287 lines
8.7 KiB
Python
287 lines
8.7 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from pathlib import Path
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from typing import Dict, Union
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import numpy as np
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import pytest
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from transformers import is_torch_available, is_vision_available
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from smolagents.types import (
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AGENT_TYPE_MAPPING,
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AgentAudio,
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AgentImage,
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AgentText,
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)
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from smolagents.tools import Tool, tool, AUTHORIZED_TYPES
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from transformers.testing_utils import get_tests_dir
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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def create_inputs(tool_inputs: Dict[str, Dict[Union[str, type], str]]):
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inputs = {}
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for input_name, input_desc in tool_inputs.items():
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input_type = input_desc["type"]
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if input_type == "string":
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inputs[input_name] = "Text input"
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elif input_type == "image":
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inputs[input_name] = Image.open(
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Path(get_tests_dir("fixtures")) / "000000039769.png"
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).resize((512, 512))
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elif input_type == "audio":
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inputs[input_name] = np.ones(3000)
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else:
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raise ValueError(f"Invalid type requested: {input_type}")
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return inputs
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def output_type(output):
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if isinstance(output, (str, AgentText)):
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return "string"
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elif isinstance(output, (Image.Image, AgentImage)):
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return "image"
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elif isinstance(output, (torch.Tensor, AgentAudio)):
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return "audio"
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else:
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raise TypeError(f"Invalid output: {output}")
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class ToolTesterMixin:
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def test_inputs_output(self):
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self.assertTrue(hasattr(self.tool, "inputs"))
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self.assertTrue(hasattr(self.tool, "output_type"))
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inputs = self.tool.inputs
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self.assertTrue(isinstance(inputs, dict))
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for _, input_spec in inputs.items():
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self.assertTrue("type" in input_spec)
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self.assertTrue("description" in input_spec)
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self.assertTrue(input_spec["type"] in AUTHORIZED_TYPES)
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self.assertTrue(isinstance(input_spec["description"], str))
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output_type = self.tool.output_type
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self.assertTrue(output_type in AUTHORIZED_TYPES)
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def test_common_attributes(self):
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self.assertTrue(hasattr(self.tool, "description"))
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self.assertTrue(hasattr(self.tool, "name"))
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self.assertTrue(hasattr(self.tool, "inputs"))
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self.assertTrue(hasattr(self.tool, "output_type"))
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def test_agent_type_output(self):
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inputs = create_inputs(self.tool.inputs)
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output = self.tool(**inputs)
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if self.tool.output_type != "any":
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agent_type = AGENT_TYPE_MAPPING[self.tool.output_type]
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self.assertTrue(isinstance(output, agent_type))
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class ToolTests(unittest.TestCase):
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def test_tool_init_with_decorator(self):
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@tool
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def coolfunc(a: str, b: int) -> float:
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"""Cool function
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Args:
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a: The first argument
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b: The second one
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"""
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return b + 2, a
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assert coolfunc.output_type == "number"
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def test_tool_init_vanilla(self):
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class HFModelDownloadsTool(Tool):
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name = "model_download_counter"
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description = """
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This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub.
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It returns the name of the checkpoint."""
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inputs = {
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"task": {
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"type": "string",
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"description": "the task category (such as text-classification, depth-estimation, etc)",
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}
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}
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output_type = "integer"
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def forward(self, task):
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return "best model"
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tool = HFModelDownloadsTool()
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assert list(tool.inputs.keys())[0] == "task"
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def test_tool_init_decorator_raises_issues(self):
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with pytest.raises(Exception) as e:
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@tool
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def coolfunc(a: str, b: int):
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"""Cool function
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Args:
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a: The first argument
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b: The second one
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"""
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return a + b
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assert coolfunc.output_type == "number"
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assert "Tool return type not found" in str(e)
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with pytest.raises(Exception) as e:
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@tool
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def coolfunc(a: str, b: int) -> int:
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"""Cool function
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Args:
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a: The first argument
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"""
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return b + a
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assert coolfunc.output_type == "number"
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assert "docstring has no description for the argument" in str(e)
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def test_tool_definition_raises_error_imports_outside_function(self):
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with pytest.raises(Exception) as e:
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from datetime import datetime
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@tool
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def get_current_time() -> str:
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"""
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Gets the current time.
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"""
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return str(datetime.now())
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get_current_time.save("output")
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assert "datetime" in str(e)
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# Also test with classic definition
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with pytest.raises(Exception) as e:
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class GetCurrentTimeTool(Tool):
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name = "get_current_time_tool"
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description = "Gets the current time"
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inputs = {}
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output_type = "string"
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def forward(self):
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return str(datetime.now())
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get_current_time = GetCurrentTimeTool()
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get_current_time.save("output")
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assert "datetime" in str(e)
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def test_tool_definition_raises_no_error_imports_in_function(self):
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@tool
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def get_current_time() -> str:
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"""
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Gets the current time.
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"""
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from datetime import datetime
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return str(datetime.now())
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class GetCurrentTimeTool(Tool):
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name = "get_current_time_tool"
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description = "Gets the current time"
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inputs = {}
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output_type = "string"
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def forward(self):
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from datetime import datetime
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return str(datetime.now())
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def test_saving_tool_allows_no_arg_in_init(self):
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# Test one cannot save tool with additional args in init
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class FailTool(Tool):
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name = "specific"
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description = "test description"
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inputs = {
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"input_str": {"type": "string", "description": "input description"}
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}
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output_type = "string"
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def __init__(self, url):
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super().__init__(self)
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self.url = "none"
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def forward(self, string_input):
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return self.url + string_input
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fail_tool = FailTool("dummy_url")
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with pytest.raises(Exception) as e:
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fail_tool.save("output")
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assert "__init__" in str(e)
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def test_saving_tool_allows_no_imports_from_outside_methods(self):
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# Test that using imports from outside functions fails
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from numpy import random
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class FailTool2(Tool):
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name = "specific"
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description = "test description"
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inputs = {
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"input_str": {"type": "string", "description": "input description"}
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}
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output_type = "string"
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def useless_method(self):
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self.client = random.random()
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return ""
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def forward(self, string_input):
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return self.useless_method() + string_input
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fail_tool_2 = FailTool2()
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with pytest.raises(Exception) as e:
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fail_tool_2.save("output")
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assert "random" in str(e)
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# Test that putting these imports inside functions works
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class FailTool3(Tool):
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name = "specific"
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description = "test description"
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inputs = {
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"input_str": {"type": "string", "description": "input description"}
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}
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output_type = "string"
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def useless_method(self):
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from numpy import random
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self.client = random.random()
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return ""
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def forward(self, string_input):
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return self.useless_method() + string_input
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fail_tool_3 = FailTool3()
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fail_tool_3.save("output")
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