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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 json
import sys
import unittest
from pathlib import Path
from typing import Optional
from unittest.mock import MagicMock, patch
import pytest
from transformers.testing_utils import get_tests_dir
from smolagents.models import (
ChatMessage,
HfApiModel,
LiteLLMModel,
MessageRole,
MLXModel,
OpenAIServerModel,
TransformersModel,
get_clean_message_list,
get_tool_json_schema,
parse_json_if_needed,
parse_tool_args_if_needed,
)
from smolagents.tools import tool
from .utils.markers import require_run_all
class ModelTests(unittest.TestCase):
def test_get_json_schema_has_nullable_args(self):
@tool
def get_weather(location: str, celsius: Optional[bool] = False) -> str:
"""
Get weather in the next days at given location.
Secretly this tool does not care about the location, it hates the weather everywhere.
Args:
location: the location
celsius: the temperature type
"""
return "The weather is UNGODLY with torrential rains and temperatures below -10°C"
assert "nullable" in get_tool_json_schema(get_weather)["function"]["parameters"]["properties"]["celsius"]
def test_chatmessage_has_model_dumps_json(self):
message = ChatMessage("user", [{"type": "text", "text": "Hello!"}])
data = json.loads(message.model_dump_json())
assert data["content"] == [{"type": "text", "text": "Hello!"}]
@unittest.skipUnless(sys.platform.startswith("darwin"), "requires macOS")
def test_get_mlx_message_no_tool(self):
model = MLXModel(model_id="HuggingFaceTB/SmolLM2-135M-Instruct", max_tokens=10)
messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
output = model(messages, stop_sequences=["great"]).content
assert output.startswith("Hello")
@unittest.skipUnless(sys.platform.startswith("darwin"), "requires macOS")
def test_get_mlx_message_tricky_stop_sequence(self):
# In this test HuggingFaceTB/SmolLM2-135M-Instruct generates the token ">'"
# which is required to test capturing stop_sequences that have extra chars at the end.
model = MLXModel(model_id="HuggingFaceTB/SmolLM2-135M-Instruct", max_tokens=100)
stop_sequence = " print '>"
messages = [{"role": "user", "content": [{"type": "text", "text": f"Please{stop_sequence}'"}]}]
# check our assumption that that ">" is followed by "'"
assert model.tokenizer.vocab[">'"]
assert model(messages, stop_sequences=[]).content == f"I'm ready to help you{stop_sequence}'"
# check stop_sequence capture when output has trailing chars
assert model(messages, stop_sequences=[stop_sequence]).content == "I'm ready to help you"
def test_transformers_message_no_tool(self):
model = TransformersModel(
model_id="HuggingFaceTB/SmolLM2-135M-Instruct",
max_new_tokens=5,
device_map="cpu",
do_sample=False,
)
messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
output = model(messages, stop_sequences=["great"]).content
assert output == "assistant\nHello"
def test_transformers_message_vl_no_tool(self):
from PIL import Image
img = Image.open(Path(get_tests_dir("fixtures")) / "000000039769.png")
model = TransformersModel(
model_id="llava-hf/llava-interleave-qwen-0.5b-hf",
max_new_tokens=5,
device_map="cpu",
do_sample=False,
)
messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}, {"type": "image", "image": img}]}]
output = model(messages, stop_sequences=["great"]).content
assert output == "Hello! How can"
def test_parse_tool_args_if_needed(self):
original_message = ChatMessage(role="user", content=[{"type": "text", "text": "Hello!"}])
parsed_message = parse_tool_args_if_needed(original_message)
assert parsed_message == original_message
def test_parse_json_if_needed(self):
args = "abc"
parsed_args = parse_json_if_needed(args)
assert parsed_args == "abc"
args = '{"a": 3}'
parsed_args = parse_json_if_needed(args)
assert parsed_args == {"a": 3}
args = "3"
parsed_args = parse_json_if_needed(args)
assert parsed_args == 3
args = 3
parsed_args = parse_json_if_needed(args)
assert parsed_args == 3
class TestHfApiModel:
def test_call_with_custom_role_conversions(self):
custom_role_conversions = {MessageRole.USER: MessageRole.SYSTEM}
model = HfApiModel(model_id="test-model", custom_role_conversions=custom_role_conversions)
model.client = MagicMock()
messages = [{"role": "user", "content": "Test message"}]
_ = model(messages)
# Verify that the role conversion was applied
assert model.client.chat_completion.call_args.kwargs["messages"][0]["role"] == "system", (
"role conversion should be applied"
)
@require_run_all
