




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	:		Dict							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      "AutoTokenizer"
    UpperCAmelCase__							  =      ["tokenizer"]

    UpperCAmelCase__							  =      {
        "semantic_prompt": 1,
        "coarse_prompt": 2,
        "fine_prompt": 2,
    }


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Tuple					, UpperCAmelCase__      :	Union[str, Any]=None)						->Optional[Any]:





           '''simple docstring'''

           super().__init__(lowerCamelCase__)

           A__       =    speaker_embeddings


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Optional[Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[int]="speaker_embeddings_path.json"					, **UpperCAmelCase__      :	int)						->Any:





           '''simple docstring'''

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

           A__       =    AutoTokenizer.from_pretrained(lowerCamelCase__					, **lowerCamelCase__)

           return cls(tokenizer=lowerCamelCase__					, speaker_embeddings=lowerCamelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	Optional[Any]="speaker_embeddings_path.json"					, UpperCAmelCase__      :	Tuple="speaker_embeddings"					, UpperCAmelCase__      :	bool = False					, **UpperCAmelCase__      :	str					, )						->Union[str, Any]:





           '''simple docstring'''

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

                  A__       =    {}

                  A__       =    save_directory

                  for prompt_key in self.speaker_embeddings:
                         if prompt_key != "repo_or_path":
                                A__       =    self._load_voice_preset(lowerCamelCase__)

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

                                A__       =    tmp_dict

                  with open(os.path.join(lowerCamelCase__					, lowerCamelCase__)					, '''w''') as fp:
                         json.dump(lowerCamelCase__					, lowerCamelCase__)

           super().save_pretrained(lowerCamelCase__					, lowerCamelCase__					, **lowerCamelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	str = None					, **UpperCAmelCase__      :	Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__       =    self.speaker_embeddings[voice_preset]

           A__       =    {}
           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}].""")

                  A__       =    get_file_from_repo(
                      self.speaker_embeddings.get('''repo_or_path'''					, '''/''')					, voice_preset_paths[key]					, subfolder=kwargs.pop('''subfolder'''					, lowerCamelCase__)					, cache_dir=kwargs.pop('''cache_dir'''					, lowerCamelCase__)					, force_download=kwargs.pop('''force_download'''					, lowerCamelCase__)					, proxies=kwargs.pop('''proxies'''					, lowerCamelCase__)					, resume_download=kwargs.pop('''resume_download'''					, lowerCamelCase__)					, local_files_only=kwargs.pop('''local_files_only'''					, lowerCamelCase__)					, use_auth_token=kwargs.pop('''use_auth_token'''					, lowerCamelCase__)					, revision=kwargs.pop('''revision'''					, lowerCamelCase__)					, )
                  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.""")

                  A__       =    np.load(lowerCamelCase__)

           return voice_preset_dict


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	Optional[dict] = None)						->Any:





           '''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      :	int					, UpperCAmelCase__      :	Union[str, Any]=None					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Dict="pt"					, UpperCAmelCase__      :	List[Any]=256					, UpperCAmelCase__      :	Optional[Any]=False					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	List[Any]=False					, **UpperCAmelCase__      :	Tuple					, )						->Tuple:





           '''simple docstring'''

           if voice_preset is not None and not isinstance(lowerCamelCase__					, lowerCamelCase__):
                  if (
                      isinstance(lowerCamelCase__					, lowerCamelCase__)
                      and self.speaker_embeddings is not None
                      and voice_preset in self.speaker_embeddings
                  ):
                         A__       =    self._load_voice_preset(lowerCamelCase__)

                  else:
                         if isinstance(lowerCamelCase__					, lowerCamelCase__) and not voice_preset.endswith('''.npz'''):
                                A__       =    voice_preset + ".npz"

                         A__       =    np.load(lowerCamelCase__)

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

           A__       =    self.tokenizer(
               lowerCamelCase__					, return_tensors=lowerCamelCase__					, padding='''max_length'''					, max_length=lowerCamelCase__					, return_attention_mask=lowerCamelCase__					, return_token_type_ids=lowerCamelCase__					, add_special_tokens=lowerCamelCase__					, **lowerCamelCase__					, )

           if voice_preset is not None:
                  A__       =    voice_preset

           return encoded_text






'''simple docstring'''


import math






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return math.sqrt(__lowerCAmelCase      ) * math.sqrt(__lowerCAmelCase      ) == num






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					0
		_UpperCAmelCase						:				Tuple	    =					n
		while left <= right:
				_UpperCAmelCase						:				int	    =					(left + right) // 2
				if mid**2 == n:
						return True
				elif mid**2 > n:
						_UpperCAmelCase						:				str	    =					mid - 1
				else:
						_UpperCAmelCase						:				List[str]	    =					mid + 1
		return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



