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:
  				SCREAMING_SNAKE_CASE__    :    Optional[int]								=					None
  
  SCREAMING_SNAKE_CASE__    :    str								=					logging.get_logger(__name__)
  SCREAMING_SNAKE_CASE__    :    int								=					{"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}
  
  SCREAMING_SNAKE_CASE__    :    Dict								=					"[INST]", "[/INST]"
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"<<SYS>>\n", "\n<</SYS>>\n\n"
  
  # fmt: off
  SCREAMING_SNAKE_CASE__    :    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 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A     :   List[Any]						       =   VOCAB_FILES_NAMES
  	A     :   Tuple						       =   LlamaTokenizer
  	A     :   Union[str, Any]						       =   '''left'''
  	A     :   str						       =   ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE="<unk>"			,    SCREAMING_SNAKE_CASE="<s>"			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=False			,    **SCREAMING_SNAKE_CASE			,    )     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(
  				    vocab_file=SCREAMING_SNAKE_CASE			,    tokenizer_file=SCREAMING_SNAKE_CASE			,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE			,    unk_token=SCREAMING_SNAKE_CASE			,    bos_token=SCREAMING_SNAKE_CASE			,    eos_token=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			,    )
  				SCREAMING_SNAKE_CASE_			=	add_bos_token
  				SCREAMING_SNAKE_CASE_			=	add_eos_token
  				self.update_post_processor()
  
  				SCREAMING_SNAKE_CASE_			=	vocab_file
  				SCREAMING_SNAKE_CASE_			=	False if not self.vocab_file else True
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.bos_token
  				SCREAMING_SNAKE_CASE_			=	self.bos_token_id
  
  				SCREAMING_SNAKE_CASE_			=	self.eos_token
  				SCREAMING_SNAKE_CASE_			=	self.eos_token_id
  
  				SCREAMING_SNAKE_CASE_			=	f'{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'
  				SCREAMING_SNAKE_CASE_			=	f'{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'
  
  				SCREAMING_SNAKE_CASE_			=	[]
  				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_			=	processors.TemplateProcessing(
  				    single=SCREAMING_SNAKE_CASE			,    pair=SCREAMING_SNAKE_CASE			,    special_tokens=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self._add_eos_token
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self._add_bos_token
  
  
  
  
  
  	@add_eos_token.setter
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	value
  				self.update_post_processor()
  
  
  
  
  
  	@add_bos_token.setter
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	value
  				self.update_post_processor()
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 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(SCREAMING_SNAKE_CASE			):
  							logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
  							return
  				SCREAMING_SNAKE_CASE_			=	os.path.join(
  				    SCREAMING_SNAKE_CASE			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
  
  				if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE			):
  							copyfile(self.vocab_file			,    SCREAMING_SNAKE_CASE			)
  
  				return (out_vocab_file,)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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_			=	[]
  				if len(conversation.past_user_inputs			) > 0:
  							if not conversation.past_user_inputs[0].startswith(SCREAMING_SNAKE_CASE			) or E_SYS not in conversation.past_user_inputs[0]:
  										SCREAMING_SNAKE_CASE_			=	(
  										    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
  										)
  				elif not dialogue[0][1].startswith(SCREAMING_SNAKE_CASE			) or E_SYS not in dialogue[0][1]:
  							SCREAMING_SNAKE_CASE_			=	(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=SCREAMING_SNAKE_CASE			)
  				        + [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=SCREAMING_SNAKE_CASE			)
  				return dialog_tokens
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  