





from __future__ import annotations

from collections.abc import Iterator




class 	UpperCamelCase_							:


							def __init__(				self     :Any				,					__A     :int		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   value
									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   None




class 	UpperCamelCase_							:


							def __init__(				self     :Dict				,					__A     :Node		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   tree


							def   _snake_case	(				self     :List[str]				,					__A     :Node | None		) -> int:



									"""simple docstring"""



									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left		) + self.depth_first_search(node.right		)
									)


							def __iter__(				self     :Tuple		) -> Iterator[int]:



									"""simple docstring"""



									yield self.depth_first_search(self.tree		)


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        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]						)			,     )						)






