



'''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__			):
    lowercase__				=					["""vqvae"""]







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







    def     lowercase_ (   self				:						Union[str, Any]			):
      return 50 if isinstance(self.scheduler		,    __snake_case			) else 10_00







    @torch.no_grad()
    def __call__(   self				:						List[str]		,    __snake_case				:						int = 1		,    __snake_case				:						str = None		,    __snake_case				:						np.ndarray = None		,    __snake_case				:						int = 0		,    __snake_case				:						int = 0		,    __snake_case				:						int = None		,    __snake_case				:						torch.Generator = None		,    __snake_case				:						float = 0		,    __snake_case				:						float = 0		,    __snake_case				:						torch.Generator = None		,    __snake_case				:						float = 0		,    __snake_case				:						torch.Tensor = None		,    __snake_case				:						torch.Tensor = None		,    __snake_case				:						int=True		,    ):
      a		:      str       =  steps or self.get_default_steps()
      self.scheduler.set_timesteps(__snake_case			)
      a		:      Tuple       =  step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size			) == int:
        a		:      Union[str, Any]       =  (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
        a		:      str       =  randn_tensor(
            (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size[0],
                self.unet.config.sample_size[1],
            )		,    generator=__snake_case		,    device=self.device		,    )
      a		:      List[Any]       =  noise
      a		:      str       =  None

      if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(__snake_case		,    __snake_case			)
        a		:      Union[str, Any]       =  self.mel.audio_slice_to_image(__snake_case			)
        a		:      Union[str, Any]       =  np.frombuffer(input_image.tobytes()		,    dtype='uint8'			).reshape(
            (input_image.height, input_image.width)			)
        a		:      Optional[int]       =  (input_image / 2_55) * 2 - 1
        a		:      int       =  torch.tensor(input_image[np.newaxis, :, :]		,    dtype=torch.float			).to(self.device			)

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

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

        a		:      Any       =  (
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        a		:      Dict       =  int(mask_start_secs * pixels_per_second			)
        a		:      str       =  int(mask_end_secs * pixels_per_second			)
        a		:      Union[str, Any]       =  self.scheduler.add_noise(__snake_case		,    __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		,    __snake_case			):
          a		:      int       =  self.unet(__snake_case		,    __snake_case		,    __snake_case			)['sample']
        else:
          a		:      Any       =  self.unet(__snake_case		,    __snake_case			)['sample']

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

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

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

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

      a		:      Optional[int]       =  [self.mel.image_to_audio(__snake_case			) for _ in images]
      if not return_dict:
        return images, (self.mel.get_sample_rate(), audios)

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







    @torch.no_grad()
    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						List[Image.Image]		,    __snake_case				:						int = 50			):
      assert isinstance(self.scheduler		,    __snake_case			)
      self.scheduler.set_timesteps(__snake_case			)
      a		:      Dict       =  np.array(
          [np.frombuffer(image.tobytes()		,    dtype='uint8'			).reshape((1, image.height, image.width)			) for image in images]			)
      a		:      Tuple       =  (sample / 2_55) * 2 - 1
      a		:      Dict       =  torch.Tensor(__snake_case			).to(self.device			)

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

      return sample







    @staticmethod
    def     lowercase_ (   __snake_case				:						torch.Tensor		,    __snake_case				:						torch.Tensor		,    __snake_case				:						float			):
      a		:      Optional[Any]       =  acos(torch.dot(torch.flatten(__snake_case			)		,    torch.flatten(__snake_case			)			) / torch.norm(__snake_case			) / torch.norm(__snake_case			)			)
      return sin((1 - alpha) * theta			) * xa / sin(__snake_case			) + sin(alpha * theta			) * xa / sin(__snake_case			)




'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    *__snake_case				:						List[str]		,    **__snake_case				:						Union[str, Any]			):
      warnings.warn(
          'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
          ' Please use ChineseCLIPImageProcessor instead.'		,    __snake_case		,    )
      super().__init__(*__snake_case		,    **__snake_case			)