from __future__ import annotations

import html
import json
import re
import time
import uuid
from collections.abc import Callable, Iterable, Iterator
from dataclasses import dataclass
from typing import Any

from services.account_service import account_service
from services.openai_backend_api import OpenAIBackendAPI
from services.protocol.conversation import count_message_tokens, count_text_tokens, normalize_messages
from services.protocol.openai_v1_chat_complete import collect_chat_content, stream_text_chat_completion

XML_TOOL_RULE = """Tool output adapter: when calling tools, output ONLY this XML and no prose/markdown:
<tool_calls><tool_call><tool_name>TOOL_NAME</tool_name><parameters><PARAM><![CDATA[value]]></PARAM></parameters></tool_call></tool_calls>"""


@dataclass
class MessageRequest:
    backend: OpenAIBackendAPI
    messages: list[dict[str, Any]]
    model: str
    tools: Any = None


def _tool_meta(tool: dict[str, object]) -> tuple[str, str, object]:
    fn = tool.get("function") if isinstance(tool.get("function"), dict) else {}
    name = str(tool.get("name") or fn.get("name") or "").strip()
    desc = str(tool.get("description") or fn.get("description") or "").strip()
    schema = tool.get("input_schema") or tool.get("parameters") or fn.get("input_schema") or fn.get("parameters") or {}
    return name, desc, schema


def build_tool_prompt(tools: object) -> str:
    if not isinstance(tools, list):
        return ""
    blocks = []
    for tool in tools:
        if not isinstance(tool, dict):
            continue
        name, desc, schema = _tool_meta(tool)
        if name:
            blocks.append(f"Tool: {name}\nDescription: {desc}\nParameters: {json.dumps(schema, ensure_ascii=False)}")
    if not blocks:
        return ""
    return "Available tools:\n" + "\n".join(blocks) + """

Tool use rules:
- If the user asks to list/read/search files, inspect project state, run a command, or answer from local code, you MUST call a suitable tool first. Do not say you cannot access files.
- To call tools, output ONLY XML and no prose/markdown:
<tool_calls><tool_call><tool_name>TOOL_NAME</tool_name><parameters><PARAM><![CDATA[value]]></PARAM></parameters></tool_call></tool_calls>
- Put parameters under <parameters> using the exact schema names.
""".strip()


def merge_system(system: object, extra: str) -> object:
    system = compact_system(system)
    if _has_claude_code_system(system):
        extra = XML_TOOL_RULE
    if not extra:
        return system
    if isinstance(system, str) and system.strip():
        return f"{system.strip()}\n\n{extra}"
    if isinstance(system, list):
        return [*system, {"type": "text", "text": extra}]
    return extra


def _has_claude_code_system(system: object) -> bool:
    if isinstance(system, str):
        return "You are Claude Code" in system
    if isinstance(system, list):
        return any(isinstance(item, dict) and "You are Claude Code" in str(item.get("text") or "") for item in system)
    return False


def compact_system(system: object) -> object:
    if isinstance(system, str):
        return _compact_system_text(system)
    if isinstance(system, list):
        result = []
        for item in system:
            if isinstance(item, dict) and str(item.get("type") or "") == "text":
                copied = dict(item)
                copied["text"] = _compact_system_text(str(item.get("text") or ""))
                result.append(copied)
            else:
                result.append(item)
        return result
    return system


def _compact_system_text(text: str) -> str:
    return text or ""


def _compact_message_text(text: str) -> str:
    return text or ""


def preprocess_payload(payload: dict[str, object], text_mapper: Callable[[str], str] | None = None) -> dict[str, object]:
    payload["messages"] = preprocess_messages(payload.get("messages"), text_mapper)
    payload["system"] = merge_system(payload.get("system"), build_tool_prompt(payload.get("tools")))
    return payload


def message_request(body: dict[str, Any]) -> MessageRequest:
    payload = preprocess_payload(dict(body))
    model = str(payload.get("model") or "auto").strip() or "auto"
    return MessageRequest(
        backend=OpenAIBackendAPI(access_token=account_service.get_text_access_token(model=model)),
        messages=normalize_messages(payload.get("messages"), payload.get("system")),
        model=model,
        tools=payload.get("tools"),
    )


