


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							(      A__      ):
							_lowercase     :      Dict  				=			['''vqvae''']

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





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       int:





									"""simple docstring"""







									return 50 if isinstance(self.scheduler   ,						__snake_case							) else 1_000

							@torch.no_grad()
							def __call__(							self:     Tuple   ,						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_:     Optional[int]=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(__snake_case							)
									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=__snake_case   ,						device=self.device   ,						)
									lowercase__						=				noise
									lowercase__						=				None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(__snake_case   ,						__snake_case							)
											lowercase__						=				self.mel.audio_slice_to_image(__snake_case							)
											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(__snake_case   ,						0							)							).latent_dist.sample(
													    generator=__snake_case							)[0]
													lowercase__						=				self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
													lowercase__						=				self.scheduler.add_noise(__snake_case   ,						__snake_case   ,						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(__snake_case   ,						__snake_case   ,						torch.tensor(self.scheduler.timesteps[start_step:]							)							)

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
											if isinstance(self.unet   ,						__snake_case							):
													lowercase__						=				self.unet(__snake_case   ,						__snake_case   ,						__snake_case							)['''sample''']
											else:
													lowercase__						=				self.unet(__snake_case   ,						__snake_case							)['''sample''']

											if isinstance(self.scheduler   ,						__snake_case							):
													lowercase__						=				self.scheduler.step(
													    model_output=__snake_case   ,						timestep=__snake_case   ,						sample=__snake_case   ,						eta=__snake_case   ,						generator=__snake_case   ,						)['''prev_sample''']
											else:
													lowercase__						=				self.scheduler.step(
													    model_output=__snake_case   ,						timestep=__snake_case   ,						sample=__snake_case   ,						generator=__snake_case   ,						)['''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(__snake_case							)['''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(__snake_case   ,						mode='''RGB'''							).convert('''L'''							) for _ in images)							)

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

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

							@torch.no_grad()
							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     List[Image.Image]   ,						UpperCamelCase_:     int = 50							)      ->       np.ndarray:





									"""simple docstring"""







									assert isinstance(self.scheduler   ,						__snake_case							)
									self.scheduler.set_timesteps(__snake_case							)
									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(__snake_case							).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(__snake_case   ,						__snake_case							)['''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(__snake_case							)   ,						torch.flatten(__snake_case							)							) / torch.norm(__snake_case							) / torch.norm(__snake_case							)							)
									return sin((1 - alpha) * theta							) * xa / sin(__snake_case							) + sin(alpha * theta							) * xa / sin(__snake_case							)






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


if __name__ == "__main__":
			print(f"""{solution() = }""")


