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68a9ee7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization class for RishAI."""
import json
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.utils import add_end_docstrings, logging
logger = logging.get_logger(__name__)
@add_end_docstrings
class RishAITokenizer(PreTrainedTokenizerBase):
"""
Construct a RishAI tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer inherits from [`PreTrainedTokenizerBase`] which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*):
The token used for padding, for example when batching sequences of different lengths.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.
split_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the special tokens should be split during the encoding.
"""
vocab_files_names = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
}
pretrained_vocab_files_map = {
"vocab_file": {},
"merges_file": {},
}
max_model_input_sizes = {"default": 4096}
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file=None,
merges_file=None,
errors="replace",
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
pad_token=None,
clean_up_tokenization_spaces=False,
split_special_tokens=False,
**kwargs,
):
# Set default special tokens if not provided
if pad_token is None:
pad_token = "<|endoftext|>"
super().__init__(
errors=errors,
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
split_special_tokens=split_special_tokens,
**kwargs,
)
self.vocab_file = vocab_file
self.merges_file = merges_file
# Initialize vocabulary
self._vocab = {}
self._merges = []
self._bpe_ranks = {}
if vocab_file is not None and merges_file is not None:
self._load_vocab_and_merges(vocab_file, merges_file)
def _load_vocab_and_merges(self, vocab_file, merges_file):
"""Load vocabulary and merges from files."""
# Load vocabulary
with open(vocab_file, "r", encoding="utf-8") as f:
self._vocab = json.load(f)
# Load merges
with open(merges_file, "r", encoding="utf-8") as f:
self._merges = f.read().split("\n")
self._merges = [merge for merge in self._merges if merge.strip()]
# Build BPE ranks
self._bpe_ranks = {merge: i for i, merge in enumerate(self._merges)}
@property
def vocab_size(self) -> int:
"""Returns vocab size."""
return len(self._vocab)
def get_vocab(self) -> dict[str, int]:
"""Returns vocab as a dict."""
return dict(self._vocab)
def _tokenize(self, text: str, **kwargs) -> list[str]:
"""Tokenize a string."""
# Simple whitespace tokenization for now
# In a real implementation, this would use BPE
return text.split()
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) to an id using the vocab."""
return self._vocab.get(token, self._vocab.get(self.unk_token, 0))
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) to a token (str) using the vocab."""
for token, idx in self._vocab.items():
if idx == index:
return token
return self.unk_token
def convert_tokens_to_string(self, tokens: list[str]) -> str:
"""Converts a sequence of tokens (string) in a single string."""
# Simple detokenization - join with spaces
return " ".join(tokens)
def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str, str]:
"""Save the vocabulary and merges files to a directory."""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your tokenizer does not have the necessary information to save the vocabulary. "
"Please use a tokenizer that has been trained with the correct parameters."
)
vocab_file = (filename_prefix + "-" if filename_prefix else "") + "vocab.json"
merges_file = (filename_prefix + "-" if filename_prefix else "") + "merges.txt"
vocab_file_path = f"{save_directory}/{vocab_file}"
merges_file_path = f"{save_directory}/{merges_file}"
with open(vocab_file_path, "w", encoding="utf-8") as f:
json.dump(self._vocab, f, ensure_ascii=False, indent=2)
with open(merges_file_path, "w", encoding="utf-8") as f:
f.write("\n".join(self._merges))
return vocab_file_path, merges_file_path
@property
def can_save_slow_tokenizer(self) -> bool:
"""Check if the tokenizer can be saved."""
return self._vocab is not None and self._merges is not None
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