# Copyright 2023 HuggingFace Inc. team and MosaicML NLP team.
#
# 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_mpt": ["MPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MptConfig", "MptOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_import_structure["modeling_mpt"] = [
"MPT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MptForCausalLM",
"MptModel",
"MptPreTrainedModel",
"MptForSequenceClassification",
"MptForTokenClassification",
"MptForQuestionAnswering",
]
if TYPE_CHECKING:
from .configuration_mpt import MPT_PRETRAINED_CONFIG_ARCHIVE_MAP, MptConfig, MptOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mpt import (
MPT_PRETRAINED_MODEL_ARCHIVE_LIST,
MptForCausalLM,
MptForQuestionAnswering,
MptForSequenceClassification,
MptForTokenClassification,
MptModel,
MptPreTrainedModel,
)
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)