242 lines
8.7 KiB
Python
242 lines
8.7 KiB
Python
# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for IntermLM."""
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
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PRETRAINED_VOCAB_FILES_MAP = {}
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class InternLMTokenizer(PreTrainedTokenizer):
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"""
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Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
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model_input_names = ["input_ids", "attention_mask"]
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_auto_class = "AutoTokenizer"
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def __init__(
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self,
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vocab_file,
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unk_token="<unk>",
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bos_token="<s>",
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eos_token="</s>",
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pad_token="</s>",
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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add_bos_token=True,
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add_eos_token=False,
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decode_with_prefix_space=False,
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clean_up_tokenization_spaces=False,
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**kwargs,
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):
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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self.vocab_file = vocab_file
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self.add_bos_token = add_bos_token
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self.add_eos_token = add_eos_token
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self.decode_with_prefix_space = decode_with_prefix_space
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(vocab_file)
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self._no_prefix_space_tokens = None
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""" Initialisation"""
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@property
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def no_prefix_space_tokens(self):
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if self._no_prefix_space_tokens is None:
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vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
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self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
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return self._no_prefix_space_tokens
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@property
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def vocab_size(self):
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"""Returns vocab size"""
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return self.sp_model.get_piece_size()
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@property
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def bos_token_id(self) -> Optional[int]:
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return self.sp_model.bos_id()
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@property
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def eos_token_id(self) -> Optional[int]:
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return self.sp_model.eos_id()
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def get_vocab(self):
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"""Returns vocab as a dict"""
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text):
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"""Returns a tokenized string."""
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.sp_model.piece_to_id(token)
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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token = self.sp_model.IdToPiece(index)
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return token
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def _maybe_add_prefix_space(self, tokens, decoded):
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if tokens and tokens[0] not in self.no_prefix_space_tokens:
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return " " + decoded
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else:
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return decoded
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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current_sub_tokens = []
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out_string = ""
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prev_is_special = False
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for token in tokens:
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# make sure that special tokens are not decoded using sentencepiece model
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if token in self.all_special_tokens:
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if not prev_is_special:
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out_string += " "
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out_string += self.sp_model.decode(current_sub_tokens) + token
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prev_is_special = True
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current_sub_tokens = []
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else:
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current_sub_tokens.append(token)
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prev_is_special = False
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out_string += self.sp_model.decode(current_sub_tokens)
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out_string = self.clean_up_tokenization(out_string)
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out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
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return out_string[1:]
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def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
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"""
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Save the vocabulary and special tokens file to a directory.
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Args:
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save_directory (`str`):
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The directory in which to save the vocabulary.
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Returns:
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`Tuple(str)`: Paths to the files saved.
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"""
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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return (out_vocab_file,)
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
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if self.add_bos_token:
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bos_token_ids = [self.bos_token_id]
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else:
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bos_token_ids = []
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output = bos_token_ids + token_ids_0
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if token_ids_1 is not None:
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output = output + token_ids_1
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if self.add_eos_token:
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output = output + [self.eos_token_id]
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return output
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
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use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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"""
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eos = [self.eos_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0] |