


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 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       ['vqvae']

     def __init__(					self:Any     ,		_a:AutoencoderKL     ,		_a:UNetaDConditionModel     ,		_a:Mel     ,		_a:Union[DDIMScheduler, DDPMScheduler]     ,		):
       super().__init__()
       self.register_modules(unet=_a     ,		scheduler=_a     ,		mel=_a     ,		vqvae=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       return 50 if isinstance(self.scheduler     ,		_a   ) else 10_00

     @torch.no_grad()
     def __call__(					self:Tuple     ,		_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:Union[str, Any]=True     ,		):
       snake_case__						= steps or self.get_default_steps()
       self.scheduler.set_timesteps(_a   )
       snake_case__						= step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size   ) == int:
         snake_case__						= (self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
         snake_case__						= 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     ,		)
       snake_case__						= noise
       snake_case__						= None

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

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

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

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

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

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

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

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

       snake_case__						= [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 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:List[Image.Image]     ,		_a:int = 50   ):
       assert isinstance(self.scheduler     ,		_a   )
       self.scheduler.set_timesteps(_a   )
       snake_case__						= np.array(
           [np.frombuffer(image.tobytes()     ,		dtype='''uint8'''   ).reshape((1, image.height, image.width)   ) for image in images]   )
       snake_case__						= (sample / 2_55) * 2 - 1
       snake_case__						= torch.Tensor(_a   ).to(self.device   )

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

       return sample







     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:torch.Tensor     ,		_a:torch.Tensor     ,		_a:float   ):
       snake_case__						= 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   )





import numpy as np







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return 1 / (1 + np.exp(-vector				))







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				np.ndarray:
  return vector * sigmoid(__lowerCAmelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

