






"""simple docstring"""



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 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["vqvae"]




					def __init__(				self     :						Dict    ,						A     :						AutoencoderKL    ,						A     :						UNetaDConditionModel    ,						A     :						Mel    ,						A     :						Union[DDIMScheduler, DDPMScheduler]    ,						)  ->  List[str]:
							super().__init__()
							self.register_modules(unet=A    ,						scheduler=A    ,						mel=A    ,						vqvae=A   )




					def   A  (				self     :						List[str]   )  ->  int:
							return 50 if isinstance(self.scheduler    ,						A   ) else 10_00




					@torch.no_grad()
					def __call__(				self     :						List[str]    ,						A     :						int = 1    ,						A     :						str = None    ,						A     :						np.ndarray = None    ,						A     :						int = 0    ,						A     :						int = 0    ,						A     :						int = None    ,						A     :						torch.Generator = None    ,						A     :						float = 0    ,						A     :						float = 0    ,						A     :						torch.Generator = None    ,						A     :						float = 0    ,						A     :						torch.Tensor = None    ,						A     :						torch.Tensor = None    ,						A     :						Tuple=True    ,						)  ->  Union[
					    Union[AudioPipelineOutput, ImagePipelineOutput],
					    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
					]:
							lowercase_ : Dict							       =   steps or self.get_default_steps()
							self.scheduler.set_timesteps(A   )
							lowercase_ : List[Any]							       =   step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size   ) == int:
									lowercase_ : List[Any]							       =   (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
									lowercase_ : int							       =   randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )    ,						generator=A    ,						device=self.device    ,						)
							lowercase_ : Dict							       =   noise
							lowercase_ : List[Any]							       =   None

							if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(A    ,						A   )
									lowercase_ : int							       =   self.mel.audio_slice_to_image(A   )
									lowercase_ : Tuple							       =   np.frombuffer(input_image.tobytes()    ,						dtype='''uint8'''   ).reshape(
									    (input_image.height, input_image.width)   )
									lowercase_ : Any							       =   (input_image / 2_55) * 2 - 1
									lowercase_ : int							       =   torch.tensor(input_image[np.newaxis, :, :]    ,						dtype=torch.float   ).to(self.device   )

									if self.vqvae is not None:
											lowercase_ : str							       =   self.vqvae.encode(torch.unsqueeze(A    ,						0   )   ).latent_dist.sample(
											    generator=A   )[0]
											lowercase_ : Optional[Any]							       =   self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
											lowercase_ : Any							       =   self.scheduler.add_noise(A    ,						A    ,						self.scheduler.timesteps[start_step - 1]   )

									lowercase_ : Optional[Any]							       =   (
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									lowercase_ : Optional[Any]							       =   int(mask_start_secs * pixels_per_second   )
									lowercase_ : int							       =   int(mask_end_secs * pixels_per_second   )
									lowercase_ : Union[str, Any]							       =   self.scheduler.add_noise(A    ,						A    ,						torch.tensor(self.scheduler.timesteps[start_step:]   )   )

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

									if isinstance(self.scheduler    ,						A   ):
											lowercase_ : List[Any]							       =   self.scheduler.step(
											    model_output=A    ,						timestep=A    ,						sample=A    ,						eta=A    ,						generator=A    ,						)['''prev_sample''']
									else:
											lowercase_ : int							       =   self.scheduler.step(
											    model_output=A    ,						timestep=A    ,						sample=A    ,						generator=A    ,						)['''prev_sample''']

									if mask is not None:
											if mask_start > 0:
													lowercase_ : List[Any]							       =   mask[:, step, :, :mask_start]
											if mask_end > 0:
													lowercase_ : List[str]							       =   mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
									# 0.18215 was scaling factor used in training to ensure unit variance
									lowercase_ : Optional[Any]							       =   1 / self.vqvae.config.scaling_factor * images
									lowercase_ : str							       =   self.vqvae.decode(A   )['''sample''']

							lowercase_ : Tuple							       =   (images / 2 + 0.5).clamp(0    ,						1   )
							lowercase_ : Optional[Any]							       =   images.cpu().permute(0    ,						2    ,						3    ,						1   ).numpy()
							lowercase_ : List[str]							       =   (images * 2_55).round().astype('''uint8'''   )
							lowercase_ : Optional[int]							       =   list(
							    (Image.fromarray(_[:, :, 0]   ) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(A    ,						mode='''RGB'''   ).convert('''L'''   ) for _ in images)   )

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

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




					@torch.no_grad()
					def   A  (				self     :						List[str]    ,						A     :						List[Image.Image]    ,						A     :						int = 50   )  ->  np.ndarray:
							assert isinstance(self.scheduler    ,						A   )
							self.scheduler.set_timesteps(A   )
							lowercase_ : List[str]							       =   np.array(
							    [np.frombuffer(image.tobytes()    ,						dtype='''uint8'''   ).reshape((1, image.height, image.width)   ) for image in images]   )
							lowercase_ : Union[str, Any]							       =   (sample / 2_55) * 2 - 1
							lowercase_ : Union[str, Any]							       =   torch.Tensor(A   ).to(self.device   )

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)   )   ):
									lowercase_ : Union[str, Any]							       =   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									lowercase_ : Dict							       =   self.scheduler.alphas_cumprod[t]
									lowercase_ : int							       =   (
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									lowercase_ : str							       =   1 - alpha_prod_t
									lowercase_ : Dict							       =   self.unet(A    ,						A   )['''sample''']
									lowercase_ : List[Any]							       =   (1 - alpha_prod_t_prev) ** 0.5 * model_output
									lowercase_ : List[str]							       =   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									lowercase_ : int							       =   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample






					@staticmethod
					def   A  (				A     :						torch.Tensor    ,						A     :						torch.Tensor    ,						A     :						float   )  ->  torch.Tensor:
							lowercase_ : List[str]							       =   acos(torch.dot(torch.flatten(A   )    ,						torch.flatten(A   )   ) / torch.norm(A   ) / torch.norm(A   )   )
							return sin((1 - alpha) * theta   ) * xa / sin(A   ) + sin(alpha * theta   ) * xa / sin(A   )







'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):

		if density <= 0:
				raise ValueError("Impossible fluid density"      )
		if bulk_modulus <= 0:
				raise ValueError("Impossible bulk modulus"      )

		return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()



