from typing import Any

import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.ndarray			):
						return np.array_equal(_SCREAMING_SNAKE_CASE		, matrix.conjugate().T			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.ndarray		, _SCREAMING_SNAKE_CASE     :				np.ndarray			):
						UpperCamelCase_		:							Tuple  =		v.conjugate().T
						UpperCamelCase_		:							Dict  =		v_star.dot(_SCREAMING_SNAKE_CASE			)
						assert isinstance(_SCREAMING_SNAKE_CASE		, np.ndarray			)
						return (v_star_dot.dot(_SCREAMING_SNAKE_CASE			)) / (v_star.dot(_SCREAMING_SNAKE_CASE			))






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							List[Any]  =		np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]			)
						UpperCamelCase_		:							str  =		np.array([[1], [2], [3]]			)
						assert is_hermitian(_SCREAMING_SNAKE_CASE			), f'''{a} is not hermitian.'''
						print(rayleigh_quotient(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)			)

						UpperCamelCase_		:							List[Any]  =		np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
						assert is_hermitian(_SCREAMING_SNAKE_CASE			), f'''{a} is not hermitian.'''
						assert rayleigh_quotient(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) == float(3			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	tests()







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        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






