# 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_tf_available, is_torch_available

_import_structure = {
    "distilbert.configuration_distilbert_performer": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
                                                      "DistilBertPerformerConfig"],
    "bert.configuration_bert_performer": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertPerformerConfig"],
    "t5.configuration_t5_performer": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5PerformerConfig"],
    "wav2vec2.configuration_wav2vec2_performer": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
                                                  "Wav2vec2PerformerConfig"]
}

if is_torch_available():
    _import_structure["distilbert.modeling_distilbert_performer"] = [
        "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DistilBertPerformerForMaskedLM",
        "DistilBertPerformerForMultipleChoice",
        "DistilBertPerformerForQuestionAnswering",
        "DistilBertPerformerForSequenceClassification",
        "DistilBertPerformerForTokenClassification",
        "DistilBertPerformerModel",
        "DistilBertPerformerPreTrainedModel",
    ]

    _import_structure["bert.modeling_bert_performer"] = [
        "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BertPerformerForMaskedLM",
        "BertPerformerForMultipleChoice",
        "BertPerformerForQuestionAnswering",
        "BertPerformerForSequenceClassification",
        "BertPerformerForTokenClassification",
        "BertPerformerModel",
        "BertPerformerPreTrainedModel",
    ]

    _import_structure["t5.modeling_t5_performer"] = [
        "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "T5PerformerEncoderModel",
        "T5PerformerForConditionalGeneration",
        "T5PerformerModel",
        "T5PerformerPreTrainedModel",
    ]

    _import_structure["wav2vec2.modeling_wav2vec2_performer"] = [
        "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
        "Wav2Vec2PerformerForCTC",
        "Wav2Vec2PerformerForMaskedLM",
        "Wav2Vec2PerformerModel",
        "Wav2Vec2PerformerPreTrainedModel",
    ]

if is_tf_available():
    _import_structure["distilbert.modeling_tf_distilbert_performer"] = [
        "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDistilBertPerformerForMaskedLM",
        "TFDistilBertPerformerForMultipleChoice",
        "TFDistilBertPerformerForQuestionAnswering",
        "TFDistilBertPerformerForSequenceClassification",
        "TFDistilBertPerformerForTokenClassification",
        "TFDistilBertPerformerMainLayer",
        "TFDistilBertPerformerModel",
        "TFDistilBertPerformerPreTrainedModel",
    ]

    _import_structure["bert.modeling_tf_bert_performer"] = [
        "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFBertPerformerForMaskedLM",
        "TFBertPerformerForMultipleChoice",
        "TFBertPerformerForQuestionAnswering",
        "TFBertPerformerForSequenceClassification",
        "TFBertPerformerForTokenClassification",
        "TFBertPerformerMainLayer",
        "TFBertPerformerModel",
        "TFBertPerformerPreTrainedModel",
    ]

    _import_structure["t5.modeling_tf_t5_performer"] = [
        "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFT5PerformerEncoderModel",
        "TFT5PerformerForConditionalGeneration",
        "TFT5PerformerModel",
        "TFT5PerformerPreTrainedModel",
    ]

if TYPE_CHECKING:
    from distilbert.configuration_distilbert_performer import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, \
        DistilBertPerformerConfig
    from bert.configuration_bert_performer import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertPerformerConfig

    if is_torch_available():
        from distilbert.modeling_distilbert_performer import (
            DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DistilBertPerformerForMaskedLM,
            DistilBertPerformerForMultipleChoice,
            DistilBertPerformerForQuestionAnswering,
            DistilBertPerformerForSequenceClassification,
            DistilBertPerformerForTokenClassification,
            DistilBertPerformerModel,
            DistilBertPerformerPreTrainedModel,
        )
        from bert.modeling_bert_performer import (
            BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BertPerformerForMaskedLM,
            BertPerformerForMultipleChoice,
            BertPerformerForQuestionAnswering,
            BertPerformerForSequenceClassification,
            BertPerformerForTokenClassification,
            BertPerformerModel,
            BertPerformerPreTrainedModel,
        )
        from t5.modeling_t5_performer import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            T5PerformerEncoderModel,
            T5PerformerForConditionalGeneration,
            T5PerformerModel,
            T5PerformerPreTrainedModel,
        )
        from wav2vec2.modeling_wav2vec2_performer import (
            WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
            Wav2Vec2PerformerForCTC,
            Wav2Vec2PerformerForMaskedLM,
            Wav2Vec2PerformerModel,
            Wav2Vec2PerformerPreTrainedModel,
        )

    if is_tf_available():
        from distilbert.modeling_tf_distilbert_performer import (
            TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDistilBertPerformerForMaskedLM,
            TFDistilBertPerformerForMultipleChoice,
            TFDistilBertPerformerForQuestionAnswering,
            TFDistilBertPerformerForSequenceClassification,
            TFDistilBertPerformerForTokenClassification,
            TFDistilBertPerformerMainLayer,
            TFDistilBertPerformerModel,
            TFDistilBertPerformerPreTrainedModel,
        )
        from bert.modeling_tf_bert_performer import (
            TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFBertPerformerForMaskedLM,
            TFBertPerformerForMultipleChoice,
            TFBertPerformerForQuestionAnswering,
            TFBertPerformerForSequenceClassification,
            TFBertPerformerForTokenClassification,
            TFBertPerformerMainLayer,
            TFBertPerformerModel,
            TFBertPerformerPreTrainedModel,
        )
        from t5.modeling_tf_t5_performer import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFT5PerformerEncoderModel,
            TFT5PerformerForConditionalGeneration,
            TFT5PerformerModel,
            TFT5PerformerPreTrainedModel,
        )

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)
