def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						if not all(x.isalpha() for x in string			):
												raise ValueError("""String must only contain alphabetic characters."""			)

						UpperCamelCase_		:							Optional[int]  =		sorted(string.lower()			)
						return len(_SCREAMING_SNAKE_CASE			) == len(set(_SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     str         =	input("Enter a string ").strip()

	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	is_isogram(input_str)
	print(F'''{input_str} is {"an" if isogram else "not an"} isogram.''')







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]						)			,     )						)






