chore: import upstream snapshot with attribution

libs/python/agent/cua_agent/integrations/hud/proxy.py

@@ -0,0 +1,297 @@
+"""HUD ComputerAgent wrapper and Fake AsyncOpenAI client.
+
+Provides FakeAsyncOpenAI that adapts our ComputerAgent to the OpenAI Responses
+interface needed by HUD's OperatorAgent. It implements only `responses.create`
+and returns an OpenAI Response object with `id` and `output` fields, where `output` is a list of
+OpenAI-like response blocks. We intentionally only support a single-step call
+by consuming the first yielded result from `ComputerAgent.run()`.
+"""
+
+import time
+import traceback
+import uuid
+from typing import Any, Dict, List, Optional
+
+from cua_agent.agent import ComputerAgent as BaseComputerAgent
+from cua_agent.callbacks import PromptInstructionsCallback
+from hud.agents import OperatorAgent
+from hud.tools.computer.settings import computer_settings
+
+# OpenAI Responses typed models (required)
+from openai.types.responses import (
+    Response,
+    ResponseComputerToolCall,
+    ResponseInputParam,
+    ResponseOutputItem,
+    ResponseOutputMessage,
+    ResponseOutputText,
+    ResponseReasoningItem,
+    ResponseUsage,
+)
+from PIL import Image
+
+
+def _map_agent_output_to_openai_blocks(
+    output_items: List[Dict[str, Any]],
+) -> List[ResponseOutputItem]:
+    """Map our agent output items to OpenAI ResponseOutputItem typed models.
+
+    Only a subset is supported: computer_call, assistant message (text), and reasoning.
+    Unknown types are ignored.
+    """
+    blocks: List[ResponseOutputItem] = []
+    for item in output_items or []:
+        t = item.get("type")
+        if t == "computer_call":
+            comp = ResponseComputerToolCall.model_validate(
+                {
+                    "id": item.get("id") or f"cu_{uuid.uuid4().hex}",
+                    "type": "computer_call",
+                    "call_id": item["call_id"],
+                    "action": item["action"],
+                    "pending_safety_checks": item.get("pending_safety_checks", []),
+                    "status": "completed",
+                }
+            )
+            blocks.append(comp)
+            # we will exit early here as the responses api only supports a single step
+            break
+        elif t == "message" and item.get("role") == "assistant":
+            content_blocks: List[ResponseOutputText] = []
+            for c in item.get("content", []) or []:
+                content_blocks.append(
+                    ResponseOutputText.model_validate(
+                        {
+                            "type": "output_text",
+                            "text": c["text"],
+                            "annotations": [],
+                        }
+                    )
+                )
+            if content_blocks:
+                msg = ResponseOutputMessage.model_validate(
+                    {
+                        "id": item.get("id") or f"msg_{uuid.uuid4()}",
+                        "type": "message",
+                        "role": "assistant",
+                        "status": "completed",
+                        "content": [ct.model_dump() for ct in content_blocks],
+                    }
+                )
+                blocks.append(msg)
+        elif t == "reasoning":
+            reasoning = ResponseReasoningItem.model_validate(
+                {
+                    "id": item.get("id") or f"rsn_{uuid.uuid4()}",
+                    "type": "reasoning",
+                    "summary": item["summary"],
+                }
+            )
+            blocks.append(reasoning)
+        # Unhandled types are ignored
+    return blocks
+
+
+def _to_plain_dict_list(items: Any) -> List[Dict[str, Any]]:
+    out: List[Dict[str, Any]] = []
+    for it in list(items):
+        if hasattr(it, "model_dump"):
+            out.append(it.model_dump())  # type: ignore[attr-defined]
+        elif isinstance(it, dict):
+            out.append(it)
+        else:
+            # Strict: rely on default __dict__ if present
+            out.append(dict(it))  # may raise if not mapping
+    return out
+
+
+class FakeAsyncOpenAI:
+    """Minimal fake OpenAI client with only `responses.create` implemented.
+
+    It uses a provided `ComputerAgent` instance to produce a single-step
+    response compatible with HUD's OperatorAgent loop.
