



"""simple docstring"""
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


A:     int										=	logging.get_logger(__name__)  # pylint: disable=invalid-name

def    _snake_case		(		UpperCamelCase		:	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"""    ,	UpperCamelCase    ,	)
 if isinstance(UpperCamelCase    ,	torch.Tensor		):
  return image
 elif isinstance(UpperCamelCase    ,	PIL.Image.Image		):
  UpperCAmelCase			:      Optional[int]			=			[image]

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

  UpperCAmelCase			:      Any			=			[np.array(i.resize((w, h)    ,	resample=PIL_INTERPOLATION["""lanczos"""]		)		)[None, :] for i in image]
  UpperCAmelCase			:      Tuple			=			np.concatenate(UpperCamelCase    ,	axis=0		)
  UpperCAmelCase			:      Dict			=			np.array(UpperCamelCase		).astype(np.floataa		) / 255.0
  UpperCAmelCase			:      List[Any]			=			image.transpose(0    ,	3    ,	1    ,	2		)
  UpperCAmelCase			:      Dict			=			2.0 * image - 1.0
  UpperCAmelCase			:      str			=			torch.from_numpy(UpperCamelCase		)
 elif isinstance(image[0]    ,	torch.Tensor		):
  UpperCAmelCase			:      Optional[int]			=			torch.cat(UpperCamelCase    ,	dim=0		)
 return image

def    _snake_case		(		UpperCamelCase		:	Union[List, PIL.Image.Image, torch.Tensor]		):
 if isinstance(UpperCamelCase    ,	torch.Tensor		):
  return mask
 elif isinstance(UpperCamelCase    ,	PIL.Image.Image		):
  UpperCAmelCase			:      List[str]			=			[mask]

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



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     UNetaDModel
   __lowerCAmelCase						:     RePaintScheduler

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    super().__init__()
    self.register_modules(unet=_SCREAMING_SNAKE_CASE   ,  scheduler=_SCREAMING_SNAKE_CASE		)




   @torch.no_grad()
   def __call__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 250   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = 10   ,  _SCREAMING_SNAKE_CASE = 10   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  )						->   Union[ImagePipelineOutput, Tuple]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			image

    UpperCAmelCase			:      Optional[Any]			=			_preprocess_image(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			original_image.to(device=self.device   ,  dtype=self.unet.dtype		)
    UpperCAmelCase			:      Dict			=			_preprocess_mask(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			mask_image.to(device=self.device   ,  dtype=self.unet.dtype		)

    UpperCAmelCase			:      int			=			original_image.shape[0]

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

    UpperCAmelCase			:      Any			=			original_image.shape
    UpperCAmelCase			:      Optional[Any]			=			randn_tensor(_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  device=self.device   ,  dtype=self.unet.dtype		)

    # set step values
    self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  self.device		)
    UpperCAmelCase			:      Union[str, Any]			=			eta

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

     else:
      # compute the reverse: x_t-1 -> x_t
      UpperCAmelCase			:      Union[str, Any]			=			self.scheduler.undo_step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Any			=			t

    UpperCAmelCase			:      Dict			=			(image / 2 + 0.5).clamp(0   ,  1		)
    UpperCAmelCase			:      Tuple			=			image.cpu().permute(0   ,  2   ,  3   ,  1		).numpy()
    if output_type == "pil":
     UpperCAmelCase			:      Union[str, Any]			=			self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)





"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			get_file_from_repo(
         _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if speaker_embeddings_path is None:
      logger.warning(
          F"`{os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  """v2"""		)   ,  exist_ok=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]   ,  _SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}"		)   ,  voice_preset[key]   ,  allow_pickle=_SCREAMING_SNAKE_CASE   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			tmp_dict

     with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  """w"""		) as fp:
      json.dump(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    super().save_pretrained(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.speaker_embeddings[voice_preset]

    UpperCAmelCase			:      List[Any]			=			{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

     UpperCAmelCase			:      List[str]			=			get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""   ,  """/"""		)   ,  voice_preset_paths[key]   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if path is None:
      raise ValueError(
          F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'   ,  '/'		)   ,  voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''simple docstring'''




    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

     if not isinstance(voice_preset[key]   ,  np.ndarray		):
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

     if len(voice_preset[key].shape		) != self.preset_shape[key]:
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if (
         isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

     else:
      if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) and not voice_preset.endswith(""".npz"""		):
       UpperCAmelCase			:      Tuple			=			voice_preset + """.npz"""

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			self.tokenizer(
        _SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_attention_mask=_SCREAMING_SNAKE_CASE   ,  return_token_type_ids=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    if voice_preset is not None:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
