





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


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

class  __A(						__snake_case ):
     __A	  =  'AutoTokenizer'
     __A	  =  ['tokenizer']

     __A	  =  {
         'semantic_prompt': 1,
         'coarse_prompt': 2,
         'fine_prompt': 2,
     }

     def __init__(      self,       A,       A=None      ):





            """simple docstring"""

            super().__init__(A_      )

            _UpperCamelCase              =						speaker_embeddings

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       A="speaker_embeddings_path.json",       **A      ):





            """simple docstring"""

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

            _UpperCamelCase              =						AutoTokenizer.from_pretrained(A_,       **A_      )

            return cls(tokenizer=A_,       speaker_embeddings=A_      )

     def 	_UpperCamelCase     (      self,       A,       A="speaker_embeddings_path.json",       A="speaker_embeddings",       A = False,       **A,       ):





            """simple docstring"""

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

                   _UpperCamelCase              =						{}

                   _UpperCamelCase              =						save_directory

                   for prompt_key in self.speaker_embeddings:
                          if prompt_key != "repo_or_path":
                                 _UpperCamelCase              =						self._load_voice_preset(A_      )

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

                                 _UpperCamelCase              =						tmp_dict

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

            super().save_pretrained(A_,       A_,       **A_      )

     def 	_UpperCamelCase     (      self,       A = None,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.speaker_embeddings[voice_preset]

            _UpperCamelCase              =						{}
            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              =						get_file_from_repo(
                       self.speaker_embeddings.get('''repo_or_path''',       '''/'''      ),       voice_preset_paths[key],       subfolder=kwargs.pop('''subfolder''',       A_      ),       cache_dir=kwargs.pop('''cache_dir''',       A_      ),       force_download=kwargs.pop('''force_download''',       A_      ),       proxies=kwargs.pop('''proxies''',       A_      ),       resume_download=kwargs.pop('''resume_download''',       A_      ),       local_files_only=kwargs.pop('''local_files_only''',       A_      ),       use_auth_token=kwargs.pop('''use_auth_token''',       A_      ),       revision=kwargs.pop('''revision''',       A_      ),       )
                   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.'''      )

                   _UpperCamelCase              =						np.load(A_      )

            return voice_preset_dict

     def 	_UpperCamelCase     (      self,       A = 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,       A=None,       A=None,       A="pt",       A=256,       A=False,       A=True,       A=False,       **A,       ):





            """simple docstring"""

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

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

                          _UpperCamelCase              =						np.load(A_      )

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

            _UpperCamelCase              =						self.tokenizer(
                A_,       return_tensors=A_,       padding='''max_length''',       max_length=A_,       return_attention_mask=A_,       return_token_type_ids=A_,       add_special_tokens=A_,       **A_,       )

            if voice_preset is not None:
                   _UpperCamelCase              =						voice_preset

            return encoded_text













import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase    : str    	=  logging.get_logger(__name__)

lowercase    : Tuple    	=  {"""vocab_file""": """vocab.json"""}

lowercase    : int    	=  {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

lowercase    : int    	=  {"""mgp-str""": 27}

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(      self,       A,       A="[GO]",       A="[GO]",       A="[s]",       A="[GO]",       **A      ):





            """simple docstring"""

            super().__init__(
                unk_token=A,       bos_token=A,       eos_token=A,       pad_token=A,       **A,       )

            with open(A,       encoding='''utf-8'''      ) as vocab_handle:
                   _UpperCamelCase              =						json.load(A      )
            _UpperCamelCase              =						{v: k for k, v in self.vocab.items()}

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return len(self.vocab      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return dict(self.vocab,       **self.added_tokens_encoder      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[]
            for s in text:
                   char_tokens.extend(A      )
            return char_tokens

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.vocab.get(A,       self.vocab.get(self.unk_token      )      )

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            return self.decoder.get(A      )



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not os.path.isdir(A      ):
                   logger.error('''Vocabulary path ({}) should be a directory'''.format(A      )      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            with open(A,       '''w''',       encoding='''utf-8'''      ) as f:
                   f.write(json.dumps(self.vocab,       indent=2,       sort_keys=A,       ensure_ascii=A      ) + '''\n'''      )

            return (vocab_file,)






