# What does this PR do?
Fixes red test on main GPTj test equivalence. Follows #24979
[`TF`]  Also apply patch to support left padding. # What does this PR do?
Fixes red test on main GPTj test equivalence. Follows #24979
# Copyright 2022 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_torch_available


_import_structure = {
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_xlm_roberta_xl"] = [
        "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XLMRobertaXLForCausalLM",
        "XLMRobertaXLForMaskedLM",
        "XLMRobertaXLForMultipleChoice",
        "XLMRobertaXLForQuestionAnswering",
        "XLMRobertaXLForSequenceClassification",
        "XLMRobertaXLForTokenClassification",
        "XLMRobertaXLModel",
        "XLMRobertaXLPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xlm_roberta_xl import (
        XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLMRobertaXLConfig,
        XLMRobertaXLOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm_roberta_xl import (
            XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMRobertaXLForCausalLM,
            XLMRobertaXLForMaskedLM,
            XLMRobertaXLForMultipleChoice,
            XLMRobertaXLForQuestionAnswering,
            XLMRobertaXLForSequenceClassification,
            XLMRobertaXLForTokenClassification,
            XLMRobertaXLModel,
            XLMRobertaXLPreTrainedModel,
        )

else:
    import sys

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

# Copyright 2022 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_torch_available


_import_structure = {
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _import_structure["modeling_xlm_roberta_xl"] = [
        "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XLMRobertaXLForCausalLM",
        "XLMRobertaXLForMaskedLM",
        "XLMRobertaXLForMultipleChoice",
        "XLMRobertaXLForQuestionAnswering",
        "XLMRobertaXLForSequenceClassification",
        "XLMRobertaXLForTokenClassification",
        "XLMRobertaXLModel",
        "XLMRobertaXLPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xlm_roberta_xl import (
        XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XLMRobertaXLConfig,
        XLMRobertaXLOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm_roberta_xl import (
            XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMRobertaXLForCausalLM,
            XLMRobertaXLForMaskedLM,
            XLMRobertaXLForMultipleChoice,
            XLMRobertaXLForQuestionAnswering,
            XLMRobertaXLForSequenceClassification,
            XLMRobertaXLForTokenClassification,
            XLMRobertaXLModel,
            XLMRobertaXLPreTrainedModel,
        )

else:
    import sys

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