





'''simple docstring'''

from __future__ import annotations

from cmath import sqrt

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       tuple[complex, complex]:

   '''simple docstring'''







   if a == 0:
      raise ValueError("""Coefficient 'a' must not be zero."""   )
   __SCREAMING_SNAKE_CASE   				=							b * b - 4 * a * c

   __SCREAMING_SNAKE_CASE   				=							(-b + sqrt(__UpperCAmelCase   )) / (2 * a)
   __SCREAMING_SNAKE_CASE   				=							(-b - sqrt(__UpperCAmelCase   )) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							quadratic_roots(a=5				,						b=6				,						c=1   )
   print(f"""The solutions are: {solutiona} and {solutiona}"""   )


if __name__ == "__main__":
    main()








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]     )       ,			)     )



