master
/ transformers / models / deberta_v2 / __init__.py

__init__.py @3c11360 — raw · history · blame 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
# 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

from ...file_utils import _LazyModule, is_tf_available, is_torch_available


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

if is_tf_available():
    _import_structure["modeling_tf_deberta_v2"] = [
        "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDebertaV2ForMaskedLM",
        "TFDebertaV2ForQuestionAnswering",
        "TFDebertaV2ForSequenceClassification",
        "TFDebertaV2ForTokenClassification",
        "TFDebertaV2Model",
        "TFDebertaV2PreTrainedModel",
    ]

if is_torch_available():
    _import_structure["modeling_deberta_v2"] = [
        "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DebertaV2ForMaskedLM",
        "DebertaV2ForQuestionAnswering",
        "DebertaV2ForSequenceClassification",
        "DebertaV2ForTokenClassification",
        "DebertaV2Model",
        "DebertaV2PreTrainedModel",
    ]


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

    if is_tf_available():
        from .modeling_tf_deberta_v2 import (
            TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaV2ForMaskedLM,
            TFDebertaV2ForQuestionAnswering,
            TFDebertaV2ForSequenceClassification,
            TFDebertaV2ForTokenClassification,
            TFDebertaV2Model,
            TFDebertaV2PreTrainedModel,
        )

    if is_torch_available():
        from .modeling_deberta_v2 import (
            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaV2ForMaskedLM,
            DebertaV2ForQuestionAnswering,
            DebertaV2ForSequenceClassification,
            DebertaV2ForTokenClassification,
            DebertaV2Model,
            DebertaV2PreTrainedModel,
        )

else:
    import sys

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