<reponame>stanford-oval/trade-dst
import logging

logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
                    datefmt='%m/%d/%Y %H:%M:%S',
                    level=logging.INFO)
logger = logging.getLogger(__name__)

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()

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, guid, input_ids, input_mask, segment_ids, sub_word_masks, label_id=None):
        self.guid = guid
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.sub_word_masks = sub_word_masks
        self.label_id = label_id

def convert_examples_to_features(examples, tokenizer, label_list=None, max_seq_length=128):
    """Loads a data file into a list of `InputBatch`s."""

    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example)

        tokens_b = None
        if None:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambigiously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 1)

        # mask to keep track of the beginning of each sub-word piece
        sub_word_masks = [0 if t.startswith('##') else 1 for t in tokens]

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        zero_padding = [0] * (max_seq_length - len(input_ids))
        one_padding = [1] * (max_seq_length - len(input_ids))
        input_ids += zero_padding
        input_mask += zero_padding
        segment_ids += zero_padding
        sub_word_masks += one_padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(sub_word_masks) == max_seq_length

        # TODO
        if ex_index < 0:
            logger.info("*** Example ***")
            logger.info("ex_index: %s" % (ex_index))
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))

        features.append(
            InputFeatures(
                          guid=ex_index,
                          input_ids=input_ids,
                          input_mask=input_mask,
                          segment_ids=segment_ids,
                          sub_word_masks=sub_word_masks,
            ))

    return features
import logging

logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
                    datefmt='%m/%d/%Y %H:%M:%S',
                    level=logging.INFO)
logger = logging.getLogger(__name__)

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()

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, guid, input_ids, input_mask, segment_ids, sub_word_masks, label_id=None):
        self.guid = guid
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.sub_word_masks = sub_word_masks
        self.label_id = label_id

def convert_examples_to_features(examples, tokenizer, label_list=None, max_seq_length=128):
    """Loads a data file into a list of `InputBatch`s."""

    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example)

        tokens_b = None
        if None:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambigiously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 1)

        # mask to keep track of the beginning of each sub-word piece
        sub_word_masks = [0 if t.startswith('##') else 1 for t in tokens]

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        zero_padding = [0] * (max_seq_length - len(input_ids))
        one_padding = [1] * (max_seq_length - len(input_ids))
        input_ids += zero_padding
        input_mask += zero_padding
        segment_ids += zero_padding
        sub_word_masks += one_padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(sub_word_masks) == max_seq_length

        # TODO
        if ex_index < 0:
            logger.info("*** Example ***")
            logger.info("ex_index: %s" % (ex_index))
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))

        features.append(
            InputFeatures(
                          guid=ex_index,
                          input_ids=input_ids,
                          input_mask=input_mask,
                          segment_ids=segment_ids,
                          sub_word_masks=sub_word_masks,
            ))

    return features
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. A single set of features of data. Loads a data file into a list of `InputBatch`s. # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" # Account for [CLS] and [SEP] with "- 2" # The convention in BERT is: # (a) For sequence pairs: #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1 # (b) For single sequences: #  tokens:   [CLS] the dog is hairy . [SEP] #  type_ids: 0   0   0   0  0     0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambigiously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. # mask to keep track of the beginning of each sub-word piece #') else 1 for t in tokens] # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # Zero-pad up to the sequence length. # TODO