()
									if output_type == "pil":
															UpperCamelCase__         = self.numpy_to_pil(lowercase			)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowercase			)








'''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	):


			'''simple docstring'''





			def 				A      (   self							:						Any			)							->    Any:

									'''simple docstring'''







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






			@property
			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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 				A      (   self							:						List[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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 				A      (   self							:						int			)							->    Optional[Any]:

									'''simple docstring'''







									torch.manual_seed(0			)
									UpperCamelCase__         = 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__         = 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 				A      (   self							:						Tuple			)							->    List[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=lowercase ,      unet=self.dummy_unet ,      mel=lowercase ,      scheduler=lowercase			)
									UpperCamelCase__         = pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(generator=lowercase ,      steps=4			)
									UpperCamelCase__         = output.audios[0]
									UpperCamelCase__         = output.images[0]

									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(generator=lowercase ,      steps=4 ,      return_dict=lowercase			)
									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"""			)[:1_0]
									UpperCamelCase__         = np.frombuffer(image_from_tuple.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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__         = 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=lowercase ,      scheduler=lowercase			)
									UpperCamelCase__         = pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									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=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(raw_audio=lowercase ,      generator=lowercase ,      start_step=5 ,      steps=1_0			)
									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"""			)[:1_0]
									UpperCamelCase__         = 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__         = self.dummy_unet_condition
									UpperCamelCase__         = AudioDiffusionPipeline(
									    vqvae=self.dummy_vqvae_and_unet[0] ,      unet=lowercase ,      mel=lowercase ,      scheduler=lowercase			)
									UpperCamelCase__         = pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									np.random.seed(0			)
									UpperCamelCase__         = torch.rand((1, 1, 1_0)			)
									UpperCamelCase__         = pipe(generator=lowercase ,      encoding=lowercase			)
									UpperCamelCase__         = output.images[0]
									UpperCamelCase__         = np.frombuffer(image.tobytes() ,      dtype="""uint8"""			)[:1_0]
									UpperCamelCase__         = 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    _SCREAMING_SNAKE_CASE							(		unittest.TestCase	):


			'''simple docstring'''





			def 				A      (   self							:						int			)							->    str:

									'''simple docstring'''







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







			def 				A      (   self							:						Any			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = torch_device

									UpperCamelCase__         = DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256"""			)
									UpperCamelCase__         = pipe.to(lowercase			)
									pipe.set_progress_bar_config(disable=lowercase			)

									UpperCamelCase__         = torch.Generator(device=lowercase			).manual_seed(4_2			)
									UpperCamelCase__         = pipe(generator=lowercase			)
									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"""			)[:1_0]
									UpperCamelCase__         = 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
