




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   A(						unittest.TestCase							,				UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  Optional[int]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   load_tool('text-to-speech'  )
       self.tool.setup()

 def     a__			(    self :  List[Any]  )       ->     Optional[Any]:

       """simple docstring"""






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





 def     a__			(    self :  str  )       ->     Optional[Any]:

       """simple docstring"""






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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	int					):


      '''simple docstring'''



      return [sentence[i : i + ngram_size] for i in range(len(lowercase					) - ngram_size + 1					)]


if __name__ == "__main__":
 from doctest import testmod

 testmod()



