





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


__lowerCamelCase	:					Union[str, Any]			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Optional[Any]						   =       'AutoTokenizer'
      _UpperCAmelCase     :Union[str, Any]						   =       ['tokenizer']

      _UpperCAmelCase     :List[str]						   =       {
          'semantic_prompt': 1,
          'coarse_prompt': 2,
          'fine_prompt': 2,
      }





      def __init__(			self	,							A_	,							A_=None       ):
       '''simple docstring'''




       super().__init__(A_       )

       UpperCamelCase     : Optional[Any]	     =       speaker_embeddings





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_="speaker_embeddings_path.json"	,							**A_       ):
       '''simple docstring'''




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

       UpperCamelCase     : int	     =       AutoTokenizer.from_pretrained(A_	,							**A_       )

       return cls(tokenizer=A_	,							speaker_embeddings=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_="speaker_embeddings_path.json"	,							A_="speaker_embeddings"	,							A_ = False	,							**A_	,							):
       '''simple docstring'''




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

        UpperCamelCase     : List[Any]	     =       {}

        UpperCamelCase     : Union[str, Any]	     =       save_directory

        for prompt_key in self.speaker_embeddings:
         if prompt_key != "repo_or_path":
          UpperCamelCase     : Union[str, Any]	     =       self._load_voice_preset(A_       )

          UpperCamelCase     : Union[str, Any]	     =       {}
          for key in self.speaker_embeddings[prompt_key]:
           np.save(
               os.path.join(
                   embeddings_dict["repo_or_path"]	,							A_	,							F"""{prompt_key}_{key}"""       )	,							voice_preset[key]	,							allow_pickle=A_	,							)
           UpperCamelCase     : Any	     =       os.path.join(A_	,							F"""{prompt_key}_{key}.npy"""       )

          UpperCamelCase     : int	     =       tmp_dict

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

       super().save_pretrained(A_	,							A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_ = None	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       self.speaker_embeddings[voice_preset]

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

        UpperCamelCase     : str	     =       np.load(A_       )

       return voice_preset_dict





      def 	__UpperCamelCase(			self	,							A_ = None       ):
       '''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	,							A_=None	,							A_=None	,							A_="pt"	,							A_=256	,							A_=False	,							A_=True	,							A_=False	,							**A_	,							):
       '''simple docstring'''




       if voice_preset is not None and not isinstance(A_	,							A_       ):
        if (
            isinstance(A_	,							A_       )
            and self.speaker_embeddings is not None
            and voice_preset in self.speaker_embeddings
        ):
         UpperCamelCase     : str	     =       self._load_voice_preset(A_       )

        else:
         if isinstance(A_	,							A_       ) and not voice_preset.endswith(".npz"       ):
          UpperCamelCase     : Dict	     =       voice_preset + ".npz"

         UpperCamelCase     : int	     =       np.load(A_       )

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

       UpperCamelCase     : int	     =       self.tokenizer(
           A_	,							return_tensors=A_	,							padding="max_length"	,							max_length=A_	,							return_attention_mask=A_	,							return_token_type_ids=A_	,							add_special_tokens=A_	,							**A_	,							)

       if voice_preset is not None:
        UpperCamelCase     : Optional[int]	     =       voice_preset

       return encoded_text







from __future__ import annotations



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			list[str]:
 if partitions <= 0:
  raise ValueError("partitions must be a positive number!"					)
 if partitions > number_of_bytes:
  raise ValueError("partitions can not > number_of_bytes!"					)
 UpperCamelCase     : str	     =       number_of_bytes // partitions
 UpperCamelCase     : List[Any]	     =       []
 for i in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       i * bytes_per_partition + 1
  UpperCamelCase     : Any	     =       (
      number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
  )
  allocation_list.append(F"""{start_bytes}-{end_bytes}"""					)
 return allocation_list


if __name__ == "__main__":
    import doctest

    doctest.testmod()
