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"""Tokenization classes for ChatGLM.""" |
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from typing import List, Optional, Union |
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import os |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from transformers.utils import logging, PaddingStrategy |
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from transformers.tokenization_utils_base import EncodedInput, BatchEncoding |
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from typing import Dict |
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import sentencepiece as spm |
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import numpy as np |
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logger = logging.get_logger(__name__) |
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PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { |
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"THUDM/chatglm-6b": 2048, |
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} |
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class TextTokenizer: |
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def __init__(self, model_path): |
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self.sp = spm.SentencePieceProcessor() |
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self.sp.Load(model_path) |
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self.num_tokens = self.sp.vocab_size() |
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def encode(self, text): |
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return self.sp.EncodeAsIds(text) |
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def decode(self, ids: List[int]): |
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return self.sp.DecodeIds(ids) |
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def tokenize(self, text): |
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return self.sp.EncodeAsPieces(text) |
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def convert_tokens_to_string(self, tokens): |
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return self.sp.DecodePieces(tokens) |
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def convert_tokens_to_ids(self, tokens): |
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return [self.sp.PieceToId(token) for token in tokens] |
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def convert_token_to_id(self, token): |
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return self.sp.PieceToId(token) |
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def convert_id_to_token(self, idx): |
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return self.sp.IdToPiece(idx) |
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def __len__(self): |
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return self.num_tokens |
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class SPTokenizer: |
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def __init__( |
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self, |
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vocab_file, |
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num_image_tokens=20000, |
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max_blank_length=80, |
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byte_fallback=True, |
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): |
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assert vocab_file is not None |
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self.vocab_file = vocab_file |
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self.num_image_tokens = num_image_tokens |
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self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"] |
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self.max_blank_length = max_blank_length |
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self.byte_fallback = byte_fallback |
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self.text_tokenizer = TextTokenizer(vocab_file) |
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def _get_text_tokenizer(self): |
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return self.text_tokenizer |
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@staticmethod |
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def get_blank_token(length: int): |
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assert length >= 2 |
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return f"<|blank_{length}|>" |
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@staticmethod |
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def get_tab_token(): |
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return f"<|tab|>" |
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@property |
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def num_text_tokens(self): |
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return self.text_tokenizer.num_tokens |
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@property |
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def num_tokens(self): |
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return self.num_image_tokens + self.num_text_tokens |
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@staticmethod |
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def _encode_whitespaces(text: str, max_len: int = 80): |
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text = text.replace("\t", SPTokenizer.get_tab_token()) |
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for i in range(max_len, 1, -1): |
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text = text.replace(" " * i, SPTokenizer.get_blank_token(i)) |
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return text |
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def _preprocess(self, text: str, linebreak=True, whitespaces=True): |
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if linebreak: |
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text = text.replace("\n", "<n>") |
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if whitespaces: |
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text = self._encode_whitespaces(text, max_len=self.max_blank_length) |
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return text |
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def encode( |
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self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True |
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) -> List[int]: |
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""" |
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@param text: Text to encode. |
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@param linebreak: Whether to encode newline (\n) in text. |
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. |
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. |
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning. |
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""" |
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text = self._preprocess(text, linebreak, whitespaces) |
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if not add_dummy_prefix: |
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text = "<n>" + text |
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tmp = self._get_text_tokenizer().encode(text) |
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tokens = [x + self.num_image_tokens for x in tmp] |
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return tokens if add_dummy_prefix else tokens[2:] |
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def postprocess(self, text): |
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text = text.replace("<n>", "\n") |
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text = text.replace(SPTokenizer.get_tab_token(), "\t") |
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for i in range(2, self.max_blank_length + 1): |
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text = text.replace(self.get_blank_token(i), " " * i) |
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return text |
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def decode(self, text_ids: List[int]) -> str: |
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ids = [int(_id) - self.num_image_tokens for _id in text_ids] |
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ids = [_id for _id in ids if _id >= 0] |
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text = self._get_text_tokenizer().decode(ids) |
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text = self.postprocess(text) |
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return text |
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def decode_tokens(self, tokens: List[str]) -> str: |
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text = self._get_text_tokenizer().convert_tokens_to_string(tokens) |
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text = self.postprocess(text) |
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return text |
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def tokenize( |
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self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True |
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) -> List[str]: |
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""" |
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@param text: Text to encode. |
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@param linebreak: Whether to encode newline (\n) in text. |
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@param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. |
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@param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. |
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@param add_dummy_prefix: Whether to add dummy blank space in the beginning. |
