code: 
  
  
  '''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
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{'''vocab_file''': '''tokenizer.model'''}
  
  UpperCAmelCase__      =							{
      '''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''',
      },
  }
  UpperCAmelCase__      =							{
      '''hf-internal-testing/llama-tokenizer''': 2_0_4_8,
  }
  UpperCAmelCase__      =							'''▁'''
  
  UpperCAmelCase__ ,     UpperCAmelCase__      =							'''[INST]''', '''[/INST]'''
  UpperCAmelCase__ ,     UpperCAmelCase__      =							'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''
  
  # fmt: off
  UpperCAmelCase__      =							'''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 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= VOCAB_FILES_NAMES
  			A	:							Optional[Any]							= PRETRAINED_VOCAB_FILES_MAP
  			A	:							List[Any]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			A	:							str							= ['''input_ids''', '''attention_mask''']
  			def __init__(     self       : Dict ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Union[str, Any]="<unk>" ,						lowerCAmelCase_       : str="<s>" ,						lowerCAmelCase_       : Any="</s>" ,						lowerCAmelCase_       : Optional[Any]=None ,						lowerCAmelCase_       : Optional[Dict[str, Any]] = None ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : str=False ,						lowerCAmelCase_       : Any=False ,						lowerCAmelCase_       : Dict=True ,						**lowerCAmelCase_       : List[str] ,						)			->		Optional[int]:
  						__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       : Optional[Any]  )			->		Optional[Any]:
  						__A=  self.__dict__.copy()
  						__A=  None
  						__A=  self.sp_model.serialized_model_proto()
  						return state
  			def __setstate__(     self       : Union[str, Any] ,						lowerCAmelCase_       : Union[str, Any]  )			->		Dict:
  						__A=  d
  						__A=  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
  						self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  			@property
  			def 				lowerCAmelCase    (     self       : Dict  )			->		Union[str, Any]:
  						return self.sp_model.get_piece_size()
  			def 				lowerCAmelCase    (     self       : Dict  )			->		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 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Optional[int] ,						**lowerCAmelCase_       : int  )			->		List[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 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Union[str, Any]  )			->		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 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[Any]  )			->		List[str]:
  						return self.sp_model.piece_to_id(lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Dict  )			->		int:
  						__A=  self.sp_model.IdToPiece(lowerCAmelCase_  )
  						return token
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Optional[Any]  )			->		Optional[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 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Optional[str] = None  )			->		Tuple[str]:
  						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 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Union[str, Any]=None  )			->		Any:
  						__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 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : List[int] ,						lowerCAmelCase_       : Optional[List[int]] = None ,						lowerCAmelCase_       : bool = False  )			->		List[int]:
  						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 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : List[int] ,						lowerCAmelCase_       : Optional[List[int]] = None  )			->		List[int]:
  						__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 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : "Conversation"  )			->		List[int]:
  						__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
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  