





'''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


__SCREAMING_SNAKE_CASE     :str       =				logging.get_logger(__name__)







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[Any]           =							"""AutoTokenizer"""
 _lowerCamelCase  : int           =							["""tokenizer"""]

 _lowerCamelCase  : Dict           =							{
     """semantic_prompt""": 1,
     """coarse_prompt""": 2,
     """fine_prompt""": 2,
 }




 def __init__(					self       :    List[str]	,			snake_case_       :    int	,			snake_case_       :    Optional[Any]=None    ):
      super().__init__(snake_case_    )

      _UpperCAmelCase     							=   speaker_embeddings




 @classmethod
 def      lowercase	(					cls       :    int	,			snake_case_       :    Optional[int]	,			snake_case_       :    List[Any]="speaker_embeddings_path.json"	,			**snake_case_       :    Dict    ):

      if speaker_embeddings_dict_path is not None:
           _UpperCAmelCase     							=   get_file_from_repo(
               snake_case_	,			snake_case_	,			subfolder=kwargs.pop("subfolder"	,			snake_case_    )	,			cache_dir=kwargs.pop("cache_dir"	,			snake_case_    )	,			force_download=kwargs.pop("force_download"	,			snake_case_    )	,			proxies=kwargs.pop("proxies"	,			snake_case_    )	,			resume_download=kwargs.pop("resume_download"	,			snake_case_    )	,			local_files_only=kwargs.pop("local_files_only"	,			snake_case_    )	,			use_auth_token=kwargs.pop("use_auth_token"	,			snake_case_    )	,			revision=kwargs.pop("revision"	,			snake_case_    )	,			)
           if speaker_embeddings_path is None:
                logger.warning(
                    f'`{os.path.join(snake_case_	,			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     							=   None
           else:
                with open(snake_case_    ) as speaker_embeddings_json:
                     _UpperCAmelCase     							=   json.load(snake_case_    )
      else:
           _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   AutoTokenizer.from_pretrained(snake_case_	,			**snake_case_    )

      return cls(tokenizer=snake_case_	,			speaker_embeddings=snake_case_    )




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[Any]	,			snake_case_       :    Dict="speaker_embeddings_path.json"	,			snake_case_       :    List[str]="speaker_embeddings"	,			snake_case_       :    bool = False	,			**snake_case_       :    Any	,			):
      if self.speaker_embeddings is not None:
           os.makedirs(os.path.join(snake_case_	,			snake_case_	,			"v2"    )	,			exist_ok=snake_case_    )

           _UpperCAmelCase     							=   {}

           _UpperCAmelCase     							=   save_directory

           for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                     _UpperCAmelCase     							=   self._load_voice_preset(snake_case_    )

                     _UpperCAmelCase     							=   {}
                     for key in self.speaker_embeddings[prompt_key]:
                          np.save(
                              os.path.join(
                                  embeddings_dict["repo_or_path"]	,			snake_case_	,			f'{prompt_key}_{key}'    )	,			voice_preset[key]	,			allow_pickle=snake_case_	,			)
                          _UpperCAmelCase     							=   os.path.join(snake_case_	,			f'{prompt_key}_{key}.npy'    )

                     _UpperCAmelCase     							=   tmp_dict

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

      super().save_pretrained(snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    str	,			snake_case_       :    str = None	,			**snake_case_       :    Tuple    ):
      _UpperCAmelCase     							=   self.speaker_embeddings[voice_preset]

      _UpperCAmelCase     							=   {}
      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     							=   get_file_from_repo(
               self.speaker_embeddings.get("repo_or_path"	,			"/"    )	,			voice_preset_paths[key]	,			subfolder=kwargs.pop("subfolder"	,			snake_case_    )	,			cache_dir=kwargs.pop("cache_dir"	,			snake_case_    )	,			force_download=kwargs.pop("force_download"	,			snake_case_    )	,			proxies=kwargs.pop("proxies"	,			snake_case_    )	,			resume_download=kwargs.pop("resume_download"	,			snake_case_    )	,			local_files_only=kwargs.pop("local_files_only"	,			snake_case_    )	,			use_auth_token=kwargs.pop("use_auth_token"	,			snake_case_    )	,			revision=kwargs.pop("revision"	,			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     							=   np.load(snake_case_    )

      return voice_preset_dict




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Optional[dict] = None    ):
      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       :    List[Any]	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any=None	,			snake_case_       :    Any="pt"	,			snake_case_       :    List[str]=2_5_6	,			snake_case_       :    str=False	,			snake_case_       :    Dict=True	,			snake_case_       :    str=False	,			**snake_case_       :    Optional[int]	,			):
      if voice_preset is not None and not isinstance(snake_case_	,			snake_case_    ):
           if (
               isinstance(snake_case_	,			snake_case_    )
               and self.speaker_embeddings is not None
               and voice_preset in self.speaker_embeddings
           ):
                _UpperCAmelCase     							=   self._load_voice_preset(snake_case_    )

           else:
                if isinstance(snake_case_	,			snake_case_    ) and not voice_preset.endswith(".npz"    ):
                     _UpperCAmelCase     							=   voice_preset + ".npz"

                _UpperCAmelCase     							=   np.load(snake_case_    )

      if voice_preset is not None:
           self._validate_voice_preset_dict(snake_case_	,			**snake_case_    )
           _UpperCAmelCase     							=   BatchFeature(data=snake_case_	,			tensor_type=snake_case_    )

      _UpperCAmelCase     							=   self.tokenizer(
          snake_case_	,			return_tensors=snake_case_	,			padding="max_length"	,			max_length=snake_case_	,			return_attention_mask=snake_case_	,			return_token_type_ids=snake_case_	,			add_special_tokens=snake_case_	,			**snake_case_	,			)

      if voice_preset is not None:
           _UpperCAmelCase     							=   voice_preset

      return encoded_text











'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




