add auto models and auto tokenizer

pytorch_transformers/tokenization_auto.py

@@ -0,0 +1,100 @@
+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# 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.
+""" Auto Model class. """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+
+from .tokenization_bert import BertTokenizer
+from .tokenization_openai import OpenAIGPTTokenizer
+from .tokenization_gpt2 import GPT2Tokenizer
+from .tokenization_transfo_xl import TransfoXLTokenizer
+from .tokenization_xlnet import XLNetTokenizer
+from .tokenization_xlm import XLMTokenizer
+
+logger = logging.getLogger(__name__)
+
+class AutoTokenizer(object):
+    r""":class:`~pytorch_transformers.AutoTokenizer` is a generic tokenizer class
+        that will be instantiated as one of the tokenizer classes of the library
+        when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)`
+        class method.
+
+        The `from_pretrained()` method take care of returning the correct tokenizer class instance
+        using pattern matching on the `pretrained_model_name_or_path` string.
+
+        The tokenizer class to instantiate is selected as the first pattern matching
+        in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `bert`: BertTokenizer (Bert model)
+            - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
+            - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
+            - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
+            - contains `xlnet`: XLNetTokenizer (XLNet model)
+            - contains `xlm`: XLMTokenizer (XLM model)
+
+        This class cannot be instantiated using `__init__()` (throw an error).
+    """
+    def __init__(self):
+        raise EnvironmentError("AutoTokenizer is designed to be instantiated "
+            "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.")
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
+        r""" Instantiate a one of the tokenizer classes of the library
+        from a pre-trained model vocabulary.
+
+        The tokenizer class to instantiate is selected as the first pattern matching
+        in the `pretrained_model_name_or_path` string (in the following order):
+            - contains `bert`: BertTokenizer (Bert model)
+            - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
+            - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
+            - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
+            - contains `xlnet`: XLNetTokenizer (XLNet model)
+            - contains `xlm`: XLMTokenizer (XLM model)
+
+        Params:
+            **pretrained_model_name_or_path**: either:
+                - a string with the `shortcut name` of a pre-trained model configuration to load from cache
+                    or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
+                - a path to a `directory` containing a configuration file saved
+                    using the `save_pretrained(save_directory)` method.
+                - a path or url to a saved configuration `file`.
+            **cache_dir**: (`optional`) string:
+                Path to a directory in which a downloaded pre-trained model
+                configuration should be cached if the standard cache should not be used.
+
+        Examples::
+
+            >>> config = AutoTokenizer.from_pretrained('bert-base-uncased')    # Download vocabulary from S3 and cache.
+            >>> config = AutoTokenizer.from_pretrained('./test/bert_saved_model/')  # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
+
+        """
+        if 'bert' in pretrained_model_name_or_path:
+            return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        elif 'openai-gpt' in pretrained_model_name_or_path:
+            return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        elif 'gpt2' in pretrained_model_name_or_path:
+            return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        elif 'transfo-xl' in pretrained_model_name_or_path:
+            return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        elif 'xlnet' in pretrained_model_name_or_path:
+            return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+        elif 'xlm' in pretrained_model_name_or_path:
+            return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
+
+        raise ValueError("Unrecognized model identifier in {}. Should contains one of "
+                         "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
+                         "'xlm'".format(pretrained_model_name_or_path))