

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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Dict														=     ["""vqvae"""]





    def __init__(      self						,   a						,   a						,   a						,   a						,   ):
         super().__init__()
         self.register_modules(unet=a						,   scheduler=a						,   mel=a						,   vqvae=a)





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

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

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

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

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

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

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

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

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

         lowercase__    :   List[Any]      =       [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       snake_case_				(      self						,   a						,   a = 50):
         assert isinstance(self.scheduler						,   a)
         self.scheduler.set_timesteps(a)
         lowercase__    :   List[Any]      =       np.array(
             [np.frombuffer(image.tobytes()						,   dtype='uint8').reshape((1, image.height, image.width)) for image in images])
         lowercase__    :   Any      =       (sample / 255) * 2 - 1
         lowercase__    :   Dict      =       torch.Tensor(a).to(self.device)

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

         return sample





    @staticmethod
    def       snake_case_				(      a						,   a						,   a):
         lowercase__    :   Union[str, Any]      =       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)




import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DPTImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

