from __future__ import annotations

import os
import time
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timedelta
from typing import Any

from common import format_modelhub_datetime, parse_datetime
from defaults import EMBEDDED_MODELHUB_XC_TOKEN
from http_json import HttpJsonError, JsonHttpClient


class ModelHubAPIError(RuntimeError):
    def __init__(self, message: str, *, code: int | None = None, payload: Any = None) -> None:
        super().__init__(message)
        self.code = code
        self.payload = payload


class ModelHubClient:
    def __init__(
        self,
        *,
        token: str | None = None,
        base_url: str = "https://modelhub.org.cn",
        timeout: int = 30,
        retries: int = 2,
        http_client: JsonHttpClient | None = None,
    ) -> None:
        self.token = token or os.getenv("MODELHUB_XC_TOKEN") or os.getenv("XC_TOKEN") or EMBEDDED_MODELHUB_XC_TOKEN
        if not self.token and http_client is None:
            raise ValueError("ModelHub token is required. Set MODELHUB_XC_TOKEN or XC_TOKEN.")
        default_headers = {"Xc-Token": self.token} if self.token else {}
        self.http_client = http_client or JsonHttpClient(
            base_url=base_url,
            default_headers=default_headers,
            timeout=timeout,
            retries=retries,
        )

    def search_by_model_id(self, model_id: str) -> dict[str, Any]:
        return self._request("GET", "/api/computility/models/search-by-model-id", query={"modelId": model_id})

    def is_model_processed_for_gpu(self, model_id: str, target_gpu: str) -> bool:
        gpu_result = self.get_verify_result_map(model_id).get(target_gpu)
        if gpu_result is None:
            return False
        return "result" in gpu_result or "records" in gpu_result

    def get_verify_result_map(self, model_id: str) -> dict[str, Any]:
        payload = self.search_by_model_id(model_id)
        return ((payload.get("data") or {}).get("verifyResult") or {})

    def processed_gpus_for_model(self, model_id: str) -> set[str]:
        return set(self.get_verify_result_map(model_id).keys())

    def list_tasks_page(
        self,
        *,
        current: int = 1,
        page_size: int = 50,
        only_mine: bool = True,
        begin_time: datetime | None = None,
        end_time: datetime | None = None,
        gpu_type: str | None = None,
        model_id: str | None = None,
    ) -> dict[str, Any]:
        return self._request(
            "GET",
            "/api/adapt/task/page",
            query={
                "current": current,
                "pageSize": page_size,
                "onlyMine": str(only_mine).lower(),
                "beginTime": format_modelhub_datetime(begin_time) if begin_time else None,
                "endTime": format_modelhub_datetime(end_time) if end_time else None,
                "gpuType": gpu_type,
                "modelId": model_id,
            },
        )

    def list_tasks(
        self,
        *,
        page_size: int = 50,
        only_mine: bool = True,
        begin_time: datetime | None = None,
        end_time: datetime | None = None,
        gpu_type: str | None = None,
        model_id: str | None = None,
    ) -> list[dict[str, Any]]:
        current = 1
        records: list[dict[str, Any]] = []
        while True:
            page = self.list_tasks_page(
                current=current,
                page_size=page_size,
                only_mine=only_mine,
                begin_time=begin_time,
                end_time=end_time,
                gpu_type=gpu_type,
                model_id=model_id,
            )
            page_data = page.get("data") or {}
            page_records = page_data.get("records") or []
            records.extend(page_records)
            pages = int(page_data.get("pages") or 0)
            if pages <= current or not page_records:
                break
            current += 1
        return records

    def add_task(self, payload: dict[str, Any]) -> dict[str, Any]:
        return self._request("POST", "/api/adapt/task/add", data=payload)

    def find_recent_task_id(self, model_id: str, gpu_type: str, submitted_after: datetime) -> str | None:
        recent_tasks = self.list_tasks(
            page_size=20,
            only_mine=True,
            begin_time=submitted_after - timedelta(hours=1),
            end_time=submitted_after + timedelta(hours=6),
            gpu_type=gpu_type,
            model_id=model_id,
        )
        if not recent_tasks:
            return None
        recent_tasks.sort(key=lambda task: parse_datetime(task.get("updateTime")) or submitted_after, reverse=True)
        return str(recent_tasks[0].get("taskId")) if recent_tasks[0].get("taskId") is not None else None

    def count_active_tasks(
        self,
        *,
        page_size: int = 100,
        only_mine: bool = True,
        begin_time: datetime | None = None,
        end_time: datetime | None = None,
        max_count: int | None = None,
    ) -> int:
        """Count active tasks with early pagination termination.

        Unlike list_tasks() which fetches all pages, this method stops
        fetching pages as soon as max_count active tasks are found.
        """
        max_count_int: int | None = max_count
        if max_count_int is not None:
            max_count_int = max(0, int(max_count_int))
            if max_count_int == 0:
                return 0

        active_count = 0
        current = 1
        while True:
            page = self.list_tasks_page(
                current=current,
                page_size=page_size,
                only_mine=only_mine,
                begin_time=begin_time,
                end_time=end_time,
            )
            page_data = page.get("data") or {}
            page_records = page_data.get("records") or []

            for task in page_records:
                if is_active_task(task):
                    active_count += 1
                    if max_count_int is not None and active_count >= max_count_int:
                        return active_count

            pages = int(page_data.get("pages") or 0)
            if pages <= current or not page_records:
                break
            current += 1

        