WIP PR to have specific types for tool outputs, which should clarify interaction to and from Agents.

Left to do:
- [x] Remove or complete the video integration
- [x] Add support for remote tools
- [x] Complete documentation
- [x] Test it out with real world use-cases
- [x] Add a test to ensure that the inputs are cast correctly (so far only the outputs are tested)
- [x] Arrange the dependencies so that they don't make all the tests fail
Tool types. WIP PR to have specific types for tool outputs, which should clarify interaction to and from Agents.

Left to do:
- [x] Remove or complete the video integration
- [x] Add support for remote tools
- [x] Complete documentation
- [x] Test it out with real world use-cases
- [x] Add a test to ensure that the inputs are cast correctly (so far only the outputs are tested)
- [x] Arrange the dependencies so that they don't make all the tests fail
# coding=utf-8
# Copyright 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.

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):
        # SpeechT5 isn't deterministic
        torch.manual_seed(0)
        result = self.tool("hey")
        self.assertTrue(
            torch.allclose(
                result[:3], torch.tensor([-0.00040140701457858086, -0.0002551682700868696, -0.00010294507956132293])
            )
        )

    def test_exact_match_kwarg(self):
        # SpeechT5 isn't deterministic
        torch.manual_seed(0)
        result = self.tool("hey")
        self.assertTrue(
            torch.allclose(
                result[:3], torch.tensor([-0.00040140701457858086, -0.0002551682700868696, -0.00010294507956132293])
            )
        )

# coding=utf-8
# Copyright 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.

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):
        # SpeechT5 isn't deterministic
        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):
        # SpeechT5 isn't deterministic
        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]),
            )
        )
