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""" |
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NeuralQuantum NQLM Tokenizer for Hugging Face Transformers |
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""" |
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import json |
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from typing import List, Optional, Union |
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from transformers import PreTrainedTokenizer |
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class NeuralQuantumTokenizer(PreTrainedTokenizer): |
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"""Tokenizer for NeuralQuantum NQLM model""" |
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def __init__( |
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self, |
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vocab_file=None, |
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merges_file=None, |
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tokenizer_file=None, |
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unk_token="<|endoftext|>", |
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bos_token="<|endoftext|>", |
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eos_token="<|endoftext|>", |
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pad_token="<|endoftext|>", |
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quantum_token="<|quantum|>", |
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classical_token="<|classical|>", |
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add_prefix_space=False, |
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**kwargs |
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): |
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vocab = { |
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"<|endoftext|>": 0, |
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"<|quantum|>": 1, |
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"<|classical|>": 2, |
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} |
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for i, char in enumerate("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 .,!?;:'\"-()[]{}"): |
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vocab[char] = i + 3 |
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self._vocab = vocab |
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self._ids_to_tokens = {v: k for k, v in vocab.items()} |
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super().__init__( |
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unk_token=unk_token, |
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bos_token=bos_token, |
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eos_token=eos_token, |
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pad_token=pad_token, |
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add_prefix_space=add_prefix_space, |
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**kwargs |
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) |
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self.quantum_token = quantum_token |
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self.classical_token = classical_token |
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@property |
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def vocab_size(self): |
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return len(self._vocab) |
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def get_vocab(self): |
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return dict(self._vocab) |
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def _tokenize(self, text): |
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"""Basic tokenization - split by whitespace and characters""" |
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tokens = [] |
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current_token = "" |
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for char in text: |
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if char.isspace(): |
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if current_token: |
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tokens.append(current_token) |
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current_token = "" |
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else: |
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current_token += char |
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if current_token: |
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tokens.append(current_token) |
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return tokens |
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def _convert_token_to_id(self, token): |
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"""Convert token to ID""" |
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return self._vocab.get(token, self._vocab[self.unk_token]) |
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def _convert_id_to_token(self, index): |
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"""Convert ID to token""" |
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return self._ids_to_tokens.get(index, self.unk_token) |
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def convert_tokens_to_string(self, tokens): |
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"""Convert tokens back to string""" |
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return " ".join(tokens) |
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def save_vocabulary(self, save_directory, filename_prefix=None): |
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"""Save vocabulary to files""" |
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vocab_file = f"{filename_prefix}-vocab.json" if filename_prefix else "vocab.json" |
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vocab_path = f"{save_directory}/{vocab_file}" |
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with open(vocab_path, 'w') as f: |
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json.dump(self._vocab, f, indent=2) |
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return (vocab_path,) |
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def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): |
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"""Build input with special tokens""" |
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if token_ids_1 is None: |
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return token_ids_0 + [self.eos_token_id] |
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return token_ids_0 + token_ids_1 + [self.eos_token_id] |
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def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): |
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"""Get special tokens mask""" |
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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 not None: |
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return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] |
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return [1] + ([0] * len(token_ids_0)) + [1] |
