



'''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


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Any     =       "AutoTokenizer"
       lowerCAmelCase__	:Any     =       ["tokenizer"]

       lowerCAmelCase__	:Optional[int]     =       {
           "semantic_prompt": 1,
           "coarse_prompt": 2,
           "fine_prompt": 2,
       }



       def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_=None   )	->				Union[str, Any]:
           super().__init__(UpperCAmelCase_   )

           lowercase__									=    speaker_embeddings



       @classmethod
       def 				_a   (					cls  ,UpperCAmelCase_  ,UpperCAmelCase_="speaker_embeddings_path.json"  ,**UpperCAmelCase_   )	->				Union[str, Any]:

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

           lowercase__									=    AutoTokenizer.from_pretrained(UpperCAmelCase_  ,**UpperCAmelCase_   )

           return cls(tokenizer=UpperCAmelCase_  ,speaker_embeddings=UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_="speaker_embeddings_path.json"  ,UpperCAmelCase_="speaker_embeddings"  ,UpperCAmelCase_ = False  ,**UpperCAmelCase_  ,)	->				Any:
           if self.speaker_embeddings is not None:
               os.makedirs(os.path.join(UpperCAmelCase_  ,UpperCAmelCase_  ,"v2"   )  ,exist_ok=UpperCAmelCase_   )

               lowercase__									=    {}

               lowercase__									=    save_directory

               for prompt_key in self.speaker_embeddings:
                   if prompt_key != "repo_or_path":
                       lowercase__									=    self._load_voice_preset(UpperCAmelCase_   )

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

                       lowercase__									=    tmp_dict

               with open(os.path.join(UpperCAmelCase_  ,UpperCAmelCase_   )  ,"w"   ) as fp:
                   json.dump(UpperCAmelCase_  ,UpperCAmelCase_   )

           super().save_pretrained(UpperCAmelCase_  ,UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_   )	->				Optional[Any]:
           lowercase__									=    self.speaker_embeddings[voice_preset]

           lowercase__									=    {}
           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}].'''   )

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

               lowercase__									=    np.load(UpperCAmelCase_   )

           return voice_preset_dict



       def 				_a   (					self  ,UpperCAmelCase_ = None   )	->				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  ,UpperCAmelCase_=None  ,UpperCAmelCase_=None  ,UpperCAmelCase_="pt"  ,UpperCAmelCase_=256  ,UpperCAmelCase_=False  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,**UpperCAmelCase_  ,)	->				Union[str, Any]:
           if voice_preset is not None and not isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ):
               if (
                   isinstance(UpperCAmelCase_  ,UpperCAmelCase_   )
                   and self.speaker_embeddings is not None
                   and voice_preset in self.speaker_embeddings
               ):
                   lowercase__									=    self._load_voice_preset(UpperCAmelCase_   )

               else:
                   if isinstance(UpperCAmelCase_  ,UpperCAmelCase_   ) and not voice_preset.endswith(".npz"   ):
                       lowercase__									=    voice_preset + ".npz"

                   lowercase__									=    np.load(UpperCAmelCase_   )

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

           lowercase__									=    self.tokenizer(
               UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,padding="max_length"  ,max_length=UpperCAmelCase_  ,return_attention_mask=UpperCAmelCase_  ,return_token_type_ids=UpperCAmelCase_  ,add_special_tokens=UpperCAmelCase_  ,**UpperCAmelCase_  ,)

           if voice_preset is not None:
               lowercase__									=    voice_preset

           return encoded_text









'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    if b == 0:
        return (1, 0)
    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,a % b							)
    lowercase__									=    a // b
    return (y, x - k * y)




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    ((lowercase__)							,							(lowercase__))									=    extended_euclid(_snake_case  ,_snake_case							)
    if b < 0:
        lowercase__									=    (b % n + n) % n
    return b




def 							lowerCamelCase	(					_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int  ,_snake_case	:    int							):





    '''simple docstring'''


    lowercase__							,							lowercase__									=    invert_modulo(_snake_case  ,_snake_case							), invert_modulo(_snake_case  ,_snake_case							)
    lowercase__									=    na * na
    lowercase__									=    ra * x * na + ra * y * na
    return (n % m + m) % m


if __name__ == "__main__":
 from doctest import testmod

 testmod(name="chinese_remainder_theorem", verbose=True)
 testmod(name="chinese_remainder_theorem2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_euclid", verbose=True)




