




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase      :  List[str]      						=						{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      :  str      						=						[
	    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "RobertaPreLayerNormForCausalLM",
	    "RobertaPreLayerNormForMaskedLM",
	    "RobertaPreLayerNormForMultipleChoice",
	    "RobertaPreLayerNormForQuestionAnswering",
	    "RobertaPreLayerNormForSequenceClassification",
	    "RobertaPreLayerNormForTokenClassification",
	    "RobertaPreLayerNormModel",
	    "RobertaPreLayerNormPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      :  Tuple      						=						[
	    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFRobertaPreLayerNormForCausalLM",
	    "TFRobertaPreLayerNormForMaskedLM",
	    "TFRobertaPreLayerNormForMultipleChoice",
	    "TFRobertaPreLayerNormForQuestionAnswering",
	    "TFRobertaPreLayerNormForSequenceClassification",
	    "TFRobertaPreLayerNormForTokenClassification",
	    "TFRobertaPreLayerNormMainLayer",
	    "TFRobertaPreLayerNormModel",
	    "TFRobertaPreLayerNormPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      :  str      						=						[
	    "FlaxRobertaPreLayerNormForCausalLM",
	    "FlaxRobertaPreLayerNormForMaskedLM",
	    "FlaxRobertaPreLayerNormForMultipleChoice",
	    "FlaxRobertaPreLayerNormForQuestionAnswering",
	    "FlaxRobertaPreLayerNormForSequenceClassification",
	    "FlaxRobertaPreLayerNormForTokenClassification",
	    "FlaxRobertaPreLayerNormModel",
	    "FlaxRobertaPreLayerNormPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

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







from math import loga




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				if a < 0:
								raise ValueError("""Input value must be a positive integer"""       )
				elif isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								raise TypeError("""Input value must be a 'int' type"""       )
				return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

