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# Copyright 2023 The TensorFlow Authors. 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.
"""Utilities for pre-processing classification data."""
from absl import logging
from official.legacy.xlnet import data_utils
SEG_ID_A = 0
SEG_ID_B = 1
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
battches could cause silent errors.
"""
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def convert_single_example(example_index, example, label_list, max_seq_length,
tokenize_fn, use_bert_format):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * max_seq_length,
input_mask=[1] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
if label_list is not None:
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenize_fn(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenize_fn(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for two [SEP] & one [CLS] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for one [SEP] & one [CLS] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:max_seq_length - 2]
tokens = []
segment_ids = []
for token in tokens_a:
tokens.append(token)
segment_ids.append(SEG_ID_A)
tokens.append(data_utils.SEP_ID)
segment_ids.append(SEG_ID_A)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(SEG_ID_B)
tokens.append(data_utils.SEP_ID)
segment_ids.append(SEG_ID_B)
if use_bert_format:
tokens.insert(0, data_utils.CLS_ID)
segment_ids.insert(0, data_utils.SEG_ID_CLS)
else:
tokens.append(data_utils.CLS_ID)
segment_ids.append(data_utils.SEG_ID_CLS)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
if len(input_ids) < max_seq_length:
delta_len = max_seq_length - len(input_ids)
if use_bert_format:
input_ids = input_ids + [0] * delta_len
input_mask = input_mask + [1] * delta_len
segment_ids = segment_ids + [data_utils.SEG_ID_PAD] * delta_len
else:
input_ids = [0] * delta_len + input_ids
input_mask = [1] * delta_len + input_mask
segment_ids = [data_utils.SEG_ID_PAD] * delta_len + segment_ids
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if label_list is not None:
label_id = label_map[example.label]
else:
label_id = example.label
if example_index < 5:
logging.info("*** Example ***")
logging.info("guid: %s", (example.guid))
logging.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
logging.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
logging.info("label: %s (id = %d)", example.label, label_id)
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id)
return feature
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