# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
T5Tokenizer
"""
import numpy as np
from mindspore.dataset.text.transforms import Implementation
from tokenizers import Tokenizer
from mindnlp.abc import PreTrainedTokenizer
from mindnlp.models.t5.t5_config import T5_SUPPORT_LIST
from mindnlp.configs import HF_TOKENIZER_CONFIG_URL_BASE
PRETRAINED_VOCAB_MAP = {
model: HF_TOKENIZER_CONFIG_URL_BASE.format(model) for model in T5_SUPPORT_LIST
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"t5-small": 512,
"t5-base": 512,
"t5-large": 512,
"t5-3b": 512,
"t5-11b": 512,
}
[docs]class T5Tokenizer(PreTrainedTokenizer):
"""
Tokenizer used for T5 text process.
Args:
vocab (Vocab): Vocabulary used to look up words.
return_token (bool): Whether to return token. If True: return tokens. False: return ids. Default: True.
Examples:
>>> from mindspore.dataset import text
>>> from mindnlp.transforms import T5Tokenizer
>>> text = "Believing that faith can triumph over everything is in itself the greatest belief"
>>> tokenizer = T5Tokenizer.from_pretrained('t5-base')
>>> tokens = tokenizer.encode(text)
"""
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_vocab_map = PRETRAINED_VOCAB_MAP
def __init__(self, vocab: str, **kwargs):
super().__init__()
return_token = kwargs.pop('return_token', False)
if isinstance(vocab, str):
self.tokenizer = Tokenizer.from_file(vocab)
else:
raise ValueError(f'only support string, but got {vocab}')
self.return_token = return_token
self.implementation = Implementation.PY
def __call__(self, text_input):
"""
Call method for input conversion for eager mode with C++ implementation.
"""
if isinstance(text_input, str):
text_input = np.array(text_input)
elif not isinstance(text_input, np.ndarray):
raise TypeError(
f"Input should be a text line in 1-D NumPy format, got {type(text_input)}.")
return super().__call__(text_input)
[docs] def execute_py(self, text_input):
"""
Execute method.
"""
return self._execute_py(text_input)
def _execute_py(self, text_input):
"""
Execute method.
"""
text_input = self._convert_to_unicode(text_input)
tokens = self.tokenizer.encode(text_input)
if self.return_token is True:
return np.array(tokens.tokens)
return np.array(tokens.ids)
def _convert_to_unicode(self, text_input):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if isinstance(text_input, str):
return text_input
if isinstance(text_input, bytes):
return text_input.decode("utf-8", "ignore")
if isinstance(text_input, np.ndarray):
if text_input.dtype.type is np.bytes_:
text_input = np.char.decode(text_input, "utf-8")
return str(text_input)
raise ValueError(f"Unsupported string type: {type(text_input)}, {text_input.dtype}")