





'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase					    =	{
    "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:
	__UpperCamelCase					    =	[
	    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecAudioForAudioFrameClassification",
	    "Data2VecAudioForCTC",
	    "Data2VecAudioForSequenceClassification",
	    "Data2VecAudioForXVector",
	    "Data2VecAudioModel",
	    "Data2VecAudioPreTrainedModel",
	]
	__UpperCamelCase					    =	[
	    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecTextForCausalLM",
	    "Data2VecTextForMaskedLM",
	    "Data2VecTextForMultipleChoice",
	    "Data2VecTextForQuestionAnswering",
	    "Data2VecTextForSequenceClassification",
	    "Data2VecTextForTokenClassification",
	    "Data2VecTextModel",
	    "Data2VecTextPreTrainedModel",
	]
	__UpperCamelCase					    =	[
	    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Data2VecVisionForImageClassification",
	    "Data2VecVisionForMaskedImageModeling",
	    "Data2VecVisionForSemanticSegmentation",
	    "Data2VecVisionModel",
	    "Data2VecVisionPreTrainedModel",
	]

if is_tf_available():
	__UpperCamelCase					    =	[
	    "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

	__UpperCamelCase					    =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


