# 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 (
    _BaseLazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_import_structure = {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

if is_tokenizers_available():
    _import_structure["tokenization_mpnet_fast"] = ["MPNetTokenizerFast"]

if is_torch_available():
    _import_structure["modeling_mpnet"] = [
        "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MPNetForMaskedLM",
        "MPNetForMultipleChoice",
        "MPNetForQuestionAnswering",
        "MPNetForSequenceClassification",
        "MPNetForTokenClassification",
        "MPNetLayer",
        "MPNetModel",
        "MPNetPreTrainedModel",
    ]

if is_tf_available():
    _import_structure["modeling_tf_mpnet"] = [
        "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMPNetEmbeddings",
        "TFMPNetForMaskedLM",
        "TFMPNetForMultipleChoice",
        "TFMPNetForQuestionAnswering",
        "TFMPNetForSequenceClassification",
        "TFMPNetForTokenClassification",
        "TFMPNetMainLayer",
        "TFMPNetModel",
        "TFMPNetPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    if is_tokenizers_available():
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    if is_torch_available():
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    if is_tf_available():
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import importlib
    import os
    import sys

    class _LazyModule(_BaseLazyModule):
        """
        Module class that surfaces all objects but only performs associated imports when the objects are requested.
        """

        __file__ = globals()["__file__"]
        __path__ = [os.path.dirname(__file__)]

        def _get_module(self, module_name: str):
            return importlib.import_module("." + module_name, self.__name__)

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