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import regex as re |
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import base64 |
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import os |
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import json |
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import tiktoken |
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from torch import TensorType |
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from typing import List, Optional, Union, Dict, Any |
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from transformers 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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class ChatGLM4Tokenizer(PreTrainedTokenizer): |
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vocab_files_names = {"vocab_file": "tokenizer.model"} |
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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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padding_side="left", |
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clean_up_tokenization_spaces=False, |
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encode_special_tokens=False, |
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**kwargs |
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): |
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self.name = "GLM4Tokenizer" |
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self.vocab_file = vocab_file |
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pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" |
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self.pat_str = re.compile(pat_str) |
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self.encode_special_tokens = encode_special_tokens |
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mergeable_ranks = {} |
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with open(vocab_file) as f: |
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for line in f: |
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token, rank = line.strip().split() |
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rank = int(rank) |
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token = base64.b64decode(token) |
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mergeable_ranks[token] = rank |
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self.mergeable_ranks = mergeable_ranks |
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self.tokenizer = tiktoken.Encoding( |
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name="my_tokenizer", |
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pat_str=pat_str, |
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mergeable_ranks=mergeable_ranks, |
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special_tokens={} |
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) |
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self.decoder = {rank: token for token, rank in mergeable_ranks.items()} |
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self.n_words = len(self.decoder) |
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super().__init__( |
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padding_side=padding_side, |
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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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@property |
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def vocab_size(self): |
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return self.n_words |
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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 convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str: |
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""" |
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Converts a sequence of tokens in a single string. |
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""" |
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text = "" |
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temp = b"" |
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for t in tokens: |
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if isinstance(t, str): |
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if temp: |
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text += temp.decode("utf-8", errors="replace") |
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temp = b"" |
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text += t |
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elif isinstance(t, bytes): |
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temp += t |
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else: |
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raise TypeError("token should only be of type types or str") |
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if temp: |
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text += temp.decode("utf-8", errors="replace") |
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return text |
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def _tokenize(self, text, **kwargs): |
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tokens = [] |
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ids = self.tokenizer.encode(text) |
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for t in ids: |
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tokens.append(self.decoder[t]) |
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return tokens |
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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.mergeable_ranks[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.decoder.get(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 get_prefix_tokens(self): |
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prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")] |
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return prefix_tokens |
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def build_single_message(self, role, metadata, message, tokenize=True): |
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assert role in ["system", "user", "assistant", "observation"], role |
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if tokenize: |
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role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n", |
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disallowed_special=()) |
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message_tokens = self.tokenizer.encode(message, disallowed_special=()) |
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tokens = role_tokens + message_tokens |
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return tokens |
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else: |
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return str(f"<|{role}|>{metadata}\n{message}") |
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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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prefix_tokens = self.get_prefix_tokens() |
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token_ids_0 = prefix_tokens + token_ids_0 |
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if token_ids_1 is not None: |
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token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")] |
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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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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 "attention_mask" not in encoded_inputs: |
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encoded_inputs["attention_mask"] = [1] * seq_length |
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if "position_ids" not in encoded_inputs: |
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encoded_inputs["position_ids"] = list(range(seq_length)) |
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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"] = [0] * difference + encoded_inputs["attention_mask"] |
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if "position_ids" in encoded_inputs: |
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encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] |
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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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