"""Deterministic model for isolated acceptance; never calls a provider or platform."""

import asyncio
import json
from contextlib import asynccontextmanager
from uuid import uuid4

from pydantic_ai.messages import ModelResponse, TextPart, ToolCallPart, ToolReturnPart, UserPromptPart
from pydantic_ai.models.function import DeltaToolCall, FunctionModel

from tests.research_fake import research_step


async def fake_stream(messages, info):
    latest = max(
        (i for i, m in enumerate(messages) if any(isinstance(p, UserPromptPart) for p in m.parts)), default=0
    )
    text = " ".join(
        str(p.content) for m in messages[latest:] for p in m.parts if isinstance(p, UserPromptPart)
    )
    returns = [p for m in messages[latest:] for p in m.parts if isinstance(p, ToolReturnPart)]
    if any(marker in text for marker in ("研究此输入", "自行选字段研究", "解读研究结果")):
        step = research_step(
            text, returns, [p for m in messages for p in m.parts if isinstance(p, ToolReturnPart)]
        )
        if isinstance(step, str):
            yield step
        else:
            name, args = step
            yield {0: DeltaToolCall(name=name, json_args=json.dumps(args), tool_call_id=uuid4().hex)}
        return
    if returns and returns[-1].tool_name == "prepare_backtest":
        content = returns[-1].content
        content = json.loads(content) if isinstance(content, str) else content
        yield {
            0: DeltaToolCall(
                name="start_backtest",
                json_args=json.dumps(
                    {
                        "preview_id": content["preview_id"],
                        "version": 1,
                        "idempotency_key": content["preview_id"],
                    }
                ),
                tool_call_id=uuid4().hex,
            )
        }
        return
    if returns and "LOOP" not in text:
        if returns[-1].tool_name == "capability_probe":
            yield str(returns[-1].content)
        else:
            yield "操作结果已返回。"
            yield "请查看下方业务记录与数据来源。"
        return
    if any(t.name == "capability_probe" for t in info.function_tools):
        name, args = "capability_probe", {}
    elif "READY" in text:
        yield "REA"
        yield "DY"
        return
    elif "SLOW" in text:
        yield "正在查询"
        await asyncio.sleep(2)
        yield "，查询完成。"
        return
    elif "回测" in text:
        name, args = (
            "prepare_backtest",
            {
                "inline": {
                    "name": "AI 固定回测",
                    "source": {"kind": "ai"},
                    "candidates": [
                        {
                            "client_item_id": "ai-1",
                            "expression": "rank(close)",
                            "settings": {"region": "USA", "universe": "TOP3000", "delay": 1},
                        }
                    ],
                }
            },
        )
    elif "批量" in text:
        name, args = "bulk_update_research", {"alpha_ids": ["a0000", "a0001"], "add_tags": ["AI"]}
    elif "修改" in text or "update" in text:
        alpha_id = "TEST0001" if "TEST0001" in text else "a0000"
        name, args = "update_research", {"alpha_id": alpha_id, "changes": {"note": "AI 测试研究记录"}}
    elif "同步" in text:
        name, args = "create_sync_job", {"kind": "full_sync"}
    elif "PnL" in text:
        name, args = "get_alpha_pnl", {"alpha_id": "TEST0001" if "TEST" in text else "a0000"}
    else:
        name, args = "search_alphas", {"filters": {"turnover_max": 0.15, "limit": 5}}
    yield {0: DeltaToolCall(name=name, json_args=json.dumps(args), tool_call_id=uuid4().hex)}


def fake_structured(messages, info):
    if not info.output_tools:
        return ModelResponse(parts=[TextPart("READY")])
    tool = info.output_tools[0]
    context = next(
        (json.loads(p.content) for m in reversed(messages) for p in m.parts if isinstance(p, UserPromptPart)),
        {},
    )
    fields = [name for name, kind in context.get("fields", {}).items() if kind == "MATRIX"][:2]
    template = {
        "name": "合成流水线模板",
        "description": "合成模型研究假设",
        "expression": "rank({field})",
        "variables": {
            "field": {"kind": "field", "field_type": "MATRIX", "values": fields or ["TEST_FIN_001"]}
        },
    }
    properties = tool.parameters_json_schema.get("properties", {})
    if "summary" in properties:
        data = {"summary": "合成评估建议", "risks": ["仅供验收"], "suggestions": ["继续核实缺失证据"]}
    elif "input_ids" in properties:
        data = {
            "name": "合成特征方案",
            "hypothesis": context.get("hypothesis", "合成假设"),
            "input_ids": [i["id"] for i in context.get("inputs", [])],
            "steps": [],
            "template": template,
        }
    else:
        data = template
    return ModelResponse(parts=[ToolCallPart(tool.name, data)])


@asynccontextmanager
async def fake_model(config, settings):
    yield FunctionModel(function=fake_structured, stream_function=fake_stream, model_name="test-model")