


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			['''vqvae''']

					def __init__(self   :						str				, _UpperCAmelCase   :						AutoencoderKL				, _UpperCAmelCase   :						UNetaDConditionModel				, _UpperCAmelCase   :						Mel				, _UpperCAmelCase   :						Union[DDIMScheduler, DDPMScheduler]				, )						->       int:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, mel=_UpperCAmelCase				, vqvae=_UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


							return 50 if isinstance(self.scheduler				, _UpperCAmelCase		) else 1000

					@torch.no_grad()
					def __call__(self   :						int				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						str = None				, _UpperCAmelCase   :						np.ndarray = None				, _UpperCAmelCase   :						int = 0				, _UpperCAmelCase   :						int = 0				, _UpperCAmelCase   :						int = None				, _UpperCAmelCase   :						torch.Generator = None				, _UpperCAmelCase   :						float = 0				, _UpperCAmelCase   :						float = 0				, _UpperCAmelCase   :						torch.Generator = None				, _UpperCAmelCase   :						float = 0				, _UpperCAmelCase   :						torch.Tensor = None				, _UpperCAmelCase   :						torch.Tensor = None				, _UpperCAmelCase   :						Union[str, Any]=True				, )						->       Union[
					    Union[AudioPipelineOutput, ImagePipelineOutput],
					    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
					]:




							"""simple docstring"""


							lowercase__					      =		steps or self.get_default_steps()
							self.scheduler.set_timesteps(_UpperCAmelCase		)
							lowercase__					      =		step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size		) == int:
									lowercase__					      =		(self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
									lowercase__					      =		randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )				, generator=_UpperCAmelCase				, device=self.device				, )
							lowercase__					      =		noise
							lowercase__					      =		None

							if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(_UpperCAmelCase				, _UpperCAmelCase		)
									lowercase__					      =		self.mel.audio_slice_to_image(_UpperCAmelCase		)
									lowercase__					      =		np.frombuffer(input_image.tobytes()				, dtype="""uint8"""		).reshape(
									    (input_image.height, input_image.width)		)
									lowercase__					      =		(input_image / 255) * 2 - 1
									lowercase__					      =		torch.tensor(input_image[np.newaxis, :, :]				, dtype=torch.float		).to(self.device		)

									if self.vqvae is not None:
											lowercase__					      =		self.vqvae.encode(torch.unsqueeze(_UpperCAmelCase				, 0		)		).latent_dist.sample(
											    generator=_UpperCAmelCase		)[0]
											lowercase__					      =		self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
											lowercase__					      =		self.scheduler.add_noise(_UpperCAmelCase				, _UpperCAmelCase				, self.scheduler.timesteps[start_step - 1]		)

									lowercase__					      =		(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									lowercase__					      =		int(mask_start_secs * pixels_per_second		)
									lowercase__					      =		int(mask_end_secs * pixels_per_second		)
									lowercase__					      =		self.scheduler.add_noise(_UpperCAmelCase				, _UpperCAmelCase				, torch.tensor(self.scheduler.timesteps[start_step:]		)		)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
									if isinstance(self.unet				, _UpperCAmelCase		):
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)["""sample"""]
									else:
											lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		)["""sample"""]

									if isinstance(self.scheduler				, _UpperCAmelCase		):
											lowercase__					      =		self.scheduler.step(
											    model_output=_UpperCAmelCase				, timestep=_UpperCAmelCase				, sample=_UpperCAmelCase				, eta=_UpperCAmelCase				, generator=_UpperCAmelCase				, )["""prev_sample"""]
									else:
											lowercase__					      =		self.scheduler.step(
											    model_output=_UpperCAmelCase				, timestep=_UpperCAmelCase				, sample=_UpperCAmelCase				, generator=_UpperCAmelCase				, )["""prev_sample"""]

