FM / tapas

/usr/local/lib/python3.6/site-packages/transformers/models/tapas/

/usr/local/lib/python3.6/site-packages/transformers/models/tapas

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
configuration_tapas.py	12768	0644	edit dl rm
convert_tapas_original_tf_checkpoint_to_pytorch.py	5021	0644	edit dl rm
modeling_tapas.py	110373	0644	edit dl rm
modeling_tf_tapas.py	106721	0644	edit dl rm
tokenization_tapas.py	119961	0644	edit dl rm
__init__.py	2595	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/transformers/models/tapas/__init__.py (2595B)

# 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 ...utils import _LazyModule, is_tf_available, is_torch_available

_import_structure = {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

if is_torch_available():
    _import_structure["modeling_tapas"] = [
        "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TapasForMaskedLM",
        "TapasForQuestionAnswering",
        "TapasForSequenceClassification",
        "TapasModel",
        "TapasPreTrainedModel",
        "load_tf_weights_in_tapas",
    ]
if is_tf_available():
    _import_structure["modeling_tf_tapas"] = [
        "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFTapasForMaskedLM",
        "TFTapasForQuestionAnswering",
        "TFTapasForSequenceClassification",
        "TFTapasModel",
        "TFTapasPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
    from .tokenization_tapas import TapasTokenizer

if is_torch_available():
        from .modeling_tapas import (
            TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TapasForMaskedLM,
            TapasForQuestionAnswering,
            TapasForSequenceClassification,
            TapasModel,
            TapasPreTrainedModel,
            load_tf_weights_in_tapas,
        )

if is_tf_available():
        from .modeling_tf_tapas import (
            TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTapasForMaskedLM,
            TFTapasForQuestionAnswering,
            TFTapasForSequenceClassification,
            TFTapasModel,
            TFTapasPreTrainedModel,
        )

else:
    import sys

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

# 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 ...utils import _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"], "tokenization_tapas": ["TapasTokenizer"], } if is_torch_available(): _import_structure["modeling_tapas"] = [ "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TapasForMaskedLM", "TapasForQuestionAnswering", "TapasForSequenceClassification", "TapasModel", "TapasPreTrainedModel", "load_tf_weights_in_tapas", ] if is_tf_available(): _import_structure["modeling_tf_tapas"] = [ "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTapasPreTrainedModel", ] if TYPE_CHECKING: from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig from .tokenization_tapas import TapasTokenizer if is_torch_available(): from .modeling_tapas import ( TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas, ) if is_tf_available(): from .modeling_tf_tapas import ( TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)