master
/ transformers / models / xlm / __init__.py

__init__.py @3c11360 — raw · history · blame 

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# flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tf_available, is_torch_available


_import_structure = {
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

if is_torch_available():
    _import_structure["modeling_xlm"] = [
        "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XLMForMultipleChoice",
        "XLMForQuestionAnswering",
        "XLMForQuestionAnsweringSimple",
        "XLMForSequenceClassification",
        "XLMForTokenClassification",
        "XLMModel",
        "XLMPreTrainedModel",
        "XLMWithLMHeadModel",
    ]

if is_tf_available():
    _import_structure["modeling_tf_xlm"] = [
        "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFXLMForMultipleChoice",
        "TFXLMForQuestionAnsweringSimple",
        "TFXLMForSequenceClassification",
        "TFXLMForTokenClassification",
        "TFXLMMainLayer",
        "TFXLMModel",
        "TFXLMPreTrainedModel",
        "TFXLMWithLMHeadModel",
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
    from .tokenization_xlm import XLMTokenizer

    if is_torch_available():
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    if is_tf_available():
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)