Lightstar/venv/Lib/site-packages/transformers/models/umt5/__init__.py

# Copyright 2023 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 ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_import_structure = {"configuration_umt5": ["UMT5Config", "UMT5OnnxConfig"]}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_umt5"] = [
        "UMT5EncoderModel",
        "UMT5ForConditionalGeneration",
        "UMT5ForQuestionAnswering",
        "UMT5ForSequenceClassification",
        "UMT5ForTokenClassification",
        "UMT5Model",
        "UMT5PreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_umt5 import UMT5Config, UMT5OnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_umt5 import (
            UMT5EncoderModel,
            UMT5ForConditionalGeneration,
            UMT5ForQuestionAnswering,
            UMT5ForSequenceClassification,
            UMT5ForTokenClassification,
            UMT5Model,
            UMT5PreTrainedModel,
        )
else:
    import sys

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