





from typing import TYPE_CHECKING

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


UpperCAmelCase_							:      List[str]      =  {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_							:      Any      =  [
						    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RobertaForCausalLM",
						    "RobertaForMaskedLM",
						    "RobertaForMultipleChoice",
						    "RobertaForQuestionAnswering",
						    "RobertaForSequenceClassification",
						    "RobertaForTokenClassification",
						    "RobertaModel",
						    "RobertaPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_							:      Union[str, Any]      =  [
						    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRobertaForCausalLM",
						    "TFRobertaForMaskedLM",
						    "TFRobertaForMultipleChoice",
						    "TFRobertaForQuestionAnswering",
						    "TFRobertaForSequenceClassification",
						    "TFRobertaForTokenClassification",
						    "TFRobertaMainLayer",
						    "TFRobertaModel",
						    "TFRobertaPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_							:      str      =  [
						    "FlaxRobertaForCausalLM",
						    "FlaxRobertaForMaskedLM",
						    "FlaxRobertaForMultipleChoice",
						    "FlaxRobertaForQuestionAnswering",
						    "FlaxRobertaForSequenceClassification",
						    "FlaxRobertaForTokenClassification",
						    "FlaxRobertaModel",
						    "FlaxRobertaPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
						from .tokenization_roberta import RobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roberta_fast import RobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta import (
												    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaForCausalLM,
												    RobertaForMaskedLM,
												    RobertaForMultipleChoice,
												    RobertaForQuestionAnswering,
												    RobertaForSequenceClassification,
												    RobertaForTokenClassification,
												    RobertaModel,
												    RobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta import (
												    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaForCausalLM,
												    TFRobertaForMaskedLM,
												    TFRobertaForMultipleChoice,
												    TFRobertaForQuestionAnswering,
												    TFRobertaForSequenceClassification,
												    TFRobertaForTokenClassification,
												    TFRobertaMainLayer,
												    TFRobertaModel,
												    TFRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta import (
												    FlaxRobertaForCausalLM,
												    FlaxRobertaForMaskedLM,
												    FlaxRobertaForMultipleChoice,
												    FlaxRobertaForQuestionAnswering,
												    FlaxRobertaForSequenceClassification,
												    FlaxRobertaForTokenClassification,
												    FlaxRobertaModel,
												    FlaxRobertaPreTrainedModel,
												)

else:
						import sys

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










import math
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * power_factor
def  UpperCamelCase						(       _A			:				float			,							_A			:				float    )->       float:





						"""simple docstring"""





						if (
						    not isinstance(_A			,							(int, float)    )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("power_factor must be a valid float value between -1 and 1."    )
						return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





