mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-17 00:23:14 +00:00
Brandon Thomas
143 lines
4.6 KiB
Python
143 lines
4.6 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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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from typing import Optional
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from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
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__all__ = ['CharTokenizer']
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class CharTokenizer(TokenizerSpec):
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"Tokenizes each character"
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def __init__(
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self,
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vocab_file: str,
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mask_token: Optional[str] = None,
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bos_token: Optional[str] = None,
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eos_token: Optional[str] = None,
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pad_token: Optional[str] = None,
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sep_token: Optional[str] = None,
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cls_token: Optional[str] = None,
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unk_token: Optional[str] = None,
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):
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"""
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Args:
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vocab_file: path to file with vocabulary which consists
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of characters separated by \n
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mask_token: mask token
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bos_token: the beginning of sequence token
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eos_token: the end of sequence token. Usually equal to sep_token
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pad_token: token to use for padding
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sep_token: token used for separating sequences
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cls_token: class token. Usually equal to bos_token
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unk_token: token to use for unknown tokens
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"""
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vocab_list = open(vocab_file, "r").readlines()
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self.vocab = {vocab_list[i].strip(): i for i in range(len(vocab_list))}
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special_tokens_dict = {}
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if unk_token:
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special_tokens_dict["unk_token"] = unk_token
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if sep_token:
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special_tokens_dict["sep_token"] = sep_token
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if mask_token:
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special_tokens_dict["mask_token"] = mask_token
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if bos_token:
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special_tokens_dict["bos_token"] = bos_token
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if eos_token:
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special_tokens_dict["eos_token"] = eos_token
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if pad_token:
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special_tokens_dict["pad_token"] = pad_token
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if cls_token:
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special_tokens_dict["cls_token"] = cls_token
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self.add_special_tokens(special_tokens_dict)
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self.inv_vocab = {v: k for k, v in self.vocab.items()}
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self.vocab_size = len(self.vocab)
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self.special_tokens = self.tokens_to_ids(special_tokens_dict.values())
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def add_special_tokens(self, special_tokens_dict: dict) -> int:
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"""
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Adds a dictionary of special tokens (eos, pad, cls...).
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If special tokens are NOT in the vocabulary, they are added
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to it (indexed starting from the last index of the current vocabulary).
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Args:
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special_tokens_dict: dict of special tokens
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"""
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for token in special_tokens_dict:
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token_str = special_tokens_dict[token]
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if token_str not in self.vocab:
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self.vocab[token_str] = len(self.vocab)
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setattr(self, token, token_str)
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def text_to_tokens(self, text):
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token_candidates = [char for char in text]
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tokens = []
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for token in token_candidates:
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if token in self.vocab:
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tokens.append(token)
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else:
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tokens.append(self.unk_token)
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return tokens
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def tokens_to_text(self, tokens):
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return self.ids_to_text(self.tokens_to_ids(tokens))
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def text_to_ids(self, text):
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return [self.vocab[token] for token in self.text_to_tokens(text)]
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def ids_to_text(self, ids):
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ids_ = [id_ for id_ in ids if id_ not in self.special_tokens]
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return "".join(self.ids_to_tokens(ids_))
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def tokens_to_ids(self, tokens):
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return [self.vocab[token] for token in tokens]
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def token_to_id(self, token):
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return self.vocab[token]
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def ids_to_tokens(self, ids):
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return [self.inv_vocab[id] for id in ids]
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@property
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def pad_id(self):
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return self.vocab[self.pad_token]
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@property
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def bos_id(self):
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return self.vocab[self.bos_token]
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@property
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def eos_id(self):
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return self.vocab[self.eos_token]
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@property
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def unk_id(self):
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return self.vocab[self.unk_token]
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@property
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def mask_id(self):
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return self.vocab[self.mask_token]
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@property
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def sep_id(self):
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return self.vocab[self.sep_token]
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@property
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def cls_id(self):
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return self.vocab[self.cls_token]
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