

"""simple docstring"""






from __future__ import annotations

from random import random







class 	__UpperCamelCase    :

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

      _lowerCAmelCase       :   Optional[Any]  =    value
      _lowerCAmelCase       :   Any  =    random()
      _lowerCAmelCase       :   Optional[Any]  =    None
      _lowerCAmelCase       :   List[Any]  =    None

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

      from pprint import pformat

      if self.left is None and self.right is None:
          return F"""'{self.value}: {self.prior:.5}'"""
      else:
          return pformat(
              {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}    ,indent=1      )







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

      _lowerCAmelCase       :   Optional[int]  =    str(self.value      ) + ' '
      _lowerCAmelCase       :   str  =    str(self.left or ''      )
      _lowerCAmelCase       :   List[str]  =    str(self.right or ''      )
      return value + left + right





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if root is None:  # None tree is split into 2 Nones
        return None, None
    elif root.value is None:
        return None, None
    else:
        if value < root.value:
            _lowerCAmelCase,					_lowerCAmelCase       :   List[str]  =    split(root.left ,   _lowerCAmelCase   )
            return left, root
        else:
            _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    split(root.right ,   _lowerCAmelCase   )
            return root, right

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if (not left) or (not right):  # If one node is None, return the other
        return left or right
    elif left.prior < right.prior:
        _lowerCAmelCase       :   List[Any]  =    merge(left.right ,   _lowerCAmelCase   )
        return left
    else:
        _lowerCAmelCase       :   Any  =    merge(_lowerCAmelCase ,   right.left   )
        return right

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    Node(_lowerCAmelCase   )
    _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    split(_lowerCAmelCase ,   _lowerCAmelCase   )
    return merge(merge(_lowerCAmelCase ,   _lowerCAmelCase   ) ,   _lowerCAmelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    split(_lowerCAmelCase ,   value - 1   )
    _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    split(_lowerCAmelCase ,   _lowerCAmelCase   )
    return merge(_lowerCAmelCase ,   _lowerCAmelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if not root:  # None
        return
    else:
        inorder(root.left   )
        print(root.value ,   end=','   )
        inorder(root.right   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    for arg in args.split():
        if arg[0] == "+":
            _lowerCAmelCase       :   Optional[Any]  =    insert(_lowerCAmelCase ,   int(arg[1:]   )   )

        elif arg[0] == "-":
            _lowerCAmelCase       :   Dict  =    erase(_lowerCAmelCase ,   int(arg[1:]   )   )

        else:
            print('Unknown command'   )

    return root

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    None
    print(
        'enter numbers to create a tree, + value to add value into treap, '
        '- value to erase all nodes with value. \'q\' to quit. '   )

    _lowerCAmelCase       :   List[str]  =    input()
    while args != "q":
        _lowerCAmelCase       :   Optional[int]  =    interact_treap(_lowerCAmelCase ,   _lowerCAmelCase   )
        print(_lowerCAmelCase   )
        _lowerCAmelCase       :   List[Any]  =    input()

    print('good by!'   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()







"""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      )





