# 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 .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
from .tokenization_bert_fast import BertTokenizerFast
from .modeling_bert import (
    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
    BertForMaskedLM,
    BertForMultipleChoice,
    BertForNextSentencePrediction,
    BertForPreTraining,
    BertForQuestionAnswering,
    BertForSequenceClassification,
    BertForTokenClassification,
    BertLayer,
    BertLMHeadModel,
    BertModel,
    BertPreTrainedModel,
    load_tf_weights_in_bert,
    BertForMetaQA,
    BertMultiTaskForMetaQA,
    BertMultiTaskForMetaQA_AnsClassifier,
    BertForMetaQA_Indep_Heads,
    BertForMetaQA_Cat_All_Ans,
)


# 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_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig"],
#     "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
# }

# if is_tokenizers_available():
#     _import_structure["tokenization_bert_fast"] = ["BertTokenizerFast"]

# if is_torch_available():
#     _import_structure["modeling_bert"] = [
#         "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
#         "BertForMaskedLM",
#         "BertForMultipleChoice",
#         "BertForNextSentencePrediction",
#         "BertForPreTraining",
#         "BertForQuestionAnswering",
#         "BertForSequenceClassification",
#         "BertForTokenClassification",
#         "BertLayer",
#         "BertLMHeadModel",
#         "BertModel",
#         "BertPreTrainedModel",
#         "load_tf_weights_in_bert",
#     ]

# if is_tf_available():
#     _import_structure["modeling_tf_bert"] = [
#         "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
#         "TFBertEmbeddings",
#         "TFBertForMaskedLM",
#         "TFBertForMultipleChoice",
#         "TFBertForNextSentencePrediction",
#         "TFBertForPreTraining",
#         "TFBertForQuestionAnswering",
#         "TFBertForSequenceClassification",
#         "TFBertForTokenClassification",
#         "TFBertLMHeadModel",
#         "TFBertMainLayer",
#         "TFBertModel",
#         "TFBertPreTrainedModel",
#     ]

# if is_flax_available():
#     _import_structure["modeling_flax_bert"] = [
#         "FlaxBertForMaskedLM",
#         "FlaxBertForMultipleChoice",
#         "FlaxBertForNextSentencePrediction",
#         "FlaxBertForPreTraining",
#         "FlaxBertForQuestionAnswering",
#         "FlaxBertForSequenceClassification",
#         "FlaxBertForTokenClassification",
#         "FlaxBertModel",
#         "FlaxBertPreTrainedModel",
#     ]

# if TYPE_CHECKING:
#     from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
#     from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

#     if is_tokenizers_available():
#         from .tokenization_bert_fast import BertTokenizerFast

#     if is_torch_available():
#         from .modeling_bert import (
#             BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
#             BertForMaskedLM,
#             BertForMultipleChoice,
#             BertForNextSentencePrediction,
#             BertForPreTraining,
#             BertForQuestionAnswering,
#             BertForSequenceClassification,
#             BertForTokenClassification,
#             BertLayer,
#             BertLMHeadModel,
#             BertModel,
#             BertPreTrainedModel,
#             load_tf_weights_in_bert,
#             BertForMetaQA,
#             BertMultiTaskForMetaQA,
#             BertMultiTaskForMetaQA_AnsClassifier,
#             BertForMetaQA_Indep_Heads,
#             BertForMetaQA_Cat_All_Ans,
#         )

#     if is_tf_available():
#         from .modeling_tf_bert import (
#             TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
#             TFBertEmbeddings,
#             TFBertForMaskedLM,
#             TFBertForMultipleChoice,
#             TFBertForNextSentencePrediction,
#             TFBertForPreTraining,
#             TFBertForQuestionAnswering,
#             TFBertForSequenceClassification,
#             TFBertForTokenClassification,
#             TFBertLMHeadModel,
#             TFBertMainLayer,
#             TFBertModel,
#             TFBertPreTrainedModel,
#         )

#     if is_flax_available():
#         from .modeling_flax_bert import (
#             FlaxBertForMaskedLM,
#             FlaxBertForMultipleChoice,
#             FlaxBertForNextSentencePrediction,
#             FlaxBertForPreTraining,
#             FlaxBertForQuestionAnswering,
#             FlaxBertForSequenceClassification,
#             FlaxBertForTokenClassification,
#             FlaxBertModel,
#             FlaxBertPreTrainedModel,
#         )

# 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)
