

'''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_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Tuple       =					'''AutoTokenizer'''
   _A       :     Optional[int]       =					['''tokenizer''']

   _A       :     Optional[int]       =					{
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }
   def __init__( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int=None							):
     super().__init__(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[str]						      =					speaker_embeddings
   @classmethod
   def     __UpperCamelCase						( cls			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      Any="speaker_embeddings_path.json"     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]							):

     if speaker_embeddings_dict_path is not None:
       SCREAMING_SNAKE_CASE:Tuple						      =					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
                    , 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`.'''							)
         SCREAMING_SNAKE_CASE:Tuple						      =					None
       else:
         with open(SCREAMING_SNAKE_CASE__							) as speaker_embeddings_json:
           SCREAMING_SNAKE_CASE:str						      =					json.load(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:str						      =					None

     SCREAMING_SNAKE_CASE:Optional[int]						      =					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

     return cls(tokenizer=SCREAMING_SNAKE_CASE__     ,speaker_embeddings=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Dict     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      Tuple="speaker_embeddings_path.json"     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]="speaker_embeddings"     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     if self.speaker_embeddings is not None:
       os.makedirs(os.path.join(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,"v2"							)     ,exist_ok=SCREAMING_SNAKE_CASE__							)

       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{}

       SCREAMING_SNAKE_CASE:Any						      =					save_directory

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

           SCREAMING_SNAKE_CASE:Union[str, 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__     ,)
             SCREAMING_SNAKE_CASE:str						      =					os.path.join(SCREAMING_SNAKE_CASE__     ,F'''{prompt_key}_{key}.npy'''							)

           SCREAMING_SNAKE_CASE:Optional[int]						      =					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     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      str = None     ,**SCREAMING_SNAKE_CASE__			:      int							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.speaker_embeddings[voice_preset]

     SCREAMING_SNAKE_CASE:Optional[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}].'''							)

       SCREAMING_SNAKE_CASE:Optional[Any]						      =					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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''							)

       SCREAMING_SNAKE_CASE:Dict						      =					np.load(SCREAMING_SNAKE_CASE__							)

     return voice_preset_dict
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_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			:      str     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]="pt"     ,SCREAMING_SNAKE_CASE__			:      List[str]=256     ,SCREAMING_SNAKE_CASE__			:      List[str]=False     ,SCREAMING_SNAKE_CASE__			:      Dict=True     ,SCREAMING_SNAKE_CASE__			:      List[Any]=False     ,**SCREAMING_SNAKE_CASE__			:      Dict     ,):
     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
       ):
         SCREAMING_SNAKE_CASE:Any						      =					self._load_voice_preset(SCREAMING_SNAKE_CASE__							)

       else:
         if isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) and not voice_preset.endswith(".npz"							):
           SCREAMING_SNAKE_CASE:int						      =					voice_preset + ".npz"

         SCREAMING_SNAKE_CASE:Optional[Any]						      =					np.load(SCREAMING_SNAKE_CASE__							)

     if voice_preset is not None:
       self._validate_voice_preset_dict(SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:str						      =					BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE: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:
       SCREAMING_SNAKE_CASE:Any						      =					voice_preset

     return encoded_text









'''simple docstring'''

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  for line in lines:
    SCREAMING_SNAKE_CASE:Any						      =					re.sub(r"#.*"      ,       ""      ,       snake_case )  # remove comments
    if line:
      filtered_lines.append(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					"\n".join(snake_case )

  # Make a hash from all this code
  SCREAMING_SNAKE_CASE:str						      =					full_str.encode("utf-8" )
  return shaaaa(snake_case ).hexdigest()


# get importable module names and hash for caching
A_       						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A_       						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_       						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A_       						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
    _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")






