






'''simple docstring'''







import math





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> float:
							if (
							    not isinstance(UpperCamelCase       ,    (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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> float:
							if (
							    not isinstance(UpperCamelCase       ,    (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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

