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# -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# 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 collections
import sys
from collections.abc import Iterable
import os
from typing import Any
import unittest
from google.generativeai import client as client_lib
from google.generativeai import generative_models
from google.generativeai.types import content_types
from google.generativeai import protos
from absl.testing import absltest
from absl.testing import parameterized
def simple_response(text: str) -> protos.GenerateContentResponse:
return protos.GenerateContentResponse(
{"candidates": [{"content": {"parts": [{"text": text}]}}]}
)
class AsyncTests(parameterized.TestCase, unittest.IsolatedAsyncioTestCase):
def setUp(self):
self.client = unittest.mock.MagicMock()
client_lib._client_manager.clients["generative_async"] = self.client
def add_client_method(f):
name = f.__name__
setattr(self.client, name, f)
return f
self.observed_requests = []
self.responses = collections.defaultdict(list)
@add_client_method
async def generate_content(
request: protos.GenerateContentRequest,
**kwargs,
) -> protos.GenerateContentResponse:
self.assertIsInstance(request, protos.GenerateContentRequest)
self.observed_requests.append(request)
response = self.responses["generate_content"].pop(0)
return response
@add_client_method
async def stream_generate_content(
request: protos.GetModelRequest,
**kwargs,
) -> Iterable[protos.GenerateContentResponse]:
self.observed_requests.append(request)
response = self.responses["stream_generate_content"].pop(0)
return response
@add_client_method
async def count_tokens(
request: protos.CountTokensRequest,
**kwargs,
) -> Iterable[protos.GenerateContentResponse]:
self.observed_requests.append(request)
response = self.responses["count_tokens"].pop(0)
return response
async def test_basic(self):
# Generate text from text prompt
model = generative_models.GenerativeModel(model_name="gemini-1.5-flash")
self.responses["generate_content"] = [simple_response("world!")]
response = await model.generate_content_async("Hello")
self.assertEqual(self.observed_requests[0].contents[0].parts[0].text, "Hello")
self.assertEqual(response.candidates[0].content.parts[0].text, "world!")
self.assertEqual(response.text, "world!")
async def test_streaming(self):
# Generate text from text prompt
model = generative_models.GenerativeModel(model_name="gemini-1.5-flash")
async def responses():
for c in "world!":
yield simple_response(c)
self.responses["stream_generate_content"] = [responses()]
response = await model.generate_content_async("Hello", stream=True)
it = iter("world!")
async for chunk in response:
c = next(it)
self.assertEqual(chunk.text, c)
self.assertEqual(response.text, "world!")
@parameterized.named_parameters(
dict(
testcase_name="test_FunctionCallingMode_str",
tool_config={"function_calling_config": "any"},
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.ANY,
"allowed_function_names": [],
}
},
),
dict(
testcase_name="test_FunctionCallingMode_int",
tool_config={"function_calling_config": 1},
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.AUTO,
"allowed_function_names": [],
}
},
),
dict(
testcase_name="test_FunctionCallingMode",
tool_config={"function_calling_config": content_types.FunctionCallingMode.NONE},
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.NONE,
"allowed_function_names": [],
}
},
),
dict(
testcase_name="test_protos.FunctionCallingConfig",
tool_config={
"function_calling_config": protos.FunctionCallingConfig(
mode=content_types.FunctionCallingMode.AUTO
)
},
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.AUTO,
"allowed_function_names": [],
}
},
),
dict(
testcase_name="test_FunctionCallingConfigDict",
tool_config={
"function_calling_config": {
"mode": "mode_auto",
"allowed_function_names": ["datetime", "greetings", "random"],
}
},
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.AUTO,
"allowed_function_names": ["datetime", "greetings", "random"],
}
},
),
dict(
testcase_name="test_protos.ToolConfig",
tool_config=protos.ToolConfig(
function_calling_config=protos.FunctionCallingConfig(
mode=content_types.FunctionCallingMode.NONE
)
),
expected_tool_config={
"function_calling_config": {
"mode": content_types.FunctionCallingMode.NONE,
"allowed_function_names": [],
}
},
),
)
async def test_tool_config(self, tool_config, expected_tool_config):
tools = dict(
function_declarations=[
dict(name="datetime", description="Returns the current UTC date and time."),
dict(name="greetings", description="Returns a greeting."),
dict(name="random", description="Returns a random number."),
]
)
self.responses["generate_content"] = [simple_response("echo echo")]
model = generative_models.GenerativeModel("gemini-1.5-flash", tools=tools)
_ = await model.generate_content_async("Hello", tools=[tools], tool_config=tool_config)
req = self.observed_requests[0]
self.assertLen(type(req.tools[0]).to_dict(req.tools[0]).get("function_declarations"), 3)
self.assertEqual(type(req.tool_config).to_dict(req.tool_config), expected_tool_config)
@parameterized.named_parameters(
["basic", "Hello"],
["list", ["Hello"]],
[
"list2",
[{"text": "Hello"}, {"inline_data": {"data": b"PNG!", "mime_type": "image/png"}}],
],
["contents", [{"role": "user", "parts": ["hello"]}]],
)
async def test_count_tokens_smoke(self, contents):
self.responses["count_tokens"] = [protos.CountTokensResponse(total_tokens=7)]
model = generative_models.GenerativeModel("gemini-1.5-flash")
response = await model.count_tokens_async(contents)
self.assertEqual(
type(response).to_dict(response, including_default_value_fields=False),
{"total_tokens": 7},
)
async def test_stream_generate_content_called_with_request_options(self):
self.client.stream_generate_content = unittest.mock.AsyncMock()
request = unittest.mock.ANY
request_options = {"timeout": 120}
model = generative_models.GenerativeModel()
try:
response = await model.generate_content_async(
contents=[""],
stream=True,
request_options=request_options,
)
except StopAsyncIteration:
pass
self.client.stream_generate_content.assert_called_once_with(request, **request_options)
async def test_generate_content_called_with_request_options(self):
self.client.generate_content = unittest.mock.AsyncMock()
request = unittest.mock.ANY
request_options = {"timeout": 120}
model = generative_models.GenerativeModel()
response = await model.generate_content_async(
contents=[""], request_options=request_options
)
self.client.generate_content.assert_called_once_with(request, **request_options)
async def test_count_tokens_called_with_request_options(self):
self.client.count_tokens = unittest.mock.AsyncMock()
request = unittest.mock.ANY
request_options = {"timeout": 120}
model = generative_models.GenerativeModel("gemini-1.5-flash")
response = await model.count_tokens_async(
contents=["Hello?"], request_options=request_options
)
self.client.count_tokens.assert_called_once_with(request, **request_options)
if __name__ == "__main__":
absltest.main()