





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   warnings.warn(
       'The preprocess method is deprecated and will be removed in a future version. Please'
       ' use VaeImageProcessor.preprocess instead',     lowerCAmelCase_,     )
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return image
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [image]

   if isinstance(image[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     image[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      __lowerCAmelCase							     =     [np.array(i.resize((w, h),     resample=PIL_INTERPOLATION['lanczos']     )     )[None, :] for i in image]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     np.array(lowerCAmelCase_     ).astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     image.transpose(0,     3,     1,     2     )
      __lowerCAmelCase							     =     2.0 * image - 1.0
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(image[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return image

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

   if isinstance(mask[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     mask[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      __lowerCAmelCase							     =     [np.array(m.convert('L'     ).resize((w, h),     resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
      __lowerCAmelCase							     =     np.concatenate(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     mask.astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     1
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(mask[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return mask




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     super().__init__()
     self.register_modules(unet=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)






  @torch.no_grad()
  def __call__(    self			:		Dict ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		Union[torch.Tensor, PIL.Image.Image] ,  lowerCAmelCase_			:		int = 2_5_0 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		int = 1_0 ,  lowerCAmelCase_			:		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,  lowerCAmelCase_			:		Optional[str] = "pil" ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[ImagePipelineOutput, Tuple]:
     __lowerCAmelCase							     =     image

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __lowerCAmelCase							     =     mask_image.to(device=self.device ,  dtype=self.unet.dtype					)

     __lowerCAmelCase							     =     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(lowerCAmelCase_					)}, but requested an effective batch"""
            f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

     __lowerCAmelCase							     =     original_image.shape
     __lowerCAmelCase							     =     randn_tensor(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

     __lowerCAmelCase							     =     self.scheduler.timesteps[0] + 1
     __lowerCAmelCase							     =     generator[0] if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
        if t < t_last:
           # predict the noise residual
           __lowerCAmelCase							     =     self.unet(lowerCAmelCase_ ,  lowerCAmelCase_					).sample
           # compute previous image: x_t -> x_t-1
           __lowerCAmelCase							     =     self.scheduler.step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					).prev_sample

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     t

     __lowerCAmelCase							     =     (image / 2 + 0.5).clamp(0 ,  1					)
     __lowerCAmelCase							     =     image.cpu().permute(0 ,  2 ,  3 ,  1					).numpy()
     if output_type == "pil":
        __lowerCAmelCase							     =     self.numpy_to_pil(lowerCAmelCase_					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)








import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case  :   List[str]						   =  logging.get_logger(__name__)

_snake_case  :   Dict						   =  {
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""encodec"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] ,  lowerCAmelCase_			:		Optional[Any]=2_4_0_0_0 ,  lowerCAmelCase_			:		int=1 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[Any]=1_2_8 ,  lowerCAmelCase_			:		List[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		Optional[int]=[8, 5, 4, 2] ,  lowerCAmelCase_			:		Optional[Any]="weight_norm" ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=7 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Dict="reflect" ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		Dict=1.0 ,  lowerCAmelCase_			:		str=1_0_2_4 ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		List[Any]=True ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Union[str, Any]:
     __lowerCAmelCase							     =     target_bandwidths
     __lowerCAmelCase							     =     sampling_rate
     __lowerCAmelCase							     =     audio_channels
     __lowerCAmelCase							     =     normalize
     __lowerCAmelCase							     =     chunk_length_s
     __lowerCAmelCase							     =     overlap
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_filters
     __lowerCAmelCase							     =     num_residual_layers
     __lowerCAmelCase							     =     upsampling_ratios
     __lowerCAmelCase							     =     norm_type
     __lowerCAmelCase							     =     kernel_size
     __lowerCAmelCase							     =     last_kernel_size
     __lowerCAmelCase							     =     residual_kernel_size
     __lowerCAmelCase							     =     dilation_growth_rate
     __lowerCAmelCase							     =     use_causal_conv
     __lowerCAmelCase							     =     pad_mode
     __lowerCAmelCase							     =     compress
     __lowerCAmelCase							     =     num_lstm_layers
     __lowerCAmelCase							     =     trim_right_ratio
     __lowerCAmelCase							     =     codebook_size
     __lowerCAmelCase							     =     codebook_dim if codebook_dim is not None else hidden_size
     __lowerCAmelCase							     =     use_conv_shortcut

     if self.norm_type not in ["weight_norm", "time_group_norm"]:
        raise ValueError(
            f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""					)

     super().__init__(**lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate					)






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     if self.chunk_length_s is None or self.overlap is None:
        return None
     else:
        return max(1 ,  int((1.0 - self.overlap) * self.chunk_length					)					)






  @property
  def     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     np.prod(self.upsampling_ratios					)
     return math.ceil(self.sampling_rate / hop_length					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)					)

