





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 :  Optional[int]										=  logging.get_logger(__name__)



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Optional[Any]							=     'AutoTokenizer'
      _UpperCAmelCase			:Tuple							=     ['tokenizer']

      _UpperCAmelCase			:int							=     {
          'semantic_prompt': 1,
          'coarse_prompt': 2,
          'fine_prompt': 2,
      }





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




       super().__init__(A_     )

       UpperCamelCase			:      List[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			:      Any			=     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			:      Optional[int]			=     None
        else:
         with open(A_     ) as speaker_embeddings_json:
          UpperCamelCase			:      Optional[Any]			=     json.load(A_     )
       else:
        UpperCamelCase			:      Any			=     None

       UpperCamelCase			:      Optional[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			:      Dict			=     {}

        UpperCamelCase			:      int			=     save_directory

        for prompt_key in self.speaker_embeddings:
         if prompt_key != "repo_or_path":
          UpperCamelCase			:      Any			=     self._load_voice_preset(A_     )

          UpperCamelCase			:      List[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			:      List[Any]			=     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			:      Optional[int]			=     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			:      Dict			=     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			:      Union[str, Any]			=     self._load_voice_preset(A_     )

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

         UpperCamelCase			:      Optional[int]			=     np.load(A_     )

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

       UpperCamelCase			:      Tuple			=     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			:      Tuple			=     voice_preset

       return encoded_text







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class       A__     ( __snake_case    ):





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




       super().__init__(
           features=A_ ,							cache_dir=A_ ,							keep_in_memory=A_ ,							streaming=A_ ,							num_proc=A_ ,							**A_ ,							)
       UpperCamelCase			:      Optional[int] 			=     Generator(
           cache_dir=A_ ,							features=A_ ,							generator=A_ ,							gen_kwargs=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.streaming:
        UpperCamelCase			:      Optional[Any] 			=     self.builder.as_streaming_dataset(split="train"     )
       # Build regular (map-style) dataset
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      Union[str, Any] 			=     None
        UpperCamelCase			:      List[Any] 			=     None
        UpperCamelCase			:      List[str] 			=     None

        self.builder.download_and_prepare(
            download_config=A_ ,							download_mode=A_ ,							verification_mode=A_ ,							base_path=A_ ,							num_proc=self.num_proc ,							)
        UpperCamelCase			:      int 			=     self.builder.as_dataset(
            split="train" ,							verification_mode=A_ ,							in_memory=self.keep_in_memory     )
       return dataset
