# 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

# rely on isort to merge the imports
from transformers.file_utils import _LazyModule, is_tokenizers_available, is_torch_available


_import_structure = {
    "configuration_ofa": ["OFA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OFAConfig"],
    "tokenization_ofa": ["OFATokenizer"],
}

if is_tokenizers_available():
    _import_structure["tokenization_ofa_fast"] = ["OFATokenizerFast"]

if is_torch_available():
    _import_structure["modeling_ofa"] = [
        "OFA_PRETRAINED_MODEL_ARCHIVE_LIST",
        "OFAModel",
        "OFAPreTrainedModel",
    ]
    _import_structure["promptcap"] = [
        "PromptCap",
        "PromptCap_VQA"
    ]


if TYPE_CHECKING:
    from .configuration_ofa import OFA_PRETRAINED_CONFIG_ARCHIVE_MAP, OFAConfig
    from .tokenization_ofa import OFATokenizer

    if is_tokenizers_available():
        from .tokenization_ofa_fast import OFATokenizerFast

    if is_torch_available():
        from .modeling_ofa import (
            OFA_PRETRAINED_MODEL_ARCHIVE_LIST,
            OFAModel,
            OFAPreTrainedModel,
        )
        from .promptcap import (
            PromptCap,
            PromptCap_VQA
        )

else:
    import sys

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