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Sat, Sep 21, 18:19
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__init__.py
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# 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
(
_BaseLazyModule
,
is_sentencepiece_available
,
is_tf_available
,
is_tokenizers_available
,
is_torch_available
,
)
_import_structure
=
{
"configuration_camembert"
:
[
"CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"
,
"CamembertConfig"
],
}
if
is_sentencepiece_available
():
_import_structure
[
"tokenization_camembert"
]
=
[
"CamembertTokenizer"
]
if
is_tokenizers_available
():
_import_structure
[
"tokenization_camembert_fast"
]
=
[
"CamembertTokenizerFast"
]
if
is_torch_available
():
_import_structure
[
"modeling_camembert"
]
=
[
"CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST"
,
"CamembertForCausalLM"
,
"CamembertForMaskedLM"
,
"CamembertForMultipleChoice"
,
"CamembertForQuestionAnswering"
,
"CamembertForSequenceClassification"
,
"CamembertForTokenClassification"
,
"CamembertModel"
,
]
if
is_tf_available
():
_import_structure
[
"modeling_tf_camembert"
]
=
[
"TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST"
,
"TFCamembertForMaskedLM"
,
"TFCamembertForMultipleChoice"
,
"TFCamembertForQuestionAnswering"
,
"TFCamembertForSequenceClassification"
,
"TFCamembertForTokenClassification"
,
"TFCamembertModel"
,
]
if
TYPE_CHECKING
:
from
.configuration_camembert
import
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
,
CamembertConfig
if
is_sentencepiece_available
():
from
.tokenization_camembert
import
CamembertTokenizer
if
is_tokenizers_available
():
from
.tokenization_camembert_fast
import
CamembertTokenizerFast
if
is_torch_available
():
from
.modeling_camembert
import
(
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
,
CamembertForCausalLM
,
CamembertForMaskedLM
,
CamembertForMultipleChoice
,
CamembertForQuestionAnswering
,
CamembertForSequenceClassification
,
CamembertForTokenClassification
,
CamembertModel
,
)
if
is_tf_available
():
from
.modeling_tf_camembert
import
(
TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
,
TFCamembertForMaskedLM
,
TFCamembertForMultipleChoice
,
TFCamembertForQuestionAnswering
,
TFCamembertForSequenceClassification
,
TFCamembertForTokenClassification
,
TFCamembertModel
,
)
else
:
import
importlib
import
os
import
sys
class
_LazyModule
(
_BaseLazyModule
):
"""
Module class that surfaces all objects but only performs associated imports when the objects are requested.
"""
__file__
=
globals
()[
"__file__"
]
__path__
=
[
os
.
path
.
dirname
(
__file__
)]
def
_get_module
(
self
,
module_name
:
str
):
return
importlib
.
import_module
(
"."
+
module_name
,
self
.
__name__
)
sys
.
modules
[
__name__
]
=
_LazyModule
(
__name__
,
_import_structure
)
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