									if mask is not None:
											if mask_start > 0:
													lowercase__					      =		mask[:, step, :, :mask_start]
											if mask_end > 0:
													lowercase__					      =		mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
									# 0.18215 was scaling factor used in training to ensure unit variance
									lowercase__					      =		1 / self.vqvae.config.scaling_factor * images
									lowercase__					      =		self.vqvae.decode(_UpperCAmelCase		)["""sample"""]

							lowercase__					      =		(images / 2 + 0.5).clamp(0				, 1		)
							lowercase__					      =		images.cpu().permute(0				, 2				, 3				, 1		).numpy()
							lowercase__					      =		(images * 255).round().astype("""uint8"""		)
							lowercase__					      =		list(
							    (Image.fromarray(_[:, :, 0]		) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(_UpperCAmelCase				, mode="""RGB"""		).convert("""L"""		) for _ in images)		)

							lowercase__					      =		[self.mel.image_to_audio(_UpperCAmelCase		) for _ in images]
							if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(_UpperCAmelCase		)[:, np.newaxis, :]		)				, **ImagePipelineOutput(_UpperCAmelCase		)		)

					@torch.no_grad()
					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						List[Image.Image]				, _UpperCAmelCase   :						int = 50		)						->       np.ndarray:




							"""simple docstring"""


							assert isinstance(self.scheduler				, _UpperCAmelCase		)
							self.scheduler.set_timesteps(_UpperCAmelCase		)
							lowercase__					      =		np.array(
							    [np.frombuffer(image.tobytes()				, dtype="""uint8"""		).reshape((1, image.height, image.width)		) for image in images]		)
							lowercase__					      =		(sample / 255) * 2 - 1
							lowercase__					      =		torch.Tensor(_UpperCAmelCase		).to(self.device		)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps				, (0,)		)		):
									lowercase__					      =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									lowercase__					      =		self.scheduler.alphas_cumprod[t]
									lowercase__					      =		(
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									lowercase__					      =		1 - alpha_prod_t
									lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase		)["""sample"""]
									lowercase__					      =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
									lowercase__					      =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									lowercase__					      =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample


					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						torch.Tensor				, _UpperCAmelCase   :						torch.Tensor				, _UpperCAmelCase   :						float		)						->       torch.Tensor:




							"""simple docstring"""


							lowercase__					      =		acos(torch.dot(torch.flatten(_UpperCAmelCase		)				, torch.flatten(_UpperCAmelCase		)		) / torch.norm(_UpperCAmelCase		) / torch.norm(_UpperCAmelCase		)		)
							return sin((1 - alpha) * theta		) * xa / sin(_UpperCAmelCase		) + sin(alpha * theta		) * xa / sin(_UpperCAmelCase		)





class 							A   :
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						list		)						->       None:




							"""simple docstring"""


							lowercase__					      =		set_counts
							lowercase__					      =		max(_UpperCAmelCase		)
							lowercase__					      =		len(_UpperCAmelCase		)
							lowercase__					      =		[1] * num_sets
							lowercase__					      =		list(range(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int		)						->       bool:




							"""simple docstring"""


							lowercase__					      =		self.get_parent(_UpperCAmelCase		)
							lowercase__					      =		self.get_parent(_UpperCAmelCase		)

							if src_parent == dst_parent:
									return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
									self.set_counts[dst_parent] += self.set_counts[src_parent]
									lowercase__					      =		0
									lowercase__					      =		dst_parent
									if self.ranks[dst_parent] == self.ranks[src_parent]:
											self.ranks[dst_parent] += 1
									lowercase__					      =		self.set_counts[dst_parent]
							else:
									self.set_counts[src_parent] += self.set_counts[dst_parent]
									lowercase__					      =		0
									lowercase__					      =		src_parent
									lowercase__					      =		self.set_counts[src_parent]

							lowercase__					      =		max(self.max_set				, _UpperCAmelCase		)
							return True


					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int		)						->       int:




							"""simple docstring"""


							if self.parents[disj_set] == disj_set:
									return disj_set
							lowercase__					      =		self.get_parent(self.parents[disj_set]		)
							return self.parents[disj_set]