+    """
+
+    def __init__(self, computer_agent: BaseComputerAgent) -> None:
+        self._agent = computer_agent
+        self.responses = self._Responses(self)
+
+    class _Responses:
+        def __init__(self, parent: "FakeAsyncOpenAI") -> None:
+            # Caches for cross-call context when using previous_response_id
+            self.blocks_cache: Dict[str, ResponseInputParam | ResponseOutputItem] = {}
+            self.context_cache: Dict[str, List[str]] = {}
+            self.agent = parent._agent
+
+        async def create(
+            self,
+            *,
+            model: str,
+            input: ResponseInputParam,
+            tools: Optional[List[Dict[str, Any]]] = None,
+            instructions: Optional[str] = None,
+            previous_response_id: Optional[str] = None,
+            max_retries: int = 5,
+            **_: Any,
+        ) -> Any:
+            for attempt in range(max_retries):
+                # Prepend cached blocks from previous_response_id to input
+                full_input = input
+                if previous_response_id is not None:
+                    prev_block_ids = self.context_cache[previous_response_id]
+                    prev_blocks = [self.blocks_cache[b_id] for b_id in prev_block_ids]
+                    full_input = _to_plain_dict_list(prev_blocks + input)
+
+                # Pre-pend instructions message
+                effective_input = full_input
+                if instructions:
+                    effective_input = [
+                        {
+                            "role": "user",
+                            "content": instructions,
+                        }
+                    ] + full_input
+
+                # Run a single iteration of the ComputerAgent
+                agent_result: Optional[Dict[str, Any]] = None
+                async for result in self.agent.run(effective_input):  # type: ignore[arg-type]
+                    agent_result = result
+                    break
+                assert agent_result is not None, "Agent failed to produce result"
+
+                output = _map_agent_output_to_openai_blocks(agent_result["output"])
+                usage = agent_result["usage"]
+
+                # Cache conversation context using the last response id
+                block_ids: List[str] = []
+                blocks_to_cache = full_input + output
+                for b in blocks_to_cache:
+                    bid = getattr(b, "id", None) or f"tmp-{hash(repr(b))}"
+                    self.blocks_cache[bid] = b  # type: ignore[assignment]
+                    block_ids.append(bid)
+                response_id = agent_result.get("id") or f"fake-{int(time.time()*1000)}"
+                self.context_cache[response_id] = block_ids
+
+                try:
+                    return Response.model_validate(
+                        {
+                            "id": response_id,
+                            "created_at": time.time(),
+                            "object": "response",
+                            "model": model,
+                            "output": output,
+                            "parallel_tool_calls": False,
+                            "tool_choice": "auto",
+                            "tools": [],
+                            "previous_response_id": previous_response_id,
+                            "usage": ResponseUsage.model_validate(
+                                {
+                                    "input_tokens": usage.get("input_tokens", 0),
+                                    "output_tokens": usage.get("output_tokens", 0),
+                                    "total_tokens": usage.get("total_tokens", 0),
+                                    "input_tokens_details": usage.get(
+                                        "input_tokens_details", {"cached_tokens": 0}
+                                    ),
+                                    "output_tokens_details": usage.get(
+                                        "output_tokens_details", {"reasoning_tokens": 0}
+                                    ),
+                                }
+                            ),
+                        }
+                    )
+                except Exception as e:
+                    print(
+                        f"Error while validating agent response (attempt {attempt + 1}/{max_retries}): ",
+                        e,
+                    )
+                    if attempt == max_retries - 1:
+                        print(traceback.format_exc())
+                        raise e
+
+
+# ---------------------------------------------------------------------------
+# Proxy OperatorAgent (moved from __init__.py)
+# ---------------------------------------------------------------------------
+
+
+class ProxyOperatorAgent(OperatorAgent):
+    """OperatorAgent that proxies model calls through our ComputerAgent.
+
+    Accepts the same config keys we pass via hud.run_dataset `agent_config`:
+    - model: str | None
+    - allowed_tools: list[str] | None
+    Additional kwargs are forwarded to OperatorAgent (if any are supported).
+    """
+
+    def __init__(
+        self,
+        *,
+        model: str | None = None,
+        allowed_tools: list[str] | None = None,
+        trajectory_dir: str | dict | None = None,
+        # === ComputerAgent kwargs ===
+        tools: list[Any] | None = None,
+        custom_loop: Any | None = None,
+        only_n_most_recent_images: int | None = None,
+        callbacks: list[Any] | None = None,
+        instructions: str | None = None,
+        verbosity: int | None = None,
+        max_retries: int | None = 3,
+        screenshot_delay: float | int = 0.5,
+        use_prompt_caching: bool | None = False,
+        max_trajectory_budget: float | dict | None = None,
+        telemetry_enabled: bool | None = True,
+        **kwargs: Any,
+    ) -> None:
+        model = model or "computer-use-preview"
+        allowed_tools = allowed_tools or ["openai_computer"]
+
+        computer_shim = {
+            "screenshot": lambda: Image.new(
+                "RGB",
+                (computer_settings.OPENAI_COMPUTER_WIDTH, computer_settings.OPENAI_COMPUTER_HEIGHT),
+            ),
+            "environment": "linux",
+            "dimensions": (
+                computer_settings.OPENAI_COMPUTER_WIDTH,
+                computer_settings.OPENAI_COMPUTER_HEIGHT,
+            ),
+        }
+        # Build tools ensuring the computer_shim is included
+        agent_tools: list[Any] = [computer_shim]
+        if tools:
+            agent_tools.extend(tools)
+
+        # Build callbacks, injecting prompt instructions if provided
+        agent_callbacks = list(callbacks or [])
+        if instructions:
+            agent_callbacks.append(PromptInstructionsCallback(instructions))
+
+        computer_agent = BaseComputerAgent(
+            model=model,
+            tools=agent_tools,
+            custom_loop=custom_loop,
+            only_n_most_recent_images=only_n_most_recent_images,
+            callbacks=agent_callbacks,
+            verbosity=verbosity,
+            trajectory_dir=trajectory_dir,
+            max_retries=max_retries,
+            screenshot_delay=screenshot_delay,
+            use_prompt_caching=use_prompt_caching,
+            max_trajectory_budget=max_trajectory_budget,
+            telemetry_enabled=telemetry_enabled,
+        )
+        model_client = FakeAsyncOpenAI(computer_agent)
+
+        super().__init__(
+            model_client=model_client,  # type: ignore[arg-type]
+            model=model,
+            allowed_tools=allowed_tools,
+            **kwargs,
+        )
+
+
+__all__ = [
+    "FakeAsyncOpenAI",
+    "ProxyOperatorAgent",
+]