


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        lowerCamelCase__	(							unittest.TestCase					,	UpperCAmelCase      ):






			"""simple docstring"""






			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       load_tool("text-to-speech"     )
						self.tool.setup()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )
						UpperCamelCase__					=       self.tool("hey"     )
						UpperCamelCase__					=       result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]      ,						torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]     )      ,						)     )







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						torch.manual_seed(0     )
						UpperCamelCase__					=       self.tool("hey"     )
						UpperCamelCase__					=       result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]      ,						torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485]     )      ,						)     )






from __future__ import annotations
def        UpperCamelCase_(				_A   :list[int]				,     _A   :int  )->		list[int]:
			UpperCamelCase__					=       0
			UpperCamelCase__					=       len(_A  ) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									UpperCamelCase__					=       i + 1
						else:
									UpperCamelCase__					=       j - 1

			return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(f'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')


