"""OpenAI 兼容的请求/响应模型。"""

from typing import Any

from pydantic import BaseModel, Field

from core.api.conv_parser import strip_session_id_suffix


class OpenAIContentPart(BaseModel):
    type: str
    text: str | None = None
    image_url: dict[str, Any] | str | None = None


class InputAttachment(BaseModel):
    filename: str
    mime_type: str
    data: bytes


class OpenAIMessage(BaseModel):
    role: str = Field(..., description="system | user | assistant | tool")
    content: str | list[OpenAIContentPart] | None = ""
    tool_calls: list[dict[str, Any]] | None = Field(
        default=None, description="assistant 发起的工具调用"
    )
    tool_call_id: str | None = Field(
        default=None, description="tool 消息对应的 call id"
    )

    model_config = {"extra": "allow"}


class OpenAIChatRequest(BaseModel):
    """OpenAI Chat Completions API 兼容请求体。"""

    model: str = Field(default="", description="模型名，可忽略")
    messages: list[OpenAIMessage] = Field(..., description="对话列表")
    stream: bool = Field(default=False, description="是否流式返回")
    tools: list[dict] | None = Field(
        default=None,
        description='工具列表，每项为 {"type":"function","function":{name,description,parameters,strict?}}',
    )
    tool_choice: str | dict | None = Field(
        default=None,
        description='工具选择: "auto"|"required"|"none" 或 {"type":"function","name":"xxx"}',
    )
    parallel_tool_calls: bool | None = Field(
        default=None,
        description="是否允许单次响应中并行多个 tool_call，false 时仅 0 或 1 个",
    )
    resume_session_id: str | None = Field(default=None, exclude=True)
    upstream_model: str | None = Field(default=None, exclude=True)
    attachment_files: list[InputAttachment] = Field(
        default_factory=list,
        exclude=True,
        description="本次实际要发送给站点的附件，由 ChatHandler 根据 full_history 选择来源填充。",
    )
    # 仅供内部调度使用：最后一条 user 消息里的附件 & 所有 user 消息里的附件
    attachment_files_last_user: list[InputAttachment] = Field(
        default_factory=list, exclude=True
    )
    attachment_files_all_users: list[InputAttachment] = Field(
        default_factory=list, exclude=True
    )


def _norm_content(c: str | list[OpenAIContentPart] | None) -> str:
    """将 content 转为单段字符串。仅支持官方格式：字符串或 type=text 的 content part（取 text 字段）。"""
    if c is None:
        return ""
    if isinstance(c, str):
        return strip_session_id_suffix(c)
    if not isinstance(c, list):
        return ""
    return strip_session_id_suffix(
        " ".join(
            p.text or ""
            for p in c
            if isinstance(p, OpenAIContentPart) and p.type == "text" and p.text
        )
    )


REACT_STRICT_SUFFIX = (
    "(严格 ReAct 执行模式;禁止输出「无法执行工具所以直接给方案」等解释或替代内容)"
)


def extract_user_content(
    messages: list[OpenAIMessage],
    *,
    has_tools: bool = False,
    react_prompt_prefix: str = "",
    full_history: bool = False,
) -> str:
    """
    从 messages 中提取对话，拼成发给模型的 prompt。
    网页/会话侧已有完整历史，只取尾部：最后一条为 user 时，从后向前找到最后一个 assistant（不包含），
    取该 assistant 之后到末尾；最后一条为 tool 时，从后向前找到最后一个 user（不包含），取该 user 之后到末尾。
    支持 user、assistant、tool 角色；assistant 的 tool_calls 与 tool 结果会拼回。
    ReAct 模式：完整 ReAct Prompt 仅第一次对话传入（按完整 messages 判断 is_first_turn）；后续只传尾部内容。
    """
    if not messages:
        return ""

    parts: list[str] = []

    # 重建会话时会把完整历史重新回放给站点，因此 tools 指令也需要重新注入。
    is_first_turn = not any(m.role in ("assistant", "tool") for m in messages)
    if has_tools and react_prompt_prefix and (full_history or is_first_turn):
        parts.append(react_prompt_prefix)

    if full_history:
        tail = messages
    else:
        last = messages[-1]
        if last.role == "user":
            i = len(messages) - 1
            while i >= 0 and messages[i].role != "assistant":
                i -= 1
            tail = messages[i + 1 :]
        elif last.role == "tool":
            i = len(messages) - 1
            while i >= 0 and messages[i].role != "user":
                i -= 1
            tail = messages[i + 1 :]
        else:
            tail = messages[-2:]

    for m in tail:
        if m.role == "system":
            txt = _norm_content(m.content)
            if txt:
                parts.append(f"System：{txt}")
        elif m.role == "user":
            txt = _norm_content(m.content)
            if txt:
                if has_tools:
                    parts.append(f"**User**: {txt} {REACT_STRICT_SUFFIX}")
                else:
                    parts.append(f"User：{txt}")
        elif m.role == "assistant":
            tool_calls_list = list(m.tool_calls or [])
            if tool_calls_list:
                for tc in tool_calls_list:
                    fn = tc.get("function") or {}
                    call_id = tc.get("id", "")
                    name = fn.get("name", "")
                    args = fn.get("arguments", "{}")
                    parts.append(
                        f"**Assistant**:\n\n```\nAction: {name}\nAction Input: {args}\nCall ID: {call_id}\n```"
                    )
            else:
                txt = _norm_content(m.content)
                if txt:
                    if has_tools:
                        parts.append(f"**Assistant**:\n\n{txt}")
                    else:
                        parts.append(f"Assistant：{txt}")
        elif m.role == "tool":
            txt = _norm_content(m.content)
            call_id = m.tool_call_id or ""
            parts.append(
                f"**Observation(Call ID: {call_id})**: {txt}\n\n请根据以上观察结果继续。如需调用工具，输出 Thought / Action / Action Input；若任务已完成，输出 Final Answer。"
            )
    return "\n".join(parts)