transformers_4_35_0/models/deberta_v2/__init__.py

# 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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_import_structure = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["tokenization_deberta_v2_fast"] = ["DebertaV2TokenizerFast"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_tf_deberta_v2"] = [
        "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaV2ForMaskedLM",
        "TFDebertaV2ForQuestionAnswering",
        "TFDebertaV2ForMultipleChoice",
        "TFDebertaV2ForSequenceClassification",
        "TFDebertaV2ForTokenClassification",
        "TFDebertaV2Model",
        "TFDebertaV2PreTrainedModel",
    ]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_deberta_v2"] = [
        "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaV2ForMaskedLM",
        "DebertaV2ForMultipleChoice",
        "DebertaV2ForQuestionAnswering",
        "DebertaV2ForSequenceClassification",
        "DebertaV2ForTokenClassification",
        "DebertaV2Model",
        "DebertaV2PreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deberta_v2 import (
        DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DebertaV2Config,
        DebertaV2OnnxConfig,
    )
    from .tokenization_deberta_v2 import DebertaV2Tokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_v2_fast import DebertaV2TokenizerFast

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta_v2 import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaV2ForMaskedLM,
            TFDebertaV2ForMultipleChoice,
            TFDebertaV2ForQuestionAnswering,
            TFDebertaV2ForSequenceClassification,
            TFDebertaV2ForTokenClassification,
            TFDebertaV2Model,
            TFDebertaV2PreTrainedModel,
        )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta_v2 import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaV2ForMaskedLM,
            DebertaV2ForMultipleChoice,
            DebertaV2ForQuestionAnswering,
            DebertaV2ForSequenceClassification,
            DebertaV2ForTokenClassification,
            DebertaV2Model,
            DebertaV2PreTrainedModel,
        )

else:
    import sys

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