






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  (   A__	):
 _lowerCAmelCase	:	Optional[Any]				=			'''AutoTokenizer'''
 _lowerCAmelCase	:	Optional[int]				=			['''tokenizer''']

 _lowerCAmelCase	:	Dict				=			{
     '''semantic_prompt''': 1,
     '''coarse_prompt''': 2,
     '''fine_prompt''': 2,
 }







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       List[str]=None						):


      '''simple docstring'''






      super().__init__(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  speaker_embeddings







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


      '''simple docstring'''






      if speaker_embeddings_dict_path is not None:
           UpperCamelCase__      :       List[str]	    =  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__      :       Dict	    =  json.load(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__      :       List[str]	    =  None

      UpperCamelCase__      :       str	    =  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

      return cls(tokenizer=SCREAMING_SNAKE_CASE							,       speaker_embeddings=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       List[Any]="speaker_embeddings_path.json"							,       SCREAMING_SNAKE_CASE :       int="speaker_embeddings"							,       SCREAMING_SNAKE_CASE :       bool = False							,       **SCREAMING_SNAKE_CASE :       Dict							,       ):


      '''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__      :       Union[str, Any]	    =  {}

           UpperCamelCase__      :       Optional[int]	    =  save_directory

           for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                     UpperCamelCase__      :       List[str]	    =  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[Any]	    =  os.path.join(SCREAMING_SNAKE_CASE							,       F'{prompt_key}_{key}.npy'						)

                     UpperCamelCase__      :       Optional[Any]	    =  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  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       str = None							,       **SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.speaker_embeddings[voice_preset]

      UpperCamelCase__      :       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__      :       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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'						)

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

      return voice_preset_dict







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       Optional[dict] = 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 :       Tuple							,       SCREAMING_SNAKE_CASE :       str=None							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Tuple="pt"							,       SCREAMING_SNAKE_CASE :       List[str]=2_56							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       SCREAMING_SNAKE_CASE :       Any=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__      :       Union[str, Any]	    =  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__      :       Any	    =  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__      :       Tuple	    =  voice_preset

      return encoded_text







import re







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     bool:
     UpperCamelCase__      :       Union[str, Any]	    =  re.compile(
         R"^(?:0|94|\+94|0{2}94)" R"7(0|1|2|4|5|6|7|8)" R"(-| |)" R"\d{7}$"      )

     return bool(re.search(__lowerCAmelCase ,   __lowerCAmelCase      )      )


if __name__ == "__main__":
 lowerCamelCase     :							Union[str, Any]    ='''0094702343221'''

 print(is_sri_lankan_phone_number(phone))