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.

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


authorized_types = ["text", "image", "audio"]


def create_inputs(input_types: List[str]):
    inputs = []

    for input_type in input_types:
        if input_type == "text":
            inputs.append("Text input")
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((512, 512))
            )
        elif input_type == "audio":
            inputs.append(torch.ones(3000))
        elif isinstance(input_type, list):
            inputs.append(create_inputs(input_type))
        else:
            raise ValueError(f"Invalid type requested: {input_type}")

    return inputs


def output_types(outputs: List):
    output_types = []

    for output in outputs:
        if isinstance(output, str):
            output_types.append("text")
        elif isinstance(output, Image.Image):
            output_types.append("image")
        elif isinstance(output, torch.Tensor):
            output_types.append("audio")
        else:
            raise ValueError(f"Invalid output: {output}")

    return output_types


@is_tool_test
class ToolTesterMixin:
    def test_inputs_outputs(self):
        self.assertTrue(hasattr(self.tool, "inputs"))
        self.assertTrue(hasattr(self.tool, "outputs"))

        inputs = self.tool.inputs
        for _input in inputs:
            if isinstance(_input, list):
                for __input in _input:
                    self.assertTrue(__input in authorized_types)
            else:
                self.assertTrue(_input in authorized_types)

        outputs = self.tool.outputs
        for _output in outputs:
            self.assertTrue(_output in authorized_types)

    def test_call(self):
        inputs = create_inputs(self.tool.inputs)
        outputs = self.tool(*inputs)

        # There is a single output
        if len(self.tool.outputs) == 1:
            outputs = [outputs]

        self.assertListEqual(output_types(outputs), self.tool.outputs)

    def test_common_attributes(self):
        self.assertTrue(hasattr(self.tool, "description"))
        self.assertTrue(hasattr(self.tool, "default_checkpoint"))
        self.assertTrue(self.tool.description.startswith("This is a tool that"))

# 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.

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


authorized_types = ["text", "image", "audio"]


def create_inputs(input_types: List[str]):
    inputs = []

    for input_type in input_types:
        if input_type == "text":
            inputs.append("Text input")
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png").resize((512, 512))
            )
        elif input_type == "audio":
            inputs.append(torch.ones(3000))
        elif isinstance(input_type, list):
            inputs.append(create_inputs(input_type))
        else:
            raise ValueError(f"Invalid type requested: {input_type}")

    return inputs


def output_types(outputs: List):
    output_types = []

    for output in outputs:
        if isinstance(output, (str, AgentText)):
            output_types.append("text")
        elif isinstance(output, (Image.Image, AgentImage)):
            output_types.append("image")
        elif isinstance(output, (torch.Tensor, AgentAudio)):
            output_types.append("audio")
        else:
            raise ValueError(f"Invalid output: {output}")

    return output_types


@is_tool_test
class ToolTesterMixin:
    def test_inputs_outputs(self):
        self.assertTrue(hasattr(self.tool, "inputs"))
        self.assertTrue(hasattr(self.tool, "outputs"))

        inputs = self.tool.inputs
        for _input in inputs:
            if isinstance(_input, list):
                for __input in _input:
                    self.assertTrue(__input in authorized_types)
            else:
                self.assertTrue(_input in authorized_types)

        outputs = self.tool.outputs
        for _output in outputs:
            self.assertTrue(_output in authorized_types)

    def test_call(self):
        inputs = create_inputs(self.tool.inputs)
        outputs = self.tool(*inputs)

        # There is a single output
        if len(self.tool.outputs) == 1:
            outputs = [outputs]

        self.assertListEqual(output_types(outputs), self.tool.outputs)

    def test_common_attributes(self):
        self.assertTrue(hasattr(self.tool, "description"))
        self.assertTrue(hasattr(self.tool, "default_checkpoint"))
        self.assertTrue(self.tool.description.startswith("This is a tool that"))

    def test_agent_types_outputs(self):
        inputs = create_inputs(self.tool.inputs)
        outputs = self.tool(*inputs)

        if not isinstance(outputs, list):
            outputs = [outputs]

        self.assertEqual(len(outputs), len(self.tool.outputs))

        for output, output_type in zip(outputs, self.tool.outputs):
            agent_type = AGENT_TYPE_MAPPING[output_type]
            self.assertTrue(isinstance(output, agent_type))

    def test_agent_types_inputs(self):
        inputs = create_inputs(self.tool.inputs)

        _inputs = []

        for _input, input_type in zip(inputs, self.tool.inputs):
            if isinstance(input_type, list):
                _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
            else:
                _inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
        outputs = self.tool(*inputs)

        if not isinstance(outputs, list):
            outputs = [outputs]

        self.assertEqual(len(outputs), len(self.tool.outputs))
