Always truncate argument in the encode method
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@ -232,6 +232,23 @@ class CommonTestCases:
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assert len(truncated_sequence) == total_length - 2
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assert truncated_sequence == tokenizer.add_special_tokens_single_sequence(sequence[:-2])
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def test_always_truncate(self):
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tokenizer = self.get_tokenizer()
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seq_0 = "This is a sentence to be encoded."
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length_single_sequence = len(tokenizer.encode(seq_0))
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length = len(tokenizer.encode(seq_0, seq_0, add_special_tokens=True))
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not_truncated = tokenizer.encode(seq_0, seq_0, add_special_tokens=True, max_length=length_single_sequence)
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truncated = tokenizer.encode(
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seq_0, seq_0,
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max_length=length_single_sequence,
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add_special_tokens=True,
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always_truncate=True
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)
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assert truncated == not_truncated[:length_single_sequence - length]
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def test_maximum_encoding_length_pair_input(self):
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tokenizer = self.get_tokenizer()
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@ -693,14 +693,15 @@ class PreTrainedTokenizer(object):
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raise NotImplementedError
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def encode(self,
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text,
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text_pair=None,
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add_special_tokens=False,
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max_length=None,
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stride=0,
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truncate_first_sequence=True,
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return_tensors=None,
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**kwargs):
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text,
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text_pair=None,
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add_special_tokens=False,
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max_length=None,
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stride=0,
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truncate_first_sequence=True,
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return_tensors=None,
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always_truncate=False,
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**kwargs):
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"""
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Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
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@ -721,6 +722,8 @@ class PreTrainedTokenizer(object):
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from the main sequence returned. The value of this argument defined the number of additional tokens.
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truncate_first_sequence: if there is a specified max_length, this flag will choose which sequence
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will be truncated.
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always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
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sequences may be lost in the process.
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return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
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or PyTorch torch.Tensor instead of a list of python integers.
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**kwargs: passed to the `self.tokenize()` method
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@ -732,6 +735,7 @@ class PreTrainedTokenizer(object):
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stride=stride,
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truncate_first_sequence=truncate_first_sequence,
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return_tensors=return_tensors,
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always_truncate=always_truncate,
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**kwargs)
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return encoded_inputs["input_ids"]
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@ -744,6 +748,7 @@ class PreTrainedTokenizer(object):
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stride=0,
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truncate_first_sequence=True,
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return_tensors=None,
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always_truncate=False,
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**kwargs):
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"""
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Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
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@ -764,6 +769,8 @@ class PreTrainedTokenizer(object):
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from the main sequence returned. The value of this argument defined the number of additional tokens.
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truncate_first_sequence: if there is a specified max_length, this flag will choose which sequence
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will be truncated.
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always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
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sequences may be lost in the process.
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return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
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or PyTorch torch.Tensor instead of a list of python integers.
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**kwargs: passed to the `self.tokenize()` method
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@ -788,11 +795,12 @@ class PreTrainedTokenizer(object):
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add_special_tokens=add_special_tokens,
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stride=stride,
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truncate_first_sequence=truncate_first_sequence,
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always_truncate=always_truncate,
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return_tensors=return_tensors)
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def prepare_for_model(self, ids, pair_ids=None, max_length=None, add_special_tokens=False, stride=0,
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truncate_first_sequence=True, return_tensors=None):
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truncate_first_sequence=True, always_truncate=False, return_tensors=None):
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"""
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Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
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It adds special tokens, truncates
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@ -812,6 +820,8 @@ class PreTrainedTokenizer(object):
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truncate_first_sequence: if set to `True` and an optional second list of input ids is provided,
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alongside a specified `max_length`, will truncate the first sequence if the total size is superior
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than the specified `max_length`. If set to `False`, will truncate the second sequence instead.
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always_truncate: if set to True, will always truncate the sequences when overflowing, even if one of the
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sequences may be lost in the process.
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return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
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or PyTorch torch.Tensor instead of a list of python integers.
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@ -826,9 +836,14 @@ class PreTrainedTokenizer(object):
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if max_length:
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n_added_tokens = self.num_added_tokens(pair=pair) if add_special_tokens else 0
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if pair and n_added_tokens + (len_pair_ids if truncate_first_sequence else len_ids) >= max_length:
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logger.warning(
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"You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length."
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"This pair of sequences will not be truncated.")
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if always_truncate:
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logger.warning(
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"You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length. "
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"This pair of sequences will be truncated but one of the sequences may not be present in the resulting list of ids.")
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else:
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logger.warning(
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"You supplied a pair of sequence in which the sequence that will not be truncated is longer than the maximum specified length. "
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"This pair of sequences will not be truncated.")
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else:
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if n_added_tokens + len_ids + len_pair_ids > max_length:
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if truncate_first_sequence or not pair:
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@ -860,6 +875,10 @@ class PreTrainedTokenizer(object):
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encoded_inputs["input_ids"] = sequence
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encoded_inputs["token_type_ids"] = token_type_ids
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if always_truncate and len(encoded_inputs["input_ids"]) > max_length:
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encoded_inputs["input_ids"] = encoded_inputs["input_ids"][:max_length]
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encoded_inputs["token_type_ids"] = encoded_inputs["token_type_ids"][:max_length]
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return encoded_inputs
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def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1):
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