"""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 				a (						lowerCAmelCase_      ):
    _snake_case   :     Tuple   							=							['vqvae']


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


    def    lowerCAmelCase_     (    self		:     Dict    ):
      return 50 if isinstance(self.scheduler      , __lowerCAmelCase    ) else 1000


    @torch.no_grad()
    def __call__(    self		:     str      , __lowerCAmelCase		:     int = 1      , __lowerCAmelCase		:     str = None      , __lowerCAmelCase		:     np.ndarray = None      , __lowerCAmelCase		:     int = 0      , __lowerCAmelCase		:     int = 0      , __lowerCAmelCase		:     int = None      , __lowerCAmelCase		:     torch.Generator = None      , __lowerCAmelCase		:     float = 0      , __lowerCAmelCase		:     float = 0      , __lowerCAmelCase		:     torch.Generator = None      , __lowerCAmelCase		:     float = 0      , __lowerCAmelCase		:     torch.Tensor = None      , __lowerCAmelCase		:     torch.Tensor = None      , __lowerCAmelCase		:     Dict=True      , ):
      _UpperCAmelCase         =					steps or self.get_default_steps()
      self.scheduler.set_timesteps(__lowerCAmelCase    )
      _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=__lowerCAmelCase      , device=self.device      , )
      _UpperCAmelCase         =					noise
      _UpperCAmelCase         =					None

      if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(__lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					self.mel.audio_slice_to_image(__lowerCAmelCase    )
        _UpperCAmelCase         =					np.frombuffer(input_image.tobytes()      , dtype="""uint8"""    ).reshape(
            (input_image.height, input_image.width)    )
        _UpperCAmelCase         =					(input_image / 255) * 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(__lowerCAmelCase      , 0    )    ).latent_dist.sample(
              generator=__lowerCAmelCase    )[0]
          _UpperCAmelCase         =					self.vqvae.config.scaling_factor * input_images

        if start_step > 0:
          _UpperCAmelCase         =					self.scheduler.add_noise(__lowerCAmelCase      , __lowerCAmelCase      , 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(__lowerCAmelCase      , __lowerCAmelCase      , torch.tensor(self.scheduler.timesteps[start_step:]    )    )

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
        if isinstance(self.unet      , __lowerCAmelCase    ):
          _UpperCAmelCase         =					self.unet(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )["""sample"""]
        else:
          _UpperCAmelCase         =					self.unet(__lowerCAmelCase      , __lowerCAmelCase    )["""sample"""]

        if isinstance(self.scheduler      , __lowerCAmelCase    ):
          _UpperCAmelCase         =					self.scheduler.step(
              model_output=__lowerCAmelCase      , timestep=__lowerCAmelCase      , sample=__lowerCAmelCase      , eta=__lowerCAmelCase      , generator=__lowerCAmelCase      , )["""prev_sample"""]
        else:
          _UpperCAmelCase         =					self.scheduler.step(
              model_output=__lowerCAmelCase      , timestep=__lowerCAmelCase      , sample=__lowerCAmelCase      , generator=__lowerCAmelCase      , )["""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(__lowerCAmelCase    )["""sample"""]

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

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

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


    @torch.no_grad()
    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     List[Image.Image]      , __lowerCAmelCase		:     int = 50    ):
      assert isinstance(self.scheduler      , __lowerCAmelCase    )
      self.scheduler.set_timesteps(__lowerCAmelCase    )
      _UpperCAmelCase         =					np.array(
          [np.frombuffer(image.tobytes()      , dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
      _UpperCAmelCase         =					(sample / 255) * 2 - 1
      _UpperCAmelCase         =					torch.Tensor(__lowerCAmelCase    ).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(__lowerCAmelCase      , __lowerCAmelCase    )["""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    lowerCAmelCase_     (    __lowerCAmelCase		:     torch.Tensor      , __lowerCAmelCase		:     torch.Tensor      , __lowerCAmelCase		:     float    ):
      _UpperCAmelCase         =					acos(torch.dot(torch.flatten(__lowerCAmelCase    )      , torch.flatten(__lowerCAmelCase    )    ) / torch.norm(__lowerCAmelCase    ) / torch.norm(__lowerCAmelCase    )    )
      return sin((1 - alpha) * theta    ) * xa / sin(__lowerCAmelCase    ) + sin(alpha * theta    ) * xa / sin(__lowerCAmelCase    )

"""simple docstring"""


from math import pow






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase      ,):







  """simple docstring"""
  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  _UpperCAmelCase         =					int(pow(lowercase      ,lowercase						)						)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    _UpperCAmelCase       , _UpperCAmelCase         =					backtrack(
        lowercase      ,lowercase      ,current_number + 1      ,lowercase      ,lowercase						)
  return current_sum, solutions_count






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
    raise ValueError(
        """Invalid input\n"""
        """needed_sum must be between 1 and 1000, power between 2 and 10."""						)

  return backtrack(lowercase      ,lowercase      ,1      ,0      ,0						)[1]  # Return the solutions_count


if __name__ == "__main__":
    import doctest

    doctest.testmod()