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""" |
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text = self._preprocess(text, linebreak, whitespaces) |
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if not add_dummy_prefix: |
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text = "<n>" + text |
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tokens = self._get_text_tokenizer().tokenize(text) |
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return tokens if add_dummy_prefix else tokens[2:] |
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def __getitem__(self, x: Union[int, str]): |
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if isinstance(x, int): |
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if x < self.num_image_tokens: |
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return "<image_{}>".format(x) |
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else: |
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return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens) |
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elif isinstance(x, str): |
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if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit(): |
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return int(x[7:-1]) |
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else: |
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return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens |
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else: |
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raise ValueError("The key should be str or int.") |
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class ChatGLMTokenizer(PreTrainedTokenizer): |
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""" |
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Construct a ChatGLM 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_file": "ice_text.model"} |
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max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES |
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model_input_names = ["input_ids", "attention_mask", "position_ids"] |
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def __init__( |
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self, |
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vocab_file, |
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do_lower_case=False, |
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remove_space=False, |
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bos_token='<sop>', |
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eos_token='<eop>', |
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end_token='</s>', |
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mask_token='[MASK]', |
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gmask_token='[gMASK]', |
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padding_side="left", |
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pad_token="<pad>", |
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unk_token="<unk>", |
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num_image_tokens=20000, |
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**kwargs |
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) -> None: |
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super().__init__( |
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do_lower_case=do_lower_case, |
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remove_space=remove_space, |
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padding_side=padding_side, |
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bos_token=bos_token, |
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eos_token=eos_token, |
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end_token=end_token, |
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mask_token=mask_token, |
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gmask_token=gmask_token, |
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pad_token=pad_token, |
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unk_token=unk_token, |
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num_image_tokens=num_image_tokens, |
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**kwargs |
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) |
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self.do_lower_case = do_lower_case |
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self.remove_space = remove_space |
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self.vocab_file = vocab_file |
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self.bos_token = bos_token |
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self.eos_token = eos_token |
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self.end_token = end_token |
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self.mask_token = mask_token |
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self.gmask_token = gmask_token |
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self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens) |
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""" Initialisation """ |
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@property |
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def gmask_token_id(self) -> Optional[int]: |
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if self.gmask_token is None: |
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return None |
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return self.convert_tokens_to_ids(self.gmask_token) |
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@property |
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def end_token_id(self) -> Optional[int]: |
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""" |
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`Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been |
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set. |
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""" |
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if self.end_token is None: |
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return None |
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return self.convert_tokens_to_ids(self.end_token) |
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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_tokenizer.num_tokens |
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def get_vocab(self): |
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""" Returns vocab as a dict """ |
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vocab = {self._convert_id_to_token(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 preprocess_text(self, inputs): |
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if self.remove_space: |
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outputs = " ".join(inputs.strip().split()) |
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else: |
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outputs = inputs |
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if self.do_lower_case: |
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outputs = outputs.lower() |
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return outputs |
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def _tokenize(self, text, **kwargs): |
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""" Returns a tokenized string. """ |
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text = self.preprocess_text(text) |
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seq = self.sp_tokenizer.tokenize(text) |
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return seq |
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def convert_tokens_to_string(self, tokens: List[str]) -> str: |
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return self.sp_tokenizer.decode_tokens(tokens) |
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def _decode( |
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self, |
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token_ids: Union[int, List[int]], |
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**kwargs |
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) -> str: |
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if isinstance(token_ids, int): |
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token_ids = [token_ids] |
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if len(token_ids) == 0: |
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return "" |
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if self.pad_token_id in token_ids: |
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token_ids = list(filter((self.pad_token_id).__ne__, token_ids)) |
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return super()._decode(token_ids, **kwargs) |
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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_tokenizer[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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return self.sp_tokenizer[index] |
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def save_vocabulary(self, save_directory, filename_prefix=None): |
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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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filename_prefix (`str`, *optional*): |
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An optional prefix to add to the named of the saved files. |
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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 os.path.isdir(save_directory): |
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vocab_file = os.path.join( |
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save_directory, self.vocab_files_names["vocab_file"] |
