let encode accept tensor inputs
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@ -163,10 +163,5 @@ if _tf_available and _torch_available:
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# Files and general utilities
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from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
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cached_path, add_start_docstrings, add_end_docstrings,
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WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME)
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def is_torch_available():
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return _torch_available
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def is_tf_available():
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return _tf_available
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WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
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is_tf_available, is_torch_available)
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@ -23,6 +23,20 @@ from botocore.exceptions import ClientError
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import requests
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from tqdm import tqdm
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try:
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import tensorflow as tf
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assert int(tf.__version__[0]) >= 2
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_tf_available = True # pylint: disable=invalid-name
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except (ImportError, AssertionError):
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_tf_available = False # pylint: disable=invalid-name
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try:
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import torch
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_torch_available = True # pylint: disable=invalid-name
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except ImportError:
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_torch_available = False # pylint: disable=invalid-name
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try:
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from torch.hub import _get_torch_home
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torch_cache_home = _get_torch_home()
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@ -55,6 +69,12 @@ CONFIG_NAME = "config.json"
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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def is_torch_available():
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return _torch_available
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def is_tf_available():
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return _tf_available
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if not six.PY2:
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def add_start_docstrings(*docstr):
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def docstring_decorator(fn):
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@ -23,7 +23,10 @@ import six
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import copy
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from io import open
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from .file_utils import cached_path
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from .file_utils import cached_path, is_tf_available
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if is_tf_available():
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import tensorflow as tf
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logger = logging.getLogger(__name__)
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@ -686,19 +689,32 @@ class PreTrainedTokenizer(object):
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to their model.
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**kwargs: passed to the `self.tokenize()` method
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"""
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if is_tf_available():
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is_tf_tensor = False
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if isinstance(text, tf.Tensor):
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text = text.numpy()
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is_tf_tensor = True
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if isinstance(text, bytes):
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text = text.decode('utf-8')
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if text_pair is None:
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if add_special_tokens:
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return self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text, **kwargs)))
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output = self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text, **kwargs)))
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else:
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return self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
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first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
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second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
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if add_special_tokens:
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return self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
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output = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
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else:
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return first_sentence_tokens, second_sentence_tokens
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first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
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second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
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if add_special_tokens:
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output = self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
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else:
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output = first_sentence_tokens, second_sentence_tokens
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if is_tf_available() and is_tf_tensor:
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output = tf.constant(output)
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return output
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def add_special_tokens_single_sentence(self, token_ids):
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logger.warning("This tokenizer does not make use of special tokens. The sequence has been returned with no modification.")
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