



"""simple docstring"""







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

__snake_case	:				str  					=			logging.get_logger(__name__)

__snake_case	:				Dict  					=			{'vocab_file': 'tokenizer.model'}

__snake_case	:				Dict  					=			{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
__snake_case	:				Optional[int]  					=			{
    'hf-internal-testing/llama-tokenizer': 2_048,
}
__snake_case	:				Union[str, Any]  					=			'▁'

__snake_case					,  __snake_case	:				Optional[Any]  					=			'[INST]', '[/INST]'
__snake_case					,  __snake_case	:				int  					=			'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
__snake_case	:				str  					=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: Optional[int]				   =VOCAB_FILES_NAMES
     _lowerCamelCase					: Tuple				   =PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase					: Optional[int]				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase					: Optional[Any]				   =["input_ids", "attention_mask"]





     def __init__(					self :							Dict	,      _lowerCamelCase :							str	,      _lowerCamelCase :							int="<unk>"	,      _lowerCamelCase :							Optional[Any]="<s>"	,      _lowerCamelCase :							List[str]="</s>"	,      _lowerCamelCase :							Dict=None	,      _lowerCamelCase :							Optional[Dict[str, Any]] = None	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							int=False	,      _lowerCamelCase :							Optional[Any]=False	,      _lowerCamelCase :							str=True	,      **_lowerCamelCase :							List[str]	,      ):
          A__				=   {} if sp_model_kwargs is None else sp_model_kwargs
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else bos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else eos_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else unk_token
          A__				=   AddedToken(_lowerCamelCase	,      lstrip=_lowerCamelCase	,      rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase	,      _lowerCamelCase ) else pad_token
          super().__init__(
              bos_token=_lowerCamelCase	,      eos_token=_lowerCamelCase	,      unk_token=_lowerCamelCase	,      pad_token=_lowerCamelCase	,      add_bos_token=_lowerCamelCase	,      add_eos_token=_lowerCamelCase	,      sp_model_kwargs=self.sp_model_kwargs	,      clean_up_tokenization_spaces=_lowerCamelCase	,      legacy=_lowerCamelCase	,      **_lowerCamelCase	,      )
          if legacy:
               logger.warning_once(
                   F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
                   ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
          A__				=   legacy
          A__				=   vocab_file
          A__				=   add_bos_token
          A__				=   add_eos_token
          A__				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(_lowerCamelCase )





     def __getstate__(					self :							List[str] ):
          A__				=   self.__dict__.copy()
          A__				=   None
          A__				=   self.sp_model.serialized_model_proto()
          return state





     def __setstate__(					self :							Tuple	,      _lowerCamelCase :							str ):
          A__				=   d
          A__				=   spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )





     @property
     def 						A__  (					self :							Tuple ):
          return self.sp_model.get_piece_size()





     def 						A__  (					self :							int ):
          A__				=   {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Dict	,      **_lowerCamelCase :							str ):
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
               A__				=   SPIECE_UNDERLINE + text.replace(_lowerCamelCase	,      ''' ''' )
          return super().tokenize(_lowerCamelCase	,      **_lowerCamelCase )





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							int ):
          if not self.legacy:
               A__				=   text.startswith(_lowerCamelCase )
               if is_first:
                    A__				=   text[1:]

          A__				=   self.sp_model.encode(_lowerCamelCase	,      out_type=_lowerCamelCase )

          if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
               A__				=   ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
          return tokens





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Union[str, Any] ):
          return self.sp_model.piece_to_id(_lowerCamelCase )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Dict ):
          A__				=   self.sp_model.IdToPiece(_lowerCamelCase )
          return token





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							List[Any] ):
          A__				=   []
          A__				=   ''''''
          A__				=   False
          for i, token in enumerate(_lowerCamelCase ):
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special and i != 0:
                         out_string += " "
                    out_string += self.sp_model.decode(_lowerCamelCase ) + token
                    A__				=   True
                    A__				=   []
               else:
                    current_sub_tokens.append(_lowerCamelCase )
                    A__				=   False
          out_string += self.sp_model.decode(_lowerCamelCase )
          return out_string





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Optional[str] = None ):
          if not os.path.isdir(_lowerCamelCase ):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
               return
          A__				=   os.path.join(
              _lowerCamelCase	,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file	,      _lowerCamelCase )
          elif not os.path.isfile(self.vocab_file ):
               with open(_lowerCamelCase	,      '''wb''' ) as fi:
                    A__				=   self.sp_model.serialized_model_proto()
                    fi.write(_lowerCamelCase )

          return (out_vocab_file,)





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							str=None ):
          A__				=   [self.bos_token_id] if self.add_bos_token else []
          A__				=   [self.eos_token_id] if self.add_eos_token else []

          A__				=   bos_token_id + token_ids_a + eos_token_id

          if token_ids_a is not None:
               A__				=   output + bos_token_id + token_ids_a + eos_token_id

          return output





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							List[int]	,      _lowerCamelCase :							Optional[List[int]] = None	,      _lowerCamelCase :							bool = False ):
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_lowerCamelCase	,      token_ids_a=_lowerCamelCase	,      already_has_special_tokens=_lowerCamelCase )

          A__				=   [1] if self.add_bos_token else []
          A__				=   [1] if self.add_eos_token else []

          if token_ids_a is None:
               return bos_token_id + ([0] * len(_lowerCamelCase )) + eos_token_id
          return (
              bos_token_id
              + ([0] * len(_lowerCamelCase ))
              + eos_token_id
              + bos_token_id
              + ([0] * len(_lowerCamelCase ))
              + eos_token_id
          )





     def 						A__  (					self :							str	,      _lowerCamelCase :							List[int]	,      _lowerCamelCase :							Optional[List[int]] = None ):
          A__				=   [self.bos_token_id] if self.add_bos_token else []
          A__				=   [self.eos_token_id] if self.add_eos_token else []

          A__				=   [0] * len(bos_token_id + token_ids_a + eos_token_id )

          if token_ids_a is not None:
               output += [1] * len(bos_token_id + token_ids_a + eos_token_id )

          return output





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							"Conversation" ):
          A__				=   list(conversation.iter_texts() )
          if not all([is_user for is_user, msg in dialogue[::2]] ) or not all(
              [not is_user for is_user, msg in dialogue[1::2]] ):
               raise ValueError(
                   '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)''' )

          A__				=   []
          if len(conversation.past_user_inputs ) > 0:
               if not conversation.past_user_inputs[0].startswith(_lowerCamelCase ) or E_SYS not in conversation.past_user_inputs[0]:
                    A__				=   (
                        B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                    )
          elif not dialogue[0][1].startswith(_lowerCamelCase ) or E_SYS not in dialogue[0][1]:
               A__				=   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

          dialog_tokens += sum(
              [
                  [self.bos_token_id]
                  + self.encode(
                      F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''	,      add_special_tokens=_lowerCamelCase )
                  + [self.eos_token_id]
                  for prompt, answer in zip(dialogue[::2]	,      dialogue[1::2] )
              ]	,      []	,      )
          if not (dialogue[-1][0]):
               raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}''' )
          dialog_tokens += [self.bos_token_id] + self.encode(
              F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''	,      add_special_tokens=_lowerCamelCase )
          return dialog_tokens








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case	:				List[Any]  					=			{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case	:				str  					=			[
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 __snake_case	:				Union[str, Any]  					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



