





'''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 			SCREAMING_SNAKE_CASE   (_lowerCamelCase							):
     lowerCAmelCase		      =	['''vqvae''']


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     ):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     @torch.no_grad()
     def __call__(						self						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 0						,     _UpperCAmelCase = 0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 0						,     _UpperCAmelCase = 0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 0						,     _UpperCAmelCase = None						,     _UpperCAmelCase = None						,     _UpperCAmelCase=True						,     ):

          '''simple docstring'''






          __A						:			List[Any]								=	steps or self.get_default_steps()
          self.scheduler.set_timesteps(_UpperCAmelCase)
          __A						:			List[Any]								=	step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size) == int:
               __A						:			List[str]								=	(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
               __A						:			Optional[int]								=	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						,     )
          __A						:			str								=	noise
          __A						:			Optional[int]								=	None

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

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

               if start_step > 0:
                    __A						:			Union[str, Any]								=	self.scheduler.add_noise(_UpperCAmelCase						,     _UpperCAmelCase						,     self.scheduler.timesteps[start_step - 1])

               __A						:			Optional[Any]								=	(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               __A						:			List[str]								=	int(mask_start_secs * pixels_per_second)
               __A						:			Any								=	int(mask_end_secs * pixels_per_second)
               __A						:			int								=	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):
                    __A						:			Dict								=	self.unet(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)['sample']
               else:
                    __A						:			Dict								=	self.unet(_UpperCAmelCase						,     _UpperCAmelCase)['sample']

               if isinstance(self.scheduler						,     _UpperCAmelCase):
                    __A						:			List[str]								=	self.scheduler.step(
                        model_output=_UpperCAmelCase						,     timestep=_UpperCAmelCase						,     sample=_UpperCAmelCase						,     eta=_UpperCAmelCase						,     generator=_UpperCAmelCase						,     )['prev_sample']
               else:
                    __A						:			Dict								=	self.scheduler.step(
                        model_output=_UpperCAmelCase						,     timestep=_UpperCAmelCase						,     sample=_UpperCAmelCase						,     generator=_UpperCAmelCase						,     )['prev_sample']

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

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

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

          __A						:			Union[str, Any]								=	[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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase = 50):

          '''simple docstring'''






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

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

          return sample


     @staticmethod
     def 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[Any]								=	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)






'''simple docstring'''




import argparse


lowercase__   :				Any      =   '''docs/source/_static/js/custom.js'''






def 	_lowerCAmelCase      (  __snake_case  :			Union[str, Any]      )					->					str:
     with open(__snake_case			,							encoding='utf-8'			,							newline='\n'      ) as f:
          __A						:			Optional[Any]								=	f.readlines()
     __A						:			List[str]								=	0

     # First let's put the right version
     while not lines[index].startswith('const stableVersion ='      ):
          index += 1
     __A						:			Tuple								=	f'const stableVersion = "v{version}"\n'

     # Then update the dictionary
     while not lines[index].startswith('const versionMapping = {'      ):
          index += 1

     # We go until the end
     while not lines[index].startswith('}'      ):
          index += 1
     # We add the new version at the end
     lines[index - 1] += f'    "v{version}": "v{version}",\n'

     with open(__snake_case			,							'w'			,							encoding='utf-8'			,							newline='\n'      ) as f:
          f.writelines(__snake_case      )


if __name__ == "__main__":
  lowercase__   :				Optional[Any]      =   argparse.ArgumentParser()
  parser.add_argument('''--version''', help='''Release version.''')
  lowercase__   :				List[str]      =   parser.parse_args()
  update_custom_js(args.version)