File size: 2,597 Bytes
4ff79c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | # SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
from datetime import datetime
from typing import Iterator
from unittest.mock import MagicMock, patch
import pytest
from openai import Stream
from openai.types.chat import ChatCompletionChunk
from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta
@pytest.fixture
def mock_auto_tokenizer():
"""
In the original mock_auto_tokenizer fixture, we were mocking the transformers.AutoTokenizer.from_pretrained
method directly, but we were not providing a return value for this method. Therefore, when from_pretrained
was called within HuggingFaceTGIChatGenerator, it returned None because that's the default behavior of a
MagicMock object when a return value isn't specified.
We will update the mock_auto_tokenizer fixture to return a MagicMock object when from_pretrained is called
in another PR. For now, we will use this fixture to mock the AutoTokenizer.from_pretrained method.
"""
with patch("transformers.AutoTokenizer.from_pretrained", autospec=True) as mock_from_pretrained:
mock_tokenizer = MagicMock()
mock_from_pretrained.return_value = mock_tokenizer
yield mock_tokenizer
@pytest.fixture
def mock_chat_completion_chunk():
"""
Mock the OpenAI API completion chunk response and reuse it for tests
"""
class MockStream(Stream[ChatCompletionChunk]):
def __init__(self, mock_chunk: ChatCompletionChunk, client=None, *args, **kwargs):
client = client or MagicMock()
super().__init__(client=client, *args, **kwargs)
self.mock_chunk = mock_chunk
def __stream__(self) -> Iterator[ChatCompletionChunk]:
# Yielding only one ChatCompletionChunk object
yield self.mock_chunk
with patch("openai.resources.chat.completions.Completions.create") as mock_chat_completion_create:
completion = ChatCompletionChunk(
id="foo",
model="gpt-4",
object="chat.completion.chunk",
choices=[
Choice(
finish_reason="stop", logprobs=None, index=0, delta=ChoiceDelta(content="Hello", role="assistant")
)
],
created=int(datetime.now().timestamp()),
usage={"prompt_tokens": 57, "completion_tokens": 40, "total_tokens": 97},
)
mock_chat_completion_create.return_value = MockStream(completion, cast_to=None, response=None, client=None)
yield mock_chat_completion_create
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