

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
  from .tokenization_llama import LlamaTokenizer
else:
  UpperCAmelCase__	:       Tuple			      =      None

UpperCAmelCase__	:       List[str]			      =      logging.get_logger(__name__)
UpperCAmelCase__	:       Optional[int]			      =      {"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase__      ,				UpperCAmelCase__	:       Tuple			      =      """[INST]""", """[/INST]"""
UpperCAmelCase__      ,				UpperCAmelCase__	:       List[Any]			      =      """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase__	:       Any			      =      """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     a__    (				__lowerCamelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	Dict         =VOCAB_FILES_NAMES
  UpperCAmelCase__ :	Tuple         =LlamaTokenizer
  UpperCAmelCase__ :	int         ="left"
  UpperCAmelCase__ :	List[Any]         =["input_ids", "attention_mask"]


  def __init__(     self      :	Any			,			UpperCAmelCase__      :	str=None			,			UpperCAmelCase__      :	str=None			,			UpperCAmelCase__      :	Any=False			,			UpperCAmelCase__      :	Optional[int]="<unk>"			,			UpperCAmelCase__      :	int="<s>"			,			UpperCAmelCase__      :	int="</s>"			,			UpperCAmelCase__      :	str=True			,			UpperCAmelCase__      :	Union[str, Any]=False			,			**UpperCAmelCase__      :	Dict			,			)   ->List[Any]:
        """simple docstring"""







        super().__init__(
            vocab_file=_UpperCamelCase			,			tokenizer_file=_UpperCamelCase			,			clean_up_tokenization_spaces=_UpperCamelCase			,			unk_token=_UpperCamelCase			,			bos_token=_UpperCamelCase			,			eos_token=_UpperCamelCase			,			**_UpperCamelCase			,			)
        SCREAMING_SNAKE_CASE	: List[str]					       =				add_bos_token
        SCREAMING_SNAKE_CASE	: Any					       =				add_eos_token
        self.update_post_processor()

        SCREAMING_SNAKE_CASE	: List[str]					       =				vocab_file
        SCREAMING_SNAKE_CASE	: int					       =				False if not self.vocab_file else True


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				self.bos_token
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.bos_token_id

        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.eos_token
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.eos_token_id

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
        SCREAMING_SNAKE_CASE	: List[Any]					       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[]
        if self.add_bos_token:
              special_tokens.append((bos, bos_token_id)						)
        if self.add_eos_token:
              special_tokens.append((eos, eos_token_id)						)
        SCREAMING_SNAKE_CASE	: int					       =				processors.TemplateProcessing(
            single=_UpperCamelCase			,			pair=_UpperCamelCase			,			special_tokens=_UpperCamelCase						)


  @property
  def 	_lowercase (     self      :	Optional[Any]						)   ->List[str]:
        """simple docstring"""







        return self._add_eos_token


  @property
  def 	_lowercase (     self      :	Optional[int]						)   ->Any:
        """simple docstring"""







        return self._add_bos_token


  @add_eos_token.setter
  def 	_lowercase (     self      :	List[str]			,			UpperCAmelCase__      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				value
        self.update_post_processor()


  @add_bos_token.setter
  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	Tuple						)   ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				value
        self.update_post_processor()


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	Optional[str] = None						)   ->Tuple[str]:
        """simple docstring"""







        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                  """tokenizer."""						)

        if not os.path.isdir(_UpperCamelCase						):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
              return
        SCREAMING_SNAKE_CASE	: Any					       =				os.path.join(
            _UpperCamelCase			,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCamelCase						):
              copyfile(self.vocab_file			,			_UpperCamelCase						)

        return (out_vocab_file,)




  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	"Conversation"						)   ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				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...)"""						)

        SCREAMING_SNAKE_CASE	: Dict					       =				[]
        if len(conversation.past_user_inputs						) > 0:
              if not conversation.past_user_inputs[0].startswith(_UpperCamelCase						) or E_SYS not in conversation.past_user_inputs[0]:
                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(
                        B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                    )
        elif not dialogue[0][1].startswith(_UpperCamelCase						) or E_SYS not in dialogue[0][1]:
              SCREAMING_SNAKE_CASE	: Dict					       =				(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=_UpperCamelCase						)
                + [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=_UpperCamelCase						)
        return dialog_tokens







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__	:       int			      =      {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__	:       int			      =      [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__	:       int			      =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




