# 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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_import_structure = {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"],
}

if is_sentencepiece_available():
    _import_structure["tokenization_camembert"] = ["CamembertTokenizer"]

if is_tokenizers_available():
    _import_structure["tokenization_camembert_fast"] = ["CamembertTokenizerFast"]

if is_torch_available():
    _import_structure["modeling_camembert"] = [
        "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CamembertForCausalLM",
        "CamembertForMaskedLM",
        "CamembertForMultipleChoice",
        "CamembertForQuestionAnswering",
        "CamembertForSequenceClassification",
        "CamembertForTokenClassification",
        "CamembertModel",
    ]

if is_tf_available():
    _import_structure["modeling_tf_camembert"] = [
        "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFCamembertForMaskedLM",
        "TFCamembertForMultipleChoice",
        "TFCamembertForQuestionAnswering",
        "TFCamembertForSequenceClassification",
        "TFCamembertForTokenClassification",
        "TFCamembertModel",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig

    if is_sentencepiece_available():
        from .tokenization_camembert import CamembertTokenizer

    if is_tokenizers_available():
        from .tokenization_camembert_fast import CamembertTokenizerFast

    if is_torch_available():
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
        )

    if is_tf_available():
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
        )

else:
    import sys

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