'''simple docstring'''

from functools import lru_cache
@lru_cache
def      __UpperCamelCase			(  UpperCAmelCase	):





 if num < 0:
  raise ValueError('''Number should not be negative.'''	)

 return 1 if num in (0, 1) else num * factorial(num - 1	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



"""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(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



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






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size) == int:
												_UpperCamelCase									=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
												_UpperCamelCase									=    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							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

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

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

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

												_UpperCamelCase									=    (
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												_UpperCamelCase									=    int(mask_start_secs * pixels_per_second)
												_UpperCamelCase									=    int(mask_end_secs * pixels_per_second)
												_UpperCamelCase									=    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):
																	_UpperCamelCase									=    self.unet(__a							,						__a							,						__a)['''sample''']
												else:
																	_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']

												if isinstance(self.scheduler							,						__a):
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						eta=__a							,						generator=__a							,						)['''prev_sample''']
												else:
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						generator=__a							,						)['''prev_sample''']

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

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

							_UpperCamelCase									=    (images / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    images.cpu().permute(0							,						2							,						3							,						1).numpy()
							_UpperCamelCase									=    (images * 2_55).round().astype('''uint8''')
							_UpperCamelCase									=    list(
							    (Image.fromarray(_[:, :, 0]) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__a							,						mode='''RGB''').convert('''L''') for _ in images))

							_UpperCamelCase									=    [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 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    np.array(
							    [np.frombuffer(image.tobytes()							,						dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
							_UpperCamelCase									=    (sample / 2_55) * 2 - 1
							_UpperCamelCase									=    torch.Tensor(__a).to(self.device)

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

							return sample




		@staticmethod
		def 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    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)






