






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     SCREAMING_SNAKE_CASE					(			unittest.TestCase	,						snake_case       ):





							"""simple docstring"""

							def    __A			(	self: int )     ->					Tuple:
												_A										=			load_tool('''text-to-speech''' )
												self.tool.setup()

							def    __A			(	self: Tuple )     ->					List[Any]:
												torch.manual_seed(0 )
												_A										=			self.tool('''hey''' )
												_A										=			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: Optional[int] )     ->					str:
												torch.manual_seed(0 )
												_A										=			self.tool('''hey''' )
												_A										=			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] )		,  ) )






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import _LazyModule


_a					:			Tuple							=    {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}


if TYPE_CHECKING:
  from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
  import sys

  _a					:			int							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


