

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			:     int								=	['vqvae']


     def __init__(							self			:		Any			,  UpperCAmelCase			:		AutoencoderKL			,  UpperCAmelCase			:		UNetaDConditionModel			,  UpperCAmelCase			:		Mel			,  UpperCAmelCase			:		Union[DDIMScheduler, DDPMScheduler]			,  )							->	Dict:
      super().__init__()
      self.register_modules(unet=_snake_case			,  scheduler=_snake_case			,  mel=_snake_case			,  vqvae=_snake_case					)


     def   UpperCAmelCase  (							self			:		List[str]					)							->	int:
      return 5_0 if isinstance(self.scheduler			,  _snake_case					) else 1_0_0_0


     @torch.no_grad()
     def __call__(							self			:		Dict			,  UpperCAmelCase			:		int = 1			,  UpperCAmelCase			:		str = None			,  UpperCAmelCase			:		np.ndarray = None			,  UpperCAmelCase			:		int = 0			,  UpperCAmelCase			:		int = 0			,  UpperCAmelCase			:		int = None			,  UpperCAmelCase			:		torch.Generator = None			,  UpperCAmelCase			:		float = 0			,  UpperCAmelCase			:		float = 0			,  UpperCAmelCase			:		torch.Generator = None			,  UpperCAmelCase			:		float = 0			,  UpperCAmelCase			:		torch.Tensor = None			,  UpperCAmelCase			:		torch.Tensor = None			,  UpperCAmelCase			:		Optional[Any]=True			,  )							->	Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __lowerCAmelCase:   Any   	=      steps or self.get_default_steps()
      self.scheduler.set_timesteps(_snake_case					)
      __lowerCAmelCase:   Any   	=      step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size					) == int:
       __lowerCAmelCase:   Optional[Any]   	=      (self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __lowerCAmelCase:   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			,  )
      __lowerCAmelCase:   Optional[int]   	=      noise
      __lowerCAmelCase:   str   	=      None

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

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

       if start_step > 0:
        __lowerCAmelCase:   int   	=      self.scheduler.add_noise(_snake_case			,  _snake_case			,  self.scheduler.timesteps[start_step - 1]					)

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

       if isinstance(self.scheduler			,  _snake_case					):
        __lowerCAmelCase:   Any   	=      self.scheduler.step(
            model_output=_snake_case			,  timestep=_snake_case			,  sample=_snake_case			,  eta=_snake_case			,  generator=_snake_case			,  )['prev_sample']
       else:
        __lowerCAmelCase:   List[str]   	=      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:
         __lowerCAmelCase:   Optional[int]   	=      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:   List[Any]   	=      1 / self.vqvae.config.scaling_factor * images
       __lowerCAmelCase:   int   	=      self.vqvae.decode(_snake_case					)['sample']

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

      __lowerCAmelCase:   Any   	=      [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   UpperCAmelCase  (							self			:		List[Any]			,  UpperCAmelCase			:		List[Image.Image]			,  UpperCAmelCase			:		int = 5_0					)							->	np.ndarray:
      assert isinstance(self.scheduler			,  _snake_case					)
      self.scheduler.set_timesteps(_snake_case					)
      __lowerCAmelCase:   Optional[Any]   	=      np.array(
          [np.frombuffer(image.tobytes()			,  dtype='uint8'					).reshape((1, image.height, image.width)					) for image in images]					)
      __lowerCAmelCase:   Tuple   	=      (sample / 2_5_5) * 2 - 1
      __lowerCAmelCase:   Tuple   	=      torch.Tensor(_snake_case					).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[Any]   	=      self.scheduler.alphas_cumprod[t]
       __lowerCAmelCase:   Dict   	=      (
           self.scheduler.alphas_cumprod[prev_timestep]
           if prev_timestep >= 0
           else self.scheduler.final_alpha_cumprod
       )
       __lowerCAmelCase:   int   	=      1 - alpha_prod_t
       __lowerCAmelCase:   Any   	=      self.unet(_snake_case			,  _snake_case					)['sample']
       __lowerCAmelCase:   List[Any]   	=      (1 - alpha_prod_t_prev) ** 0.5 * model_output
       __lowerCAmelCase:   Dict   	=      (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
       __lowerCAmelCase:   List[str]   	=      sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample


     @staticmethod
     def   UpperCAmelCase  (							UpperCAmelCase			:		torch.Tensor			,  UpperCAmelCase			:		torch.Tensor			,  UpperCAmelCase			:		float					)							->	torch.Tensor:
      __lowerCAmelCase:   str   	=      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					)










from collections import deque

from .hash_table import HashTable




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], *_snake_case  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            super().__init__(*_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  Dict					)   ->Dict:
            snake_case__							:	int   	=	deque([]					) if self.values[key] is None else self.values[key]
            self.values[key].appendleft(_snake_case					)
            snake_case__							:	Dict   	=	self.values[key]






     def    lowercase_   (       self  :  Any					)   ->Optional[Any]:
            return (
                sum(self.charge_factor - len(_snake_case					) for slot in self.values					)
                / self.size_table
                * self.charge_factor
            )






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str, _snake_case  :  Optional[int]=None					)   ->Optional[Any]:
            if not (
                len(self.values[key]					) == self.charge_factor and self.values.count(_snake_case					) == 0
            ):
                   return key
            return super()._collision_resolution(_snake_case, _snake_case					)






