chore: import upstream snapshot with attribution

tests/parser/engine/conftest.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+
+
+@pytest.fixture()
+def should_do_global_cleanup_after_test() -> bool:
+    return False
+
+
+def make_mock_tokenizer(
+    vocab: dict[str, int],
+    special_tokens: list[str] | None = None,
+) -> MagicMock:
+    """Create a mock tokenizer with the given vocabulary.
+
+    Args:
+        vocab: Mapping of token text to token ID.
+        special_tokens: Which tokens to mark as special.  When ``None``
+            (the default), every key in *vocab* is treated as special —
+            convenient when the vocab only contains delimiter tokens.
+
+    The returned mock supports get_vocab(), encode(), and decode().
+    decode() maps known token IDs back to their text and falls back to
+    chr(id) for ASCII IDs or ``<id>`` for others.
+    """
+    id_to_text = {v: k for k, v in vocab.items()}
+    tokenizer = MagicMock()
+    tokenizer.encode.return_value = [1, 2, 3]
+    tokenizer.get_vocab.return_value = dict(vocab)
+    tokenizer.decode.side_effect = lambda ids: "".join(
+        id_to_text.get(i, chr(i) if i < 128 else f"<{i}>") for i in ids
+    )
+    st = special_tokens if special_tokens is not None else list(vocab.keys())
+    tokenizer.all_special_tokens = st
+    tokenizer.all_special_ids = [vocab[t] for t in st if t in vocab]
+    return tokenizer
+
+
+@pytest.fixture
+def mock_request():
+    req = MagicMock(spec=ChatCompletionRequest)
+    req.tools = []
+    req.tool_choice = "auto"
+    req.include_reasoning = True
+    return req