wehub-resource-sync
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from unittest.mock import MagicMock
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import pytest
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from vllm.entrypoints.openai.chat_completion.protocol import (
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ChatCompletionRequest,
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)
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@pytest.fixture()
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def should_do_global_cleanup_after_test() -> bool:
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return False
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def make_mock_tokenizer(
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vocab: dict[str, int],
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special_tokens: list[str] | None = None,
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) -> MagicMock:
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"""Create a mock tokenizer with the given vocabulary.
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Args:
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vocab: Mapping of token text to token ID.
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special_tokens: Which tokens to mark as special. When ``None``
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(the default), every key in *vocab* is treated as special —
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convenient when the vocab only contains delimiter tokens.
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The returned mock supports get_vocab(), encode(), and decode().
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decode() maps known token IDs back to their text and falls back to
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chr(id) for ASCII IDs or ``<id>`` for others.
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"""
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id_to_text = {v: k for k, v in vocab.items()}
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tokenizer = MagicMock()
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tokenizer.encode.return_value = [1, 2, 3]
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tokenizer.get_vocab.return_value = dict(vocab)
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tokenizer.decode.side_effect = lambda ids: "".join(
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id_to_text.get(i, chr(i) if i < 128 else f"<{i}>") for i in ids
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)
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st = special_tokens if special_tokens is not None else list(vocab.keys())
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tokenizer.all_special_tokens = st
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tokenizer.all_special_ids = [vocab[t] for t in st if t in vocab]
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return tokenizer
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@pytest.fixture
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def mock_request():
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req = MagicMock(spec=ChatCompletionRequest)
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req.tools = []
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req.tool_choice = "auto"
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req.include_reasoning = True
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return req
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