code: 
  
  
  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
  
  A_    :					str             =logging.get_logger(__name__)
  
  A_    :					Union[str, Any]             ={"""vocab_file""": """tokenizer.model"""}
  
  A_    :					Any             ={
      """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""",
      },
  }
  A_    :					Optional[int]             ={
      """hf-internal-testing/llama-tokenizer""": 2048,
  }
  A_    :					Optional[Any]             ="""▁"""
  
  A_    :					Optional[Any]             ="""[INST]""", """[/INST]"""
  A_    :					Optional[int]             ="""<<SYS>>\n""", """\n<</SYS>>\n\n"""
  
  # fmt: off
  A_    :					Optional[int]             ="""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"""
  
  
  
  
  
  
  
         snake_case_        =		VOCAB_FILES_NAMES
         snake_case_        =		PRETRAINED_VOCAB_FILES_MAP
         snake_case_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         snake_case_        =		['''input_ids''', '''attention_mask''']
  
  
         def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase="<unk>"			,    _UpperCAmelCase="<s>"			,    _UpperCAmelCase="</s>"			,    _UpperCAmelCase=None			,    _UpperCAmelCase = None			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=False			,    _UpperCAmelCase=True			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		{} if sp_model_kwargs is None else sp_model_kwargs
               a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else bos_token
               a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else eos_token
               a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else unk_token
               a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else pad_token
               super().__init__(
                   bos_token=_UpperCAmelCase			,    eos_token=_UpperCAmelCase			,    unk_token=_UpperCAmelCase			,    pad_token=_UpperCAmelCase			,    add_bos_token=_UpperCAmelCase			,    add_eos_token=_UpperCAmelCase			,    sp_model_kwargs=self.sp_model_kwargs			,    clean_up_tokenization_spaces=_UpperCAmelCase			,    legacy=_UpperCAmelCase			,    **_UpperCAmelCase			,    )
               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(_UpperCAmelCase		)
  
  
         def __getstate__(   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.__dict__.copy()
               a_					=		None
               a_					=		self.sp_model.serialized_model_proto()
               return state
  
  
         def __setstate__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		d
               a_					=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
               self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)
  
  
         @property
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               return self.sp_model.get_piece_size()
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
               vocab.update(self.added_tokens_encoder		)
               return vocab
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               if not self.legacy:
                     a_					=		SPIECE_UNDERLINE + text.replace(_UpperCAmelCase			,    """ """		)
               return super().tokenize(_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               if not self.legacy:
                     a_					=		text.startswith(_UpperCAmelCase		)
                     if is_first:
                           a_					=		text[1:]
  
               a_					=		self.sp_model.encode(_UpperCAmelCase			,    out_type=_UpperCAmelCase		)
  
               if not self.legacy and not is_first and not text.startswith(""" """		) and tokens[0].startswith(_UpperCAmelCase		):
                     a_					=		([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
               return tokens
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               return self.sp_model.piece_to_id(_UpperCAmelCase		)
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.sp_model.IdToPiece(_UpperCAmelCase		)
               return token
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		[]
               a_					=		""""""
               a_					=		False
               for i, token in enumerate(_UpperCAmelCase		):
                     # 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(_UpperCAmelCase		) + token
                           a_					=		True
                           a_					=		[]
                     else:
                           current_sub_tokens.append(_UpperCAmelCase		)
                           a_					=		False
               out_string += self.sp_model.decode(_UpperCAmelCase		)
               return out_string
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               if not os.path.isdir(_UpperCAmelCase		):
                     logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
                     return
               a_					=		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		) and os.path.isfile(self.vocab_file		):
                     copyfile(self.vocab_file			,    _UpperCAmelCase		)
               elif not os.path.isfile(self.vocab_file		):
                     with open(_UpperCAmelCase			,    """wb"""		) as fi:
                           a_					=		self.sp_model.serialized_model_proto()
                           fi.write(_UpperCAmelCase		)
  
               return (out_vocab_file,)
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=None		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    _UpperCAmelCase = False		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_UpperCAmelCase			,    token_ids_a=_UpperCAmelCase			,    already_has_special_tokens=_UpperCAmelCase		)
  
               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(_UpperCAmelCase		)) + eos_token_id
               return (
                   bos_token_id
                   + ([0] * len(_UpperCAmelCase		))
                   + eos_token_id
                   + bos_token_id
                   + ([0] * len(_UpperCAmelCase		))
                   + eos_token_id
               )
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               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 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               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(_UpperCAmelCase		) 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(_UpperCAmelCase		) 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=_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
style_context: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		BartTokenizer
  							snake_case_        =		BartTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		filter_roberta_detectors
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return "lower newer", "lower newer"
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
  																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
  																			a_					=		batch.input_ids.tolist()[0]
  																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			# Test that special tokens are reset
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			# check if input_ids are returned and no labels
  																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
  																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[
  													    """Summary of the text.""",
  													    """Another summary.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
  																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(
  																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization."""]
  													a_					=		[
  													    """Summary of the text.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		inputs["""input_ids"""]
  																			a_					=		targets["""input_ids"""]
  																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
  																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													pass
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
  																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		"""A, <mask> AllenNLP sentence."""
  																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )
  
  																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
  																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)