def test_get_hfapi_message_no_tool(self):
model = HfApiModel(model="Qwen/Qwen2.5-Coder-32B-Instruct", max_tokens=10)
messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
model(messages, stop_sequences=["great"])
@require_run_all
def test_get_hfapi_message_no_tool_external_provider(self):
model = HfApiModel(model="Qwen/Qwen2.5-Coder-32B-Instruct", provider="together", max_tokens=10)
messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
model(messages, stop_sequences=["great"])
class TestLiteLLMModel:
@pytest.mark.parametrize(
"model_id, error_flag",
[
("groq/llama-3.3-70b", "Missing API Key"),
("cerebras/llama-3.3-70b", "The api_key client option must be set"),
("mistral/mistral-tiny", "The api_key client option must be set"),
],
)
def test_call_different_providers_without_key(self, model_id, error_flag):
model = LiteLLMModel(model_id=model_id)
messages = [{"role": "user", "content": [{"type": "text", "text": "Test message"}]}]
with pytest.raises(Exception) as e:
# This should raise 401 error because of missing API key, not fail for any "bad format" reason
model(messages)
assert error_flag in str(e)
def test_passing_flatten_messages(self):
model = LiteLLMModel(model_id="groq/llama-3.3-70b", flatten_messages_as_text=False)
assert not model.flatten_messages_as_text
model = LiteLLMModel(model_id="fal/llama-3.3-70b", flatten_messages_as_text=True)
assert model.flatten_messages_as_text
class TestOpenAIServerModel:
def test_client_kwargs_passed_correctly(self):
model_id = "gpt-3.5-turbo"
api_base = "https://api.openai.com/v1"
api_key = "test_api_key"
organization = "test_org"
project = "test_project"
client_kwargs = {"max_retries": 5}
with patch("openai.OpenAI") as MockOpenAI:
_ = OpenAIServerModel(
model_id=model_id,
api_base=api_base,
api_key=api_key,
organization=organization,
project=project,
client_kwargs=client_kwargs,
)
MockOpenAI.assert_called_once_with(
base_url=api_base, api_key=api_key, organization=organization, project=project, max_retries=5
)
def test_get_clean_message_list_basic():
messages = [
{"role": "user", "content": [{"type": "text", "text": "Hello!"}]},
{"role": "assistant", "content": [{"type": "text", "text": "Hi there!"}]},
]
result = get_clean_message_list(messages)
assert len(result) == 2
assert result[0]["role"] == "user"
assert result[0]["content"][0]["text"] == "Hello!"
assert result[1]["role"] == "assistant"
assert result[1]["content"][0]["text"] == "Hi there!"
def test_get_clean_message_list_role_conversions():
messages = [
{"role": "tool-call", "content": [{"type": "text", "text": "Calling tool..."}]},
{"role": "tool-response", "content": [{"type": "text", "text": "Tool response"}]},
]
result = get_clean_message_list(messages, role_conversions={"tool-call": "assistant", "tool-response": "user"})
assert len(result) == 2
assert result[0]["role"] == "assistant"
assert result[0]["content"][0]["text"] == "Calling tool..."
assert result[1]["role"] == "user"
assert result[1]["content"][0]["text"] == "Tool response"
@pytest.mark.parametrize(
"convert_images_to_image_urls, expected_clean_message",
[
(
False,
{
"role": "user",
"content": [
{"type": "image", "image": "encoded_image"},
{"type": "image", "image": "second_encoded_image"},
],
},
),
(
True,
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "data:image/png;base64,encoded_image"}},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,second_encoded_image"}},
],
},
),
],
)
def test_get_clean_message_list_image_encoding(convert_images_to_image_urls, expected_clean_message):
messages = [
{
"role": "user",
"content": [{"type": "image", "image": b"image_data"}, {"type": "image", "image": b"second_image_data"}],
}
]
with patch("smolagents.models.encode_image_base64") as mock_encode:
mock_encode.side_effect = ["encoded_image", "second_encoded_image"]
result = get_clean_message_list(messages, convert_images_to_image_urls=convert_images_to_image_urls)
mock_encode.assert_any_call(b"image_data")
mock_encode.assert_any_call(b"second_image_data")
assert len(result) == 1
assert result[0] == expected_clean_message
def test_get_clean_message_list_flatten_messages_as_text():
messages = [
{"role": "user", "content": [{"type": "text", "text": "Hello!"}]},
{"role": "user", "content": [{"type": "text", "text": "How are you?"}]},
]
result = get_clean_message_list(messages, flatten_messages_as_text=True)
assert len(result) == 1
assert result[0]["role"] == "user"
assert result[0]["content"] == "Hello!How are you?"
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