def preprocess_messages(messages: object, text_mapper: Callable[[str], str] | None = None) -> object:
    if not isinstance(messages, list):
        return messages
    mapper = text_mapper or (lambda text: text)
    result = []
    for message in messages:
        if not isinstance(message, dict):
            continue
        item = dict(message)
        content = item.get("content")
        if isinstance(content, str):
            item["content"] = _compact_message_text(mapper(content))
        elif isinstance(content, list):
            item["content"] = [_preprocess_block(block, mapper) for block in content]
        result.append(item)
    return result


def _preprocess_block(block: object, text_mapper: Callable[[str], str]) -> object:
    if not isinstance(block, dict):
        return block
    block_type = str(block.get("type") or "")
    if block_type == "text":
        item = dict(block)
        item["text"] = _compact_message_text(text_mapper(str(block.get("text") or "")))
        return item
    if block_type == "tool_use":
        return {"type": "text", "text": f"<tool_calls><tool_call><tool_name>{block.get('name') or ''}</tool_name><parameters>{json.dumps(block.get('input') or {}, ensure_ascii=False)}</parameters></tool_call></tool_calls>"}
    if block_type == "tool_result":
        return {"type": "text", "text": f"Tool result {block.get('tool_use_id') or ''}: {block.get('content') or ''}"}
    return block


def message_response(model: str, text: str, input_tokens: int, output_tokens: int, tools: object = None) -> dict[str, object]:
    content, stop_reason = content_blocks(text, tools)
    return {
        "id": f"msg_{uuid.uuid4()}",
        "type": "message",
        "role": "assistant",
        "model": model,
        "content": content,
        "stop_reason": stop_reason,
        "stop_sequence": None,
        "usage": {"input_tokens": input_tokens, "output_tokens": output_tokens},
    }


def content_blocks(text: str, tools: object = None) -> tuple[list[dict[str, object]], str]:
    calls = parse_tool_calls(text) if isinstance(tools, list) and tools else []
    text = strip_tool_markup(text)
    if calls:
        content = ([{"type": "text", "text": text}] if text else []) + [{"type": "tool_use", "id": f"toolu_{uuid.uuid4()}", "name": name, "input": args} for name, args in calls]
        return content, "tool_use"
    return [{"type": "text", "text": text}], "end_turn"


def strip_tool_markup(text: str) -> str:
    return re.sub(r"(?is)<tool_calls\b[^>]*>.*?</tool_calls>|<tool_call\b[^>]*>.*?</tool_call>|<function_call\b[^>]*>.*?</function_call>|<invoke\b[^>]*>.*?</invoke>", "", text or "").strip()


def streamable_text(text: str) -> str:
    text = text or ""
    match = re.search(r"(?is)<tool_calls\b|<tool_call\b|<function_call\b|<invoke\b", text)
    return text[:match.start()].rstrip() if match else text


def parse_tool_calls(text: str) -> list[tuple[str, dict[str, object]]]:
    text = re.sub(r"(?is)```.*?```", "", text or "").strip()
    blocks = re.findall(r"(?is)<tool_call\b[^>]*>(.*?)</tool_call>|<function_call\b[^>]*>(.*?)</function_call>|<invoke\b[^>]*>(.*?)</invoke>", text)
    result = []
    for block in (next((part for part in match if part), "") for match in blocks):
        name = xml_value(block, "tool_name") or xml_value(block, "name") or xml_value(block, "function")
        params = xml_value(block, "parameters") or xml_value(block, "input") or xml_value(block, "arguments") or "{}"
        if name:
            result.append((name, parse_tool_params(params)))
    return result


def xml_value(text: str, tag: str) -> str:
    match = re.search(rf"(?is)<{tag}\b[^>]*>(.*?)</{tag}>", text)
    if not match:
        return ""
    value = match.group(1).strip()
    cdata = re.fullmatch(r"(?is)<!\[CDATA\[(.*?)]]>", value)
    return html.unescape(cdata.group(1) if cdata else value).strip()


