






"""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 						snake_case  (  a__	):
    __magic_name__ :    Dict   					=					['vqvae']





    def __init__(							self     : Optional[int]    ,		A     : List[str]    ,		A     : Optional[Any]    ,		A     : List[Any]    ,		A     : str    ,		):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      return 5_0 if isinstance(self.scheduler    ,		_lowerCamelCase      ) else 1_0_0_0





    @torch.no_grad()
    def __call__(							self     : Optional[int]    ,		A     : Union[str, Any] = 1    ,		A     : Union[str, Any] = None    ,		A     : Optional[Any] = None    ,		A     : List[str] = 0    ,		A     : Optional[int] = 0    ,		A     : List[str] = None    ,		A     : Union[str, Any] = None    ,		A     : int = 0    ,		A     : Dict = 0    ,		A     : Optional[int] = None    ,		A     : Tuple = 0    ,		A     : Optional[int] = None    ,		A     : Dict = None    ,		A     : Dict=True    ,		):


      '''simple docstring'''




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

      if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(_lowerCamelCase    ,		_lowerCamelCase      )
        a					:      Optional[int]    =       self.mel.audio_slice_to_image(_lowerCamelCase      )
        a					:      Dict    =       np.frombuffer(input_image.tobytes()    ,		dtype='uint8'      ).reshape(
            (input_image.height, input_image.width)      )
        a					:      Dict    =       (input_image / 2_5_5) * 2 - 1
        a					:      str    =       torch.tensor(input_image[np.newaxis, :, :]    ,		dtype=torch.float      ).to(self.device      )

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

        if start_step > 0:
          a					:      Dict    =       self.scheduler.add_noise(_lowerCamelCase    ,		_lowerCamelCase    ,		self.scheduler.timesteps[start_step - 1]      )

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

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

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

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

      a					:      Union[str, Any]    =       (images / 2 + 0.5).clamp(0    ,		1      )
      a					:      int    =       images.cpu().permute(0    ,		2    ,		3    ,		1      ).numpy()
      a					:      str    =       (images * 2_5_5).round().astype('uint8'      )
      a					:      Union[str, Any]    =       list(
          (Image.fromarray(_[:, :, 0]      ) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(_lowerCamelCase    ,		mode='RGB'      ).convert('L'      ) for _ in images)      )

      a					:      Optional[int]    =       [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     : List[Any]    ,		A     : Optional[Any]    ,		A     : int = 5_0      ):


      '''simple docstring'''




      assert isinstance(self.scheduler    ,		_lowerCamelCase      )
      self.scheduler.set_timesteps(_lowerCamelCase      )
      a					:      int    =       np.array(
          [np.frombuffer(image.tobytes()    ,		dtype='uint8'      ).reshape((1, image.height, image.width)      ) for image in images]      )
      a					:      Union[str, Any]    =       (sample / 2_5_5) * 2 - 1
      a					:      Any    =       torch.Tensor(_lowerCamelCase      ).to(self.device      )

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

      return sample





    @staticmethod
    def     lowerCamelCase__				(							A     : str    ,		A     : int    ,		A     : Optional[int]      ):


      '''simple docstring'''




      a					:      str    =       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"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




