




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   a (			self	:	Tuple  ) ->			List[Any]:
         lowerCAmelCase__            =					load_tool("text-to-speech"  )
         self.tool.setup()


    def   a (			self	:	Dict  ) ->			Optional[int]:
         # SpeechT5 isn't deterministic
         torch.manual_seed(0  )
         lowerCAmelCase__            =					self.tool("hey"  )
         lowerCAmelCase__            =					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	:	List[Any]  ) ->			Union[str, Any]:
         # SpeechT5 isn't deterministic
         torch.manual_seed(0  )
         lowerCAmelCase__            =					self.tool("hey"  )
         lowerCAmelCase__            =					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]  )  ,      )  )



from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

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