def parse_tool_params(raw: str) -> dict[str, object]:
    raw = raw.strip()
    try:
        parsed = json.loads(raw)
        return parsed if isinstance(parsed, dict) else {}
    except Exception:
        return {m.group(1): parse_tool_value(m.group(2)) for m in re.finditer(r"(?is)<([\w.-]+)\b[^>]*>(.*?)</\1>", raw)}


def parse_tool_value(raw: str) -> object:
    value = xml_value(f"<x>{raw}</x>", "x")
    try:
        return json.loads(value)
    except Exception:
        return value


def stream_events(chunks: Iterable[dict[str, object]], model: str, input_tokens: int, output_tokens: Callable[[str], int], tools: object = None) -> Iterator[dict[str, object]]:
    message_id = f"msg_{uuid.uuid4()}"
    created = int(time.time())
    current_text = ""
    streamed_text = ""
    tool_mode = isinstance(tools, list) and bool(tools)
    tool_started = False
    text_open = False
    yield {"type": "message_start", "message": {"id": message_id, "type": "message", "role": "assistant", "model": model, "content": [], "stop_reason": None, "stop_sequence": None, "usage": {"input_tokens": input_tokens, "output_tokens": 0}}}
    if not tool_mode:
        text_open = True
        yield {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}
    for chunk in chunks:
        choice = (chunk.get("choices") or [{}])[0]
        delta = choice.get("delta") or {}
        text_delta = delta.get("content", "") if isinstance(delta, dict) else ""
        if text_delta:
            current_text += text_delta
            if not tool_started:
                visible_text = current_text if not tool_mode else streamable_text(current_text)
                if visible_text.startswith(streamed_text):
                    text_delta = visible_text[len(streamed_text):]
                    if text_delta:
                        if not text_open:
                            text_open = True
                            yield {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}
                        streamed_text = visible_text
                        yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": text_delta}}
                tool_started = tool_mode and visible_text != current_text
        if choice.get("finish_reason"):
            content, stop_reason = content_blocks(current_text, tools)
            if text_open:
                yield {"type": "content_block_stop", "index": 0}
            if stop_reason == "tool_use":
                start_index = 1 if text_open else 0
                if content and content[0]["type"] == "text":
                    remaining = str(content[0].get("text") or "")[len(streamed_text):]
                    if remaining:
                        if not text_open:
                            yield {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}
                        yield {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": remaining}}
                        if not text_open:
                            yield {"type": "content_block_stop", "index": 0}
                    start_index = 1
                    content = content[1:]
                yield from _stream_buffered_blocks(content, start_index)
            yield {"type": "message_delta", "delta": {"stop_reason": stop_reason, "stop_sequence": None}, "usage": {"output_tokens": output_tokens(current_text)}}
            break
    yield {"type": "message_stop", "created": created}


def _stream_buffered_blocks(content: list[dict[str, object]], start_index: int = 0) -> Iterator[dict[str, object]]:
    for offset, block in enumerate(content):
        index = start_index + offset
        if block["type"] == "tool_use":
            start = {"type": "tool_use", "id": block["id"], "name": block["name"], "input": {}}
            delta = {"type": "input_json_delta", "partial_json": json.dumps(block.get("input") or {}, ensure_ascii=False)}
        else:
            start = {"type": "text", "text": ""}
            delta = {"type": "text_delta", "text": block.get("text") or ""}
        yield {"type": "content_block_start", "index": index, "content_block": start}
        yield {"type": "content_block_delta", "index": index, "delta": delta}
        yield {"type": "content_block_stop", "index": index}


def handle(body: dict[str, Any]) -> dict[str, Any] | Iterator[dict[str, Any]]:
    request = message_request(body)
    if body.get("stream"):
        return stream_events(
            stream_text_chat_completion(request.backend, request.messages, request.model),
            request.model,
            count_message_tokens(request.messages, request.model),
            lambda text: count_text_tokens(text, request.model),
            request.tools,
        )
    text = collect_chat_content(stream_text_chat_completion(request.backend, request.messages, request.model))
    return message_response(
        request.model,
        text,
        count_message_tokens(request.messages, request.model),
        count_text_tokens(text, request.model),
        request.tools,
    )