modeling_auto_bert_ts.py

# 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
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""" Auto Model class. """


import warnings
from collections import OrderedDict

from transformers.configuration_auto import (
    AutoConfig,
    BertConfig,
    replace_list_option_in_docstrings,
)

from transformers.file_utils import(
    add_start_docstrings,
)

from transformers.configuration_utils import PretrainedConfig

from transformers.utils import logging

from modeling_bert_ts import (
    BertForQuestionAnswering
)


logger = logging.get_logger(__name__)


MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
    [
        (BertConfig, BertForQuestionAnswering),
    ]
)

AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
        The model class to instantiate is selected based on the :obj:`model_type` property of the config object (either
        passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's missing,
        by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`:
        List options
        The model is set in evaluation mode by default using ``model.eval()`` (so for instance, dropout modules are
        deactivated). To train the model, you should first set it back in training mode with ``model.train()``
        Args:
            pretrained_model_name_or_path:
                Can be either:
                    - A string with the `shortcut name` of a pretrained model to load from cache or download, e.g.,
                      ``bert-base-uncased``.
                    - A string with the `identifier name` of a pretrained model that was user-uploaded to our S3, e.g.,
                      ``dbmdz/bert-base-german-cased``.
                    - A path to a `directory` containing model weights saved using
                      :func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
                    - A path or url to a `tensorflow index checkpoint file` (e.g, ``./tf_model/model.ckpt.index``). In
                      this case, ``from_tf`` should be set to :obj:`True` and a configuration object should be provided
                      as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in
                      a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
            model_args (additional positional arguments, `optional`):
                Will be passed along to the underlying model ``__init__()`` method.
            config (:class:`~transformers.PretrainedConfig`, `optional`):
                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                be automatically loaded when:
                    - The model is a model provided by the library (loaded with the `shortcut name` string of a
                      pretrained model).
                    - The model was saved using :meth:`~transformers.PreTrainedModel.save_pretrained` and is reloaded
                      by supplying the save directory.
                    - The model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
                      configuration JSON file named `config.json` is found in the directory.
            state_dict (`Dict[str, torch.Tensor]`, `optional`):
                A state dictionary to use instead of a state dictionary loaded from saved weights file.
                This option can be used if you want to create a model from a pretrained configuration but load your own
                weights. In this case though, you should check if using
                :func:`~transformers.PreTrainedModel.save_pretrained` and
                :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Load the model weights from a TensorFlow checkpoint save file (see docstring of
                ``pretrained_model_name_or_path`` argument).
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (:obj:`Dict[str, str], `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to only look at local files (e.g., not try downloading the model).
            use_cdn(:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to use Cloudfront (a Content Delivery Network, or CDN) when searching for the model on
                our S3 (faster). Should be set to :obj:`False` for checkpoints larger than 20GB.
            kwargs (additional keyword arguments, `optional`):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                :obj:`output_attentions=True`). Behaves differently depending on whether a ``config`` is provided or
                automatically loaded:
                    - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the
                      underlying model's ``__init__`` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class
                      initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of
                      ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute
                      with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration
                      attribute will be passed to the underlying model's ``__init__`` function.
"""

class AutoModelForQuestionAnswering:
    r"""
    This is a generic model class that will be instantiated as one of the model classes of the library---with a
    question answering head---when created with the when created with the
    :meth:`~transformers.AutoModeForQuestionAnswering.from_pretrained` class method or the
    :meth:`~transformers.AutoModelForQuestionAnswering.from_config` class method.
    This class cannot be instantiated directly using ``__init__()`` (throws an error).
    """

    def __init__(self):
        raise EnvironmentError(
            "AutoModelForQuestionAnswering is designed to be instantiated "
            "using the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
            "`AutoModelForQuestionAnswering.from_config(config)` methods."
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_QUESTION_ANSWERING_MAPPING, use_model_types=False)
    def from_config(cls, config):
        r"""
        Instantiates one of the model classes of the library---with a question answering head---from a configuration.
        Note:
            Loading a model from its configuration file does **not** load the model weights. It only affects the
            model's configuration. Use :meth:`~transformers.AutoModelForQuestionAnswering.from_pretrained` to load the
            model weights.
        Args:
            config (:class:`~transformers.PretrainedConfig`):
                The model class to instantiate is selected based on the configuration class:
                List options
        Examples::
            >>> from transformers import AutoConfig, AutoModelForQuestionAnswering
            >>> # Download configuration from S3 and cache.
            >>> config = AutoConfig.from_pretrained('bert-base-uncased')
            >>> model = AutoModelForQuestionAnswering.from_config(config)
        """
        if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
            return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)](config)

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
            )
        )

    @classmethod
    @replace_list_option_in_docstrings(MODEL_FOR_QUESTION_ANSWERING_MAPPING)
    @add_start_docstrings(
        "Instantiate one of the model classes of the library---with a question answering head---from a "
        "pretrained model.",
        AUTO_MODEL_PRETRAINED_DOCSTRING,
    )
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Examples::
            >>> from transformers import AutoConfig, AutoModelForQuestionAnswering
            >>> # Download model and configuration from S3 and cache.
            >>> model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')
            >>> # Update configuration during loading
            >>> model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attentions=True)
            >>> model.config.output_attentions
            True
            >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            >>> config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            >>> model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
        """
        config = kwargs.pop("config", None)
        if not isinstance(config, PretrainedConfig):
            config, kwargs = AutoConfig.from_pretrained(
                pretrained_model_name_or_path, return_unused_kwargs=True, **kwargs
            )

        if type(config) in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys():
            return MODEL_FOR_QUESTION_ANSWERING_MAPPING[type(config)].from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )

        raise ValueError(
            "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
            "Model type should be one of {}.".format(
                config.__class__,
                cls.__name__,
                ", ".join(c.__name__ for c in MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys()),
            )
        )