

"""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    (    a__						):
  _UpperCAmelCase          =		["vqvae"]

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  @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    ,):
      '''simple docstring'''

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

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

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

          if start_step > 0:
              _lowerCAmelCase       :   List[Any]  =    self.scheduler.add_noise(_A    ,_A    ,self.scheduler.timesteps[start_step - 1]      )

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

          if isinstance(self.scheduler    ,_A      ):
              _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler.step(
                  model_output=_A    ,timestep=_A    ,sample=_A    ,eta=_A    ,generator=_A    ,)['prev_sample']
          else:
              _lowerCAmelCase       :   Any  =    self.scheduler.step(
                  model_output=_A    ,timestep=_A    ,sample=_A    ,generator=_A    ,)['prev_sample']

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

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

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

      _lowerCAmelCase       :   Dict  =    [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 __lowerCamelCase		(		self    ,_A    ,_A = 50      ):
      '''simple docstring'''

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

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

      return sample







  @staticmethod
  def __lowerCamelCase		(		_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    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      )








"""simple docstring"""






from collections.abc import Callable







class 	__UpperCamelCase    :

  def __init__(		self    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   list  =    []
      # Stores indexes of each item for supporting updates and deletion.
      _lowerCAmelCase       :   dict  =    {}
      # Stores current size of heap.
      _lowerCAmelCase       :   Union[str, Any]  =    0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      _lowerCAmelCase       :   Union[str, Any]  =    key or (lambda _A      : x)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return int((i - 1) / 2      ) if i > 0 else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    int(2 * i + 1      )
      return left if 0 < left < self.size else None

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    int(2 * i + 2      )
      return right if 0 < right < self.size else None

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.arr[j], self.arr[i]

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return self.arr[i][1] < self.arr[j][1]

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self._left(_A      )
      _lowerCAmelCase       :   str  =    self._right(_A      )
      _lowerCAmelCase       :   Tuple  =    i

      if left is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   int  =    left
      if right is not None and not self._cmp(_A    ,_A      ):
          _lowerCAmelCase       :   Optional[int]  =    right

      return valid_parent

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self._parent(_A      )
      while parent is not None and not self._cmp(_A    ,_A      ):
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    parent, self._parent(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self._get_valid_parent(_A      )
      while valid_parent != index:
          self._swap(_A    ,_A      )
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    valid_parent, self._get_valid_parent(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   int  =    self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    [item, self.key(_A      )]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(_A      )
      self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if item not in self.pos_map:
          return
      _lowerCAmelCase       :   List[str]  =    self.pos_map[item]
      del self.pos_map[item]
      _lowerCAmelCase       :   Dict  =    self.arr[self.size - 1]
      _lowerCAmelCase       :   Optional[Any]  =    index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
          self._heapify_up(_A      )
          self._heapify_down(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    len(self.arr      )
      if arr_len == self.size:
          self.arr.append([item, self.key(_A      )]      )
      else:
          _lowerCAmelCase       :   Any  =    [item, self.key(_A      )]
      _lowerCAmelCase       :   str  =    self.size
      self.size += 1
      self._heapify_up(self.size - 1      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.arr[0] if self.size else None







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.get_top()
      if top_item_tuple:
          self.delete_item(top_item_tuple[0]      )
      return top_item_tuple





def 		lowerCamelCase__		(					):

    '''simple docstring'''





if __name__ == "__main__":
 import doctest

 doctest.testmod()





