
'''simple docstring'''







import math



def 			_a		(	_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ):
						if (
						    not isinstance(_SCREAMING_SNAKE_CASE	,			(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 			_a		(	_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ):
						if (
						    not isinstance(_SCREAMING_SNAKE_CASE	,			(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()

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   Optional[Any]     =				{
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """RagModel""",
						    """RagPreTrainedModel""",
						    """RagSequenceForGeneration""",
						    """RagTokenForGeneration""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """TFRagModel""",
						    """TFRagPreTrainedModel""",
						    """TFRagSequenceForGeneration""",
						    """TFRagTokenForGeneration""",
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

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