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:
     UpperCAmelCase_       :				Optional[int]		   =      None
  
  UpperCAmelCase_       :				str		   =      logging.get_logger(__name__)
  UpperCAmelCase_       :				int		   =      {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
  
  UpperCAmelCase_       :				Dict		   =      "[INST]", "[/INST]"
  UpperCAmelCase_       :				List[str]		   =      "<<SYS>>\n", "\n<</SYS>>\n\n"
  
  # fmt: off
  UpperCAmelCase_       :				List[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     lowerCamelCase_			(  SCREAMING_SNAKE_CASE__  ):
   _lowercase		:      Optional[int]    				=							VOCAB_FILES_NAMES
   _lowercase		:      Dict    				=							LlamaTokenizer
   _lowercase		:      List[str]    				=							'''left'''
   _lowercase		:      Tuple    				=							['''input_ids''', '''attention_mask''']
  
  
  
  
   def __init__(    self	:    List[Any]				,			__A	:    str=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=False				,			__A	:    int="<unk>"				,			__A	:    Tuple="<s>"				,			__A	:    Optional[Any]="</s>"				,			__A	:    Optional[Any]=True				,			__A	:    int=False				,			**__A	:    List[Any]				,			):
    super().__init__(
        vocab_file=__A				,			tokenizer_file=__A				,			clean_up_tokenization_spaces=__A				,			unk_token=__A				,			bos_token=__A				,			eos_token=__A				,			**__A				,			)
    __A   : str								= add_bos_token
    __A   : str								= add_eos_token
    self.update_post_processor()
  
    __A   : str								= vocab_file
    __A   : List[Any]								= False if not self.vocab_file else True
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Any       ):
    __A   : int								= self.bos_token
    __A   : Union[str, Any]								= self.bos_token_id
  
    __A   : str								= self.eos_token
    __A   : Tuple								= self.eos_token_id
  
    __A   : List[Any]								= F"""{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}"""
    __A   : str								= F"""{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}"""
  
    __A   : 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)       )
    __A   : str								= processors.TemplateProcessing(
        single=__A				,			pair=__A				,			special_tokens=__A       )
  
  
  
  
   @property
   def 					lowerCAmelCase_					(    self	:    Tuple       ):
    return self._add_eos_token
  
  
  
  
   @property
   def 					lowerCAmelCase_					(    self	:    Tuple       ):
    return self._add_bos_token
  
  
  
  
   @add_eos_token.setter
   def 					lowerCAmelCase_					(    self	:    str				,			__A	:    List[str]       ):
    __A   : List[str]								= value
    self.update_post_processor()
  
  
  
  
   @add_bos_token.setter
   def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Tuple       ):
    __A   : Optional[Any]								= value
    self.update_post_processor()
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Dict				,			__A	:    str				,			__A	:    Dict = 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(__A       ):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
     return
    __A   : List[str]								= os.path.join(
        __A				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
  
    if os.path.abspath(self.vocab_file       ) != os.path.abspath(__A       ):
     copyfile(self.vocab_file				,			__A       )
  
    return (out_vocab_file,)
  
  
  
  
   def 					lowerCAmelCase_					(    self	:    Optional[Any]				,			__A	:    int       ):
    __A   : Union[str, Any]								= 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   : Union[str, Any]								= []
    if len(conversation.past_user_inputs       ) > 0:
     if not conversation.past_user_inputs[0].startswith(__A       ) or E_SYS not in conversation.past_user_inputs[0]:
      __A   : Optional[Any]								= (
          B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
      )
    elif not dialogue[0][1].startswith(__A       ) or E_SYS not in dialogue[0][1]:
     __A   : Union[str, 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=__A       )
            + [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=__A       )
    return dialog_tokens
  
  
  
  
  
  
style_context: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''plbart'''
  	_lowercase		:      str    				=							['''past_key_values''']
  	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= vocab_size
  		__A   : str								= max_position_embeddings
  		__A   : Union[str, Any]								= d_model
  		__A   : List[str]								= encoder_ffn_dim
  		__A   : Optional[Any]								= encoder_layers
  		__A   : Tuple								= encoder_attention_heads
  		__A   : List[Any]								= decoder_ffn_dim
  		__A   : Optional[int]								= decoder_layers
  		__A   : Any								= decoder_attention_heads
  		__A   : Optional[int]								= dropout
  		__A   : Any								= attention_dropout
  		__A   : Tuple								= activation_dropout
  		__A   : List[Any]								= activation_function
  		__A   : Tuple								= init_std
  		__A   : List[Any]								= encoder_layerdrop
  		__A   : str								= decoder_layerdrop
  		__A   : Union[str, Any]								= classifier_dropout
  		__A   : Optional[Any]								= use_cache
  		__A   : List[str]								= encoder_layers
  		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return OrderedDict(
  		    [
  		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  		    ]       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  		else:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  
  
  
  
  
  