


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase	=							{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecAudioForAudioFrameClassification',
			    'Data2VecAudioForCTC',
			    'Data2VecAudioForSequenceClassification',
			    'Data2VecAudioForXVector',
			    'Data2VecAudioModel',
			    'Data2VecAudioPreTrainedModel',
			]
			lowerCAmelCase	=							[
			    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecTextForCausalLM',
			    'Data2VecTextForMaskedLM',
			    'Data2VecTextForMultipleChoice',
			    'Data2VecTextForQuestionAnswering',
			    'Data2VecTextForSequenceClassification',
			    'Data2VecTextForTokenClassification',
			    'Data2VecTextModel',
			    'Data2VecTextPreTrainedModel',
			]
			lowerCAmelCase	=							[
			    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Data2VecVisionForImageClassification',
			    'Data2VecVisionForMaskedImageModeling',
			    'Data2VecVisionForSemanticSegmentation',
			    'Data2VecVisionModel',
			    'Data2VecVisionPreTrainedModel',
			]

if is_tf_available():
			lowerCAmelCase	=							[
			    'TFData2VecVisionForImageClassification',
			    'TFData2VecVisionForSemanticSegmentation',
			    'TFData2VecVisionModel',
			    'TFData2VecVisionPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
			from .configuration_dataavec_text import (
			    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecTextConfig,
			    DataaVecTextOnnxConfig,
			)
			from .configuration_dataavec_vision import (
			    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecVisionConfig,
			    DataaVecVisionOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dataavec_audio import (
						    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecAudioForAudioFrameClassification,
						    DataaVecAudioForCTC,
						    DataaVecAudioForSequenceClassification,
						    DataaVecAudioForXVector,
						    DataaVecAudioModel,
						    DataaVecAudioPreTrainedModel,
						)
						from .modeling_dataavec_text import (
						    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecTextForCausalLM,
						    DataaVecTextForMaskedLM,
						    DataaVecTextForMultipleChoice,
						    DataaVecTextForQuestionAnswering,
						    DataaVecTextForSequenceClassification,
						    DataaVecTextForTokenClassification,
						    DataaVecTextModel,
						    DataaVecTextPreTrainedModel,
						)
						from .modeling_dataavec_vision import (
						    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForMaskedImageModeling,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						    DataaVecVisionPreTrainedModel,
						)
			if is_tf_available():
						from .modeling_tf_dataavec_vision import (
						    TFDataaVecVisionForImageClassification,
						    TFDataaVecVisionForSemanticSegmentation,
						    TFDataaVecVisionModel,
						    TFDataaVecVisionPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


if __name__ == "__main__":
			print(f"""{solution() = }""")


