"""Validation and wire helpers for the supported Responses API subset."""

from __future__ import annotations

import json
from dataclasses import dataclass
from typing import Any
from uuid import uuid4

from db.models import OpenAIResponse


LOCKED_FIELDS = {"instructions", "tools", "tool_choice"}
ALLOWED_FIELDS = {
    "input",
    "model",
    "previous_response_id",
    "store",
    "stream",
    "x_interactive_media",
}
UNSUPPORTED_FIELDS = {
    "background",
    "conversation",
    "include",
    "max_output_tokens",
    "parallel_tool_calls",
    "reasoning",
    "temperature",
    "text",
    "top_p",
    "truncation",
}


@dataclass
class ResponsesAPIError(ValueError):
    message: str
    code: str = "invalid_request_error"
    param: str | None = None
    status_code: int = 400

    def __str__(self) -> str:
        return self.message


@dataclass(frozen=True)
class ResponseRequest:
    model: str | None
    previous_response_id: str | None
    input_items: list[dict[str, Any]]
    stream: bool
    dynamic_variables: dict[str, Any]


def error_payload(error: ResponsesAPIError) -> dict[str, Any]:
    return {
        "error": {
            "type": "invalid_request_error",
            "code": error.code,
            "message": error.message,
            "param": error.param,
        }
    }


def _text_content(value: object, *, role: str) -> list[dict[str, str]]:
    expected = "input_text" if role == "user" else "output_text"
    if isinstance(value, str):
        text = value.strip()
        if not text:
            raise ResponsesAPIError("Message content cannot be empty", param="input")
        return [{"type": expected, "text": text}]
    if not isinstance(value, list) or not value:
        raise ResponsesAPIError("Message content must be text", param="input")
    result: list[dict[str, str]] = []
    for part in value:
        if not isinstance(part, dict) or part.get("type") != expected:
            raise ResponsesAPIError(
                f"Only {expected} content is supported", param="input"
            )
        text = str(part.get("text") or "").strip()
        if not text:
            raise ResponsesAPIError("Message text cannot be empty", param="input")
        result.append({"type": expected, "text": text})
    return result


def normalize_input(value: object) -> list[dict[str, Any]]:
    if isinstance(value, str):
        return [
            {
                "type": "message",
                "role": "user",
                "content": _text_content(value, role="user"),
            }
        ]
    if not isinstance(value, list) or not value:
        raise ResponsesAPIError("input must be a non-empty string or array", param="input")

    items: list[dict[str, Any]] = []
    for index, raw in enumerate(value):
        param = f"input[{index}]"
        if not isinstance(raw, dict):
            raise ResponsesAPIError("Input item must be an object", param=param)
        item_type = str(raw.get("type") or "message")
        if item_type == "message":
            role = str(raw.get("role") or "user")
            if role not in {"user", "assistant"}:
                raise ResponsesAPIError(
                    "Message role must be user or assistant", param=f"{param}.role"
                )
            items.append(
                {
                    "type": "message",
                    "role": role,
                    "content": _text_content(raw.get("content"), role=role),
                }
            )
            continue
        if item_type == "function_call_output":
            call_id = str(raw.get("call_id") or "").strip()
            if not call_id:
                raise ResponsesAPIError(
                    "function_call_output requires call_id",
                    param=f"{param}.call_id",
                )
            output = raw.get("output")
            if not isinstance(output, str):
                output = json.dumps(output, ensure_ascii=False, default=str)
            items.append(
                {
                    "type": "function_call_output",
                    "call_id": call_id,
                    "output": output,
                }
            )
            continue
        raise ResponsesAPIError(
            f"Unsupported input item type: {item_type}",
            code="unsupported_parameter",
            param=f"{param}.type",
        )
    return items


def parse_request(body: object) -> ResponseRequest:
    if not isinstance(body, dict):
        raise ResponsesAPIError("Request body must be an object")
    unknown = sorted(set(body) - ALLOWED_FIELDS - LOCKED_FIELDS - UNSUPPORTED_FIELDS)
    if unknown:
        raise ResponsesAPIError(
            f"Unsupported parameter: {unknown[0]}",
            code="unsupported_parameter",
            param=unknown[0],
        )
    locked = sorted(LOCKED_FIELDS.intersection(body))
    if locked:
        raise ResponsesAPIError(
            f"{locked[0]} is owned by the selected assistant",
            code="immutable_configuration",
            param=locked[0],
        )
    unsupported = sorted(UNSUPPORTED_FIELDS.intersection(body))
    if unsupported:
        raise ResponsesAPIError(
            f"Unsupported parameter: {unsupported[0]}",
            code="unsupported_parameter",
            param=unsupported[0],
        )
    if "store" in body and body["store"] is not True:
        raise ResponsesAPIError(
            "store must be true for this stateful API",
            code="unsupported_parameter",
            param="store",
        )
    stream = body.get("stream", False)
    if not isinstance(stream, bool):
        raise ResponsesAPIError("stream must be a boolean", param="stream")
    extension = body.get("x_interactive_media") or {}
    if not isinstance(extension, dict):
        raise ResponsesAPIError(
            "x_interactive_media must be an object", param="x_interactive_media"
        )
    variables = extension.get("variables") or {}
    if not isinstance(variables, dict):
        raise ResponsesAPIError(
            "variables must be an object",
            param="x_interactive_media.variables",
        )
    raw_model = body.get("model")
    if raw_model is not None and not isinstance(raw_model, str):
        raise ResponsesAPIError("model must be a string", param="model")
    raw_previous = body.get("previous_response_id")
    if raw_previous is not None and not isinstance(raw_previous, str):
        raise ResponsesAPIError(
            "previous_response_id must be a string", param="previous_response_id"
        )
    model = (raw_model or "").strip() or None
    previous = (raw_previous or "").strip() or None
    if not model and not previous:
        raise ResponsesAPIError(
            "model is required for the first response", param="model"
        )
    if "input" not in body:
        raise ResponsesAPIError("input is required", param="input")
    return ResponseRequest(
        model=model,
        previous_response_id=previous,
        input_items=normalize_input(body["input"]),
        stream=stream,
        dynamic_variables=dict(variables),
    )


def new_id(prefix: str) -> str:
    return f"{prefix}_{uuid4().hex}"


def response_payload(row: OpenAIResponse) -> dict[str, Any]:
    return {
        "id": row.id,
        "object": "response",
        "created_at": int(row.created_at.timestamp()),
        "completed_at": (
            int(row.completed_at.timestamp()) if row.completed_at else None
        ),
        "status": row.status,
        "error": row.error,
        "incomplete_details": None,
        "instructions": None,
        "model": f"assistant:{row.assistant_id}" if row.assistant_id else None,
        "output": list(row.output_items or []),
        "parallel_tool_calls": False,
        "previous_response_id": row.previous_response_id,
        "store": True,
        "tool_choice": "auto",
        "tools": [],
        "metadata": {},
        "usage": None,
    }