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) |
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else: |
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vocab_file = save_directory |
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with open(self.vocab_file, 'rb') as fin: |
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proto_str = fin.read() |
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with open(vocab_file, "wb") as writer: |
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writer.write(proto_str) |
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return (vocab_file,) |
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def build_inputs_with_special_tokens( |
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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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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
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adding special tokens. A BERT sequence has the following format: |
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- single sequence: `[CLS] X [SEP]` |
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- pair of sequences: `[CLS] A [SEP] B [SEP]` |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs to which the special tokens will be added. |
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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 [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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gmask_id = self.sp_tokenizer[self.gmask_token] |
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eos_id = self.sp_tokenizer[self.eos_token] |
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token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]] |
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if token_ids_1 is not None: |
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token_ids_0 = token_ids_0 + token_ids_1 + [eos_id] |
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return token_ids_0 |
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def _pad( |
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self, |
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encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], |
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max_length: Optional[int] = None, |
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padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, |
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pad_to_multiple_of: Optional[int] = None, |
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return_attention_mask: Optional[bool] = None, |
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) -> dict: |
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""" |
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Pad encoded inputs (on left/right and up to predefined length or max length in the batch) |
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Args: |
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encoded_inputs: |
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Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). |
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max_length: maximum length of the returned list and optionally padding length (see below). |
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Will truncate by taking into account the special tokens. |
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padding_strategy: PaddingStrategy to use for padding. |
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- PaddingStrategy.LONGEST Pad to the longest sequence in the batch |
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- PaddingStrategy.MAX_LENGTH: Pad to the max length (default) |
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- PaddingStrategy.DO_NOT_PAD: Do not pad |
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The tokenizer padding sides are defined in self.padding_side: |
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- 'left': pads on the left of the sequences |
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- 'right': pads on the right of the sequences |
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pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. |
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This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability |
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`>= 7.5` (Volta). |
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return_attention_mask: |
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(optional) Set to False to avoid returning attention mask (default: set to model specifics) |
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""" |
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bos_token_id = self.sp_tokenizer[self.bos_token] |
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mask_token_id = self.sp_tokenizer[self.mask_token] |
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gmask_token_id = self.sp_tokenizer[self.gmask_token] |
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assert self.padding_side == "left" |
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required_input = encoded_inputs[self.model_input_names[0]] |
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seq_length = len(required_input) |
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if padding_strategy == PaddingStrategy.LONGEST: |
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max_length = len(required_input) |
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if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): |
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max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of |
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needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length |
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if max_length is not None: |
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if "attention_mask" not in encoded_inputs: |
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if bos_token_id in required_input: |
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context_length = required_input.index(bos_token_id) |
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else: |
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context_length = seq_length |
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attention_mask = np.ones((1, seq_length, seq_length)) |
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attention_mask = np.tril(attention_mask) |
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attention_mask[:, :, :context_length] = 1 |
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attention_mask = np.bool_(attention_mask < 0.5) |
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encoded_inputs["attention_mask"] = attention_mask |
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if "position_ids" not in encoded_inputs: |
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if bos_token_id in required_input: |
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context_length = required_input.index(bos_token_id) |
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else: |
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context_length = seq_length |
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position_ids = np.arange(seq_length, dtype=np.int64) |
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mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id |
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if mask_token in required_input: |
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mask_position = required_input.index(mask_token) |
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position_ids[context_length:] = mask_position |
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block_position_ids = np.concatenate( |
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[np.zeros(context_length, dtype=np.int64), |
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np.arange(1, seq_length - context_length + 1, dtype=np.int64)]) |
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encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0) |
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if needs_to_be_padded: |
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difference = max_length - len(required_input) |
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if "attention_mask" in encoded_inputs: |
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encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"], |
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pad_width=[(0, 0), (difference, 0), (difference, 0)], |
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mode='constant', constant_values=True) |
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if "token_type_ids" in encoded_inputs: |
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encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ |
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"token_type_ids" |
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] |
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if "special_tokens_mask" in encoded_inputs: |
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encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] |
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if "position_ids" in encoded_inputs: |
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encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"], |
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pad_width=[(0, 0), (difference, 0)]) |
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encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input |
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return encoded_inputs |
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