let encode accept tensor inputs

pytorch_transformers/__init__.py

@@ -163,10 +163,5 @@ if _tf_available and _torch_available:
 # Files and general utilities
 from .file_utils import (PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
                          cached_path, add_start_docstrings, add_end_docstrings,
-                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME)
-
-def is_torch_available():
-    return _torch_available
-
-def is_tf_available():
-    return _tf_available
+                         WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
+                         is_tf_available, is_torch_available)
\ No newline at end of file

pytorch_transformers/file_utils.py

@@ -23,6 +23,20 @@ from botocore.exceptions import ClientError
 import requests
 from tqdm import tqdm
 
+try:
+    import tensorflow as tf
+    assert int(tf.__version__[0]) >= 2
+    _tf_available = True  # pylint: disable=invalid-name
+except (ImportError, AssertionError):
+    _tf_available = False  # pylint: disable=invalid-name
+
+try:
+    import torch
+    _torch_available = True  # pylint: disable=invalid-name
+except ImportError:
+    _torch_available = False  # pylint: disable=invalid-name
+
+
 try:
     from torch.hub import _get_torch_home
     torch_cache_home = _get_torch_home()
@@ -55,6 +69,12 @@ CONFIG_NAME = "config.json"
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
+def is_torch_available():
+    return _torch_available
+
+def is_tf_available():
+    return _tf_available
+
 if not six.PY2:
     def add_start_docstrings(*docstr):
         def docstring_decorator(fn):

pytorch_transformers/tokenization_utils.py

@@ -23,7 +23,10 @@ import six
 import copy
 from io import open
 
-from .file_utils import cached_path
+from .file_utils import cached_path, is_tf_available
+
+if is_tf_available():
+    import tensorflow as tf
 
 logger = logging.getLogger(__name__)
 
@@ -686,19 +689,32 @@ class PreTrainedTokenizer(object):
                 to their model.
             **kwargs: passed to the `self.tokenize()` method
         """
+        if is_tf_available():
+            is_tf_tensor = False
+            if isinstance(text, tf.Tensor):
+                text = text.numpy()
+                is_tf_tensor = True
+            if isinstance(text, bytes):
+                text = text.decode('utf-8')
+
         if text_pair is None:
             if add_special_tokens:
-                return self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text, **kwargs)))
+                output = self.add_special_tokens_single_sentence(self.convert_tokens_to_ids(self.tokenize(text, **kwargs)))
+            else:
+                output = self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
         else:
-                return self.convert_tokens_to_ids(self.tokenize(text, **kwargs))
-
             first_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text, **kwargs)]
             second_sentence_tokens = [self._convert_token_to_id(token) for token in self.tokenize(text_pair, **kwargs)]
 
             if add_special_tokens:
-            return self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
+                output = self.add_special_tokens_sentences_pair(first_sentence_tokens, second_sentence_tokens)
             else:
-            return first_sentence_tokens, second_sentence_tokens
+                output = first_sentence_tokens, second_sentence_tokens
+
+        if is_tf_available() and is_tf_tensor:
+            output = tf.constant(output)
+
+        return output
 
     def add_special_tokens_single_sentence(self, token_ids):
         logger.warning("This tokenizer does not make use of special tokens. The sequence has been returned with no modification.")