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# coding=utf-8
# Copyright 2024 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 smolagents import (
AgentError,
AgentImage,
CodeAgent,
ToolCallingAgent,
stream_to_gradio,
)
from smolagents.models import (
ChatMessage,
ChatMessageToolCall,
ChatMessageToolCallDefinition,
)
from smolagents.monitoring import AgentLogger, LogLevel
class FakeLLMModel:
def __init__(self):
self.last_input_token_count = 10
self.last_output_token_count = 20
def __call__(self, prompt, tools_to_call_from=None, **kwargs):
if tools_to_call_from is not None:
return ChatMessage(
role="assistant",
content="",
tool_calls=[
ChatMessageToolCall(
id="fake_id",
type="function",
function=ChatMessageToolCallDefinition(name="final_answer", arguments={"answer": "image"}),
)
],
)
else:
return ChatMessage(
role="assistant",
content="""
Code:
```py
final_answer('This is the final answer.')
```""",
)
class MonitoringTester(unittest.TestCase):
def test_code_agent_metrics(self):
agent = CodeAgent(
tools=[],
model=FakeLLMModel(),
max_steps=1,
)
agent.run("Fake task")
self.assertEqual(agent.monitor.total_input_token_count, 10)
self.assertEqual(agent.monitor.total_output_token_count, 20)
def test_toolcalling_agent_metrics(self):
agent = ToolCallingAgent(
tools=[],
model=FakeLLMModel(),
max_steps=1,
)
agent.run("Fake task")
self.assertEqual(agent.monitor.total_input_token_count, 10)
self.assertEqual(agent.monitor.total_output_token_count, 20)
def test_code_agent_metrics_max_steps(self):
class FakeLLMModelMalformedAnswer:
def __init__(self):
self.last_input_token_count = 10
self.last_output_token_count = 20
def __call__(self, prompt, **kwargs):
return ChatMessage(role="assistant", content="Malformed answer")
agent = CodeAgent(
tools=[],
model=FakeLLMModelMalformedAnswer(),
max_steps=1,
)
agent.run("Fake task")
self.assertEqual(agent.monitor.total_input_token_count, 20)
self.assertEqual(agent.monitor.total_output_token_count, 40)
def test_code_agent_metrics_generation_error(self):
class FakeLLMModelGenerationException:
def __init__(self):
self.last_input_token_count = 10
self.last_output_token_count = 20
def __call__(self, prompt, **kwargs):
self.last_input_token_count = 10
self.last_output_token_count = 0
raise Exception("Cannot generate")
agent = CodeAgent(
tools=[],
model=FakeLLMModelGenerationException(),
max_steps=1,
)
agent.run("Fake task")
self.assertEqual(agent.monitor.total_input_token_count, 20) # Should have done two monitoring callbacks
self.assertEqual(agent.monitor.total_output_token_count, 0)
def test_streaming_agent_text_output(self):
agent = CodeAgent(
tools=[],
model=FakeLLMModel(),
max_steps=1,
)
# Use stream_to_gradio to capture the output
outputs = list(stream_to_gradio(agent, task="Test task"))
self.assertEqual(len(outputs), 7)
final_message = outputs[-1]
self.assertEqual(final_message.role, "assistant")
self.assertIn("This is the final answer.", final_message.content)
def test_streaming_agent_image_output(self):
agent = ToolCallingAgent(
tools=[],
model=FakeLLMModel(),
max_steps=1,
)
# Use stream_to_gradio to capture the output
outputs = list(
stream_to_gradio(
agent,
task="Test task",
additional_args=dict(image=AgentImage(value="path.png")),
)
)
self.assertEqual(len(outputs), 5)
final_message = outputs[-1]
self.assertEqual(final_message.role, "assistant")
self.assertIsInstance(final_message.content, dict)
self.assertEqual(final_message.content["path"], "path.png")
self.assertEqual(final_message.content["mime_type"], "image/png")
def test_streaming_with_agent_error(self):
logger = AgentLogger(level=LogLevel.INFO)
def dummy_model(prompt, **kwargs):
raise AgentError("Simulated agent error", logger)
agent = CodeAgent(
tools=[],
model=dummy_model,
max_steps=1,
)
# Use stream_to_gradio to capture the output
outputs = list(stream_to_gradio(agent, task="Test task"))
self.assertEqual(len(outputs), 9)
final_message = outputs[-1]
self.assertEqual(final_message.role, "assistant")
self.assertIn("Simulated agent error", final_message.content)
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