






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__	):
 _lowerCAmelCase	:	int				=			['''vqvae''']







 def __init__(		self :       Any							,       SCREAMING_SNAKE_CASE :       AutoencoderKL							,       SCREAMING_SNAKE_CASE :       UNetaDConditionModel							,       SCREAMING_SNAKE_CASE :       Mel							,       SCREAMING_SNAKE_CASE :       Union[DDIMScheduler, DDPMScheduler]							,       ):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      return 50 if isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						) else 10_00







 @torch.no_grad()
 def __call__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       int = 1							,       SCREAMING_SNAKE_CASE :       str = None							,       SCREAMING_SNAKE_CASE :       np.ndarray = None							,       SCREAMING_SNAKE_CASE :       int = 0							,       SCREAMING_SNAKE_CASE :       int = 0							,       SCREAMING_SNAKE_CASE :       int = None							,       SCREAMING_SNAKE_CASE :       torch.Generator = None							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       torch.Generator = None							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       torch.Tensor = None							,       SCREAMING_SNAKE_CASE :       torch.Tensor = None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  steps or self.get_default_steps()
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size						) == int:
           UpperCamelCase__      :       List[str]	    =  (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
           UpperCamelCase__      :       Optional[Any]	    =  randn_tensor(
               (
                   batch_size,
                   self.unet.config.in_channels,
                   self.unet.config.sample_size[0],
                   self.unet.config.sample_size[1],
               )							,       generator=SCREAMING_SNAKE_CASE							,       device=self.device							,       )
      UpperCamelCase__      :       Dict	    =  noise
      UpperCamelCase__      :       Any	    =  None

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

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

           if start_step > 0:
                UpperCamelCase__      :       Optional[int]	    =  self.scheduler.add_noise(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       self.scheduler.timesteps[start_step - 1]						)

           UpperCamelCase__      :       List[Any]	    =  (
               self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
           )
           UpperCamelCase__      :       Optional[int]	    =  int(mask_start_secs * pixels_per_second						)
           UpperCamelCase__      :       List[Any]	    =  int(mask_end_secs * pixels_per_second						)
           UpperCamelCase__      :       int	    =  self.scheduler.add_noise(SCREAMING_SNAKE_CASE							,       SCREAMING_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							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       str	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)["sample"]
           else:
                UpperCamelCase__      :       Optional[int]	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)["sample"]

           if isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						):
                UpperCamelCase__      :       Optional[Any]	    =  self.scheduler.step(
                    model_output=SCREAMING_SNAKE_CASE							,       timestep=SCREAMING_SNAKE_CASE							,       sample=SCREAMING_SNAKE_CASE							,       eta=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       )["prev_sample"]
           else:
                UpperCamelCase__      :       List[Any]	    =  self.scheduler.step(
                    model_output=SCREAMING_SNAKE_CASE							,       timestep=SCREAMING_SNAKE_CASE							,       sample=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       )["prev_sample"]

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

      if self.vqvae is not None:
           # 0.18215 was scaling factor used in training to ensure unit variance
           UpperCamelCase__      :       int	    =  1 / self.vqvae.config.scaling_factor * images
           UpperCamelCase__      :       Union[str, Any]	    =  self.vqvae.decode(SCREAMING_SNAKE_CASE						)["sample"]

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

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

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







 @torch.no_grad()
 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       List[Image.Image]							,       SCREAMING_SNAKE_CASE :       int = 50						):


      '''simple docstring'''






      assert isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						)
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[str]	    =  np.array(
          [np.frombuffer(image.tobytes()							,       dtype="uint8"						).reshape((1, image.height, image.width)						) for image in images]						)
      UpperCamelCase__      :       Optional[Any]	    =  (sample / 2_55) * 2 - 1
      UpperCamelCase__      :       List[Any]	    =  torch.Tensor(SCREAMING_SNAKE_CASE						).to(self.device						)

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,       (0,)						)						):
           UpperCamelCase__      :       Tuple	    =  t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
           UpperCamelCase__      :       Any	    =  self.scheduler.alphas_cumprod[t]
           UpperCamelCase__      :       str	    =  (
               self.scheduler.alphas_cumprod[prev_timestep]
               if prev_timestep >= 0
               else self.scheduler.final_alpha_cumprod
           )
           UpperCamelCase__      :       Tuple	    =  1 - alpha_prod_t
           UpperCamelCase__      :       Tuple	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)["sample"]
           UpperCamelCase__      :       List[Any]	    =  (1 - alpha_prod_t_prev) ** 0.5 * model_output
           UpperCamelCase__      :       int	    =  (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
           UpperCamelCase__      :       Optional[Any]	    =  sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample







 @staticmethod
 def  __lowercase (		SCREAMING_SNAKE_CASE :       torch.Tensor							,       SCREAMING_SNAKE_CASE :       torch.Tensor							,       SCREAMING_SNAKE_CASE :       float						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE						)							,       torch.flatten(SCREAMING_SNAKE_CASE						)						) / torch.norm(SCREAMING_SNAKE_CASE						) / torch.norm(SCREAMING_SNAKE_CASE						)						)
      return sin((1 - alpha) * theta						) * xa / sin(SCREAMING_SNAKE_CASE						) + sin(alpha * theta						) * xa / sin(SCREAMING_SNAKE_CASE						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       int	    =  1
     for i in range(1 ,   num + 1      ):
          fact *= i
     return fact







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  0
     while number > 0:
          UpperCamelCase__      :       List[Any]	    =  number % 10
          sum_of_digits += last_digit
          UpperCamelCase__      :       int	    =  number // 10  # Removing the last_digit from the given number
     return sum_of_digits







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase = 100      )			->     int:
     UpperCamelCase__      :       Optional[Any]	    =  factorial(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[int]	    =  split_and_add(__lowerCAmelCase      )
     return result


if __name__ == "__main__":
 print(solution(int(input('''Enter the Number: ''').strip())))