public/gpt-2/transformers/models/led/__init__.py

# 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 2021 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_tf_available, is_tokenizers_available, is_torch_available


_import_structure = {
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

if is_tokenizers_available():
    _import_structure["tokenization_led_fast"] = ["LEDTokenizerFast"]

if is_torch_available():
    _import_structure["modeling_led"] = [
        "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LEDForConditionalGeneration",
        "LEDForQuestionAnswering",
        "LEDForSequenceClassification",
        "LEDModel",
        "LEDPreTrainedModel",
    ]


if is_tf_available():
    _import_structure["modeling_tf_led"] = ["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
    from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
    from .tokenization_led import LEDTokenizer

    if is_tokenizers_available():
        from .tokenization_led_fast import LEDTokenizerFast

    if is_torch_available():
        from .modeling_led import (
            LED_PRETRAINED_MODEL_ARCHIVE_LIST,
            LEDForConditionalGeneration,
            LEDForQuestionAnswering,
            LEDForSequenceClassification,
            LEDModel,
            LEDPreTrainedModel,
        )

    if is_tf_available():
        from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
    import sys

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