

import math

def 		UpperCamelCase   (lowercase_:					float       , lowercase_:					float   )	-> float:
	if (
	    not isinstance(lowercase_       , (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   (lowercase_:					float       , lowercase_:					float   )	-> float:
	if (
	    not isinstance(lowercase_       , (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()






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



