
'''simple docstring'''



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			,				_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					self.tool("""hey""" )
												_lowerCAmelCase    		=					result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]       ,  torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] )       ,  ) )




						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					self.tool("""hey""" )
												_lowerCAmelCase    		=					result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]       ,  torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] )       ,  ) )



'''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__)


