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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[int]    			=		'''AutoTokenizer'''
 lowerCamelCase	:Tuple    			=		['''tokenizer''']

 lowerCamelCase	:Union[str, Any]    			=		{
     '''semantic_prompt''': 1,
     '''coarse_prompt''': 2,
     '''fine_prompt''': 2,
 }



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=None							)  ->					List[str]:
        super().__init__(lowerCAmelCase_							)

        _A        =  speaker_embeddings



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_		,				lowerCAmelCase_="speaker_embeddings_path.json"		,				**lowerCAmelCase_							)  ->					Any:

        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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_="speaker_embeddings_path.json"		,				lowerCAmelCase_="speaker_embeddings"		,				lowerCAmelCase_ = False		,				**lowerCAmelCase_		,				)  ->					str:
        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      UpperCAmelCase							(					self		,				lowerCAmelCase_ = None		,				**lowerCAmelCase_							)  ->					Dict:
        _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      UpperCAmelCase							(					self		,				lowerCAmelCase_ = None							)  ->					Optional[int]:
        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		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				lowerCAmelCase_="pt"		,				lowerCAmelCase_=2_56		,				lowerCAmelCase_=False		,				lowerCAmelCase_=True		,				lowerCAmelCase_=False		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        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





import math
import sys






def  snake_case  (       snake_case__			:int)						->   int:
       if number != int(snake_case__):
              raise ValueError("""the value of input must be a natural number""")
       if number < 0:
              raise ValueError("""the value of input must not be a negative number""")
       if number == 0:
              return 1
       _A        =  [-1] * (number + 1)
       _A        =  0
       for i in range(1   ,	number + 1):
              _A        =  sys.maxsize
              _A        =  int(math.sqrt(snake_case__))
              for j in range(1   ,	root + 1):
                     _A        =  1 + answers[i - (j**2)]
                     _A        =  min(snake_case__   ,	snake_case__)
              _A        =  answer
       return answers[number]


if __name__ == "__main__":
    import doctest

    doctest.testmod()




