code: 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:
   a     :			Any					    =						None
  
  a     :			str					    =						logging.get_logger(__name__)
  a     :			Union[str, Any]					    =						{"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}
  
  a    ,   a     :			Union[str, Any]					    =						"""[INST]""", """[/INST]"""
  a    ,   a     :			Optional[int]					    =						"""<<SYS>>\n""", """\n<</SYS>>\n\n"""
  
  # fmt: off
  a     :			List[str]					    =						"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
  answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
  that your responses are socially unbiased and positive in nature.
  
  If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
  correct. If you don't know the answer to a question, please don't share false information."""
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						VOCAB_FILES_NAMES
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						LlamaTokenizer
      SCREAMING_SNAKE_CASE:							Optional[int]        =						'left'
      SCREAMING_SNAKE_CASE:							Tuple        =						['input_ids', 'attention_mask']
  
  
  
  
      def __init__(						self					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__=False					,	lowerCamelCase__="<unk>"					,	lowerCamelCase__="<s>"					,	lowerCamelCase__="</s>"					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	**lowerCamelCase__					,	):
         super().__init__(
             vocab_file=lowerCamelCase__					,	tokenizer_file=lowerCamelCase__					,	clean_up_tokenization_spaces=lowerCamelCase__					,	unk_token=lowerCamelCase__					,	bos_token=lowerCamelCase__					,	eos_token=lowerCamelCase__					,	**lowerCamelCase__					,	)
         lowerCAmelCase_:       Dict         =  add_bos_token
         lowerCAmelCase_:       Tuple         =  add_eos_token
         self.update_post_processor()
  
         lowerCAmelCase_:       List[Any]         =  vocab_file
         lowerCAmelCase_:       Tuple         =  False if not self.vocab_file else True
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[int]         =  self.bos_token
         lowerCAmelCase_:       List[str]         =  self.bos_token_id
  
         lowerCAmelCase_:       Union[str, Any]         =  self.eos_token
         lowerCAmelCase_:       str         =  self.eos_token_id
  
         lowerCAmelCase_:       int         =  F'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
         lowerCAmelCase_:       List[str]         =  F'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''
  
         lowerCAmelCase_:       Optional[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)   )
         lowerCAmelCase_:       Any         =  processors.TemplateProcessing(
             single=lowerCamelCase__					,	pair=lowerCamelCase__					,	special_tokens=lowerCamelCase__   )
  
  
  
  
      @property
      def     _a						(						self   ):
         return self._add_eos_token
  
  
  
  
      @property
      def     _a						(						self   ):
         return self._add_bos_token
  
  
  
  
      @add_eos_token.setter
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  value
         self.update_post_processor()
  
  
  
  
      @add_bos_token.setter
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       int         =  value
         self.update_post_processor()
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         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(lowerCamelCase__   ):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
            return
         lowerCAmelCase_:       Any         =  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__   ):
            copyfile(self.vocab_file					,	lowerCamelCase__   )
  
         return (out_vocab_file,)
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[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...)"   )
  
         lowerCAmelCase_:       Tuple         =  []
         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]:
               lowerCAmelCase_:       int         =  (
                   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]:
            lowerCAmelCase_:       Any         =  (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
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float
  
  
  
  
      @property
      def     _a						(						self   ):
         return sizeof(self.value   )
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
     # < 0 is possible if using a dynamic axis
     if dimension <= 0:
        lowerCAmelCase_:       int         =  fixed_dimension
  
     dimension -= num_token_to_add
     return dimension
  def 		snake_case__  (		lowercase       ,							lowercase				):
     return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
     try:
        return AutoProcessor.from_pretrained(lowercase				)
     except (ValueError, OSError, KeyError):
        lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  None, None
        try:
           lowerCAmelCase_:       Dict         =  AutoTokenizer.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
        try:
           lowerCAmelCase_:       Optional[int]         =  AutoFeatureExtractor.from_pretrained(lowercase				)
        except (OSError, KeyError):
           pass
  
        if tokenizer is not None and feature_extractor is not None:
           raise ValueError(
               F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
        elif tokenizer is None and feature_extractor is None:
           return None
        elif tokenizer is not None:
           return tokenizer
        else:
           return feature_extractor