codingutf8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license speecht5 isn t deterministic speecht5 isn t deterministic coding utf 8 2023 huggingface inc licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license speecht5 isn t deterministic speecht5 isn t deterministic
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 TextToSpeechToolTester(unittest.TestCase, ToolTesterMixin):
    def setUp(self):
        self.tool = load_tool("text-to-speech")
        self.tool.setup()

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

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