



from typing import TYPE_CHECKING

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


UpperCAmelCase      :							List[Any]							       ={
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      :							str							       =["""ElectraTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      :							Any							       =[
	    """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ElectraForCausalLM""",
	    """ElectraForMaskedLM""",
	    """ElectraForMultipleChoice""",
	    """ElectraForPreTraining""",
	    """ElectraForQuestionAnswering""",
	    """ElectraForSequenceClassification""",
	    """ElectraForTokenClassification""",
	    """ElectraModel""",
	    """ElectraPreTrainedModel""",
	    """load_tf_weights_in_electra""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      :							Dict							       =[
	    """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFElectraForMaskedLM""",
	    """TFElectraForMultipleChoice""",
	    """TFElectraForPreTraining""",
	    """TFElectraForQuestionAnswering""",
	    """TFElectraForSequenceClassification""",
	    """TFElectraForTokenClassification""",
	    """TFElectraModel""",
	    """TFElectraPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase      :							Dict							       =[
	    """FlaxElectraForCausalLM""",
	    """FlaxElectraForMaskedLM""",
	    """FlaxElectraForMultipleChoice""",
	    """FlaxElectraForPreTraining""",
	    """FlaxElectraForQuestionAnswering""",
	    """FlaxElectraForSequenceClassification""",
	    """FlaxElectraForTokenClassification""",
	    """FlaxElectraModel""",
	    """FlaxElectraPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
	from .tokenization_electra import ElectraTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_electra_fast import ElectraTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_electra import (
		    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ElectraForCausalLM,
		    ElectraForMaskedLM,
		    ElectraForMultipleChoice,
		    ElectraForPreTraining,
		    ElectraForQuestionAnswering,
		    ElectraForSequenceClassification,
		    ElectraForTokenClassification,
		    ElectraModel,
		    ElectraPreTrainedModel,
		    load_tf_weights_in_electra,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_electra import (
		    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFElectraForMaskedLM,
		    TFElectraForMultipleChoice,
		    TFElectraForPreTraining,
		    TFElectraForQuestionAnswering,
		    TFElectraForSequenceClassification,
		    TFElectraForTokenClassification,
		    TFElectraModel,
		    TFElectraPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_electra import (
		    FlaxElectraForCausalLM,
		    FlaxElectraForMaskedLM,
		    FlaxElectraForMultipleChoice,
		    FlaxElectraForPreTraining,
		    FlaxElectraForQuestionAnswering,
		    FlaxElectraForSequenceClassification,
		    FlaxElectraForTokenClassification,
		    FlaxElectraModel,
		    FlaxElectraPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase      :							int							       =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""






from __future__ import annotations

from math import pi, sqrt



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  tuple:

   if inductance <= 0:
      raise ValueError('''Inductance cannot be 0 or negative''' )

   elif capacitance <= 0:
      raise ValueError('''Capacitance cannot be 0 or negative''' )

   else:
      return (
          "Resonant frequency",
          float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
