


'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowercase							=logging.get_logger(__name__)

lowercase							={'vocab_file': 'spm_char.model'}

lowercase							={
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

lowercase							={
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=VOCAB_FILES_NAMES
       UpperCAmelCase										=PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase										=["input_ids", "attention_mask"]






       def __init__(      self  ,							snake_case  ,							snake_case="<s>"  ,							snake_case="</s>"  ,							snake_case="<unk>"  ,							snake_case="<pad>"  ,							snake_case = None  ,							**snake_case  ,							)						->  None:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   ={} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=snake_case  ,							eos_token=snake_case  ,							unk_token=snake_case  ,							pad_token=snake_case  ,							sp_model_kwargs=self.sp_model_kwargs  ,							**snake_case  ,							)

          _UpperCAmelCase       :       Any							   =vocab_file

          _UpperCAmelCase       :       Dict							   =spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(snake_case)






       @property
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          return self.sp_model.get_piece_size()






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   ={self.convert_ids_to_tokens(snake_case): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab






       def __getstate__(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =self.__dict__.copy()
          _UpperCAmelCase       :       Dict							   =None
          return state






       def __setstate__(      self  ,							snake_case)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =d

          # for backward compatibility
          if not hasattr(self  ,							'sp_model_kwargs'):
             _UpperCAmelCase       :       Any							   ={}

          _UpperCAmelCase       :       List[Any]							   =spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(self.vocab_file)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          return self.sp_model.encode(snake_case  ,							out_type=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.sp_model.piece_to_id(snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Union[str, Any]							   =self.sp_model.IdToPiece(snake_case)
          return token






       def       lowerCAmelCase		(      self  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =[]
          _UpperCAmelCase       :       List[Any]							   =''
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                out_string += self.sp_model.decode(snake_case) + token
                _UpperCAmelCase       :       Tuple							   =[]
             else:
                current_sub_tokens.append(snake_case)
          out_string += self.sp_model.decode(snake_case)
          return out_string.strip()






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=None)						->  List[int]:




          '''simple docstring'''
          if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = False)						->  List[int]:




          '''simple docstring'''
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=snake_case  ,							token_ids_a=snake_case  ,							already_has_special_tokens=snake_case)

          _UpperCAmelCase       :       List[Any]							   =[1]
          if token_ids_a is None:
             return ([0] * len(snake_case)) + suffix_ones
          return ([0] * len(snake_case)) + ([0] * len(snake_case)) + suffix_ones






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None)						->  Tuple[str]:




          '''simple docstring'''
          if not os.path.isdir(snake_case):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory")
             return
          _UpperCAmelCase       :       Tuple							   =os.path.join(
              snake_case  ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(snake_case) and os.path.isfile(self.vocab_file):
             copyfile(self.vocab_file  ,							snake_case)
          elif not os.path.isfile(self.vocab_file):
             with open(snake_case  ,							'wb') as fi:
                _UpperCAmelCase       :       Optional[int]							   =self.sp_model.serialized_model_proto()
                fi.write(snake_case)

          return (out_vocab_file,)






'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase							='\nHuman: <<task>>\n\nAssistant: '


lowercase							='huggingface-tools/default-prompts'
lowercase							={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							int="run"							):







   '''simple docstring'''




   if prompt_or_repo_id is None:
      _UpperCAmelCase       :       List[str]							   =DEFAULT_PROMPTS_REPO

   # prompt is considered a repo ID when it does not contain any kind of space
   if re.search('\\s'	,   __lowerCamelCase							) is not None:
      return prompt_or_repo_id

   _UpperCAmelCase       :       Dict							   =cached_file(
       __lowerCamelCase	,   PROMPT_FILES[mode]	,   repo_type='dataset'	,   user_agent={'agent': agent_name}							)
   with open(__lowerCamelCase	,   'r'	,   encoding='utf-8'							) as f:
      return f.read()

