





"""simple docstring"""


# Copyright 2023 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__       :					Dict						=			{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Any						=			["""VivitImageProcessor"""]


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """VivitModel""",
		    """VivitPreTrainedModel""",
		    """VivitForVideoClassification""",
		]


if TYPE_CHECKING:
		from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_vivit import VivitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vivit import (
				    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VivitForVideoClassification,
				    VivitModel,
				    VivitPreTrainedModel,
				)


else:
		import sys

		__magic_name__       :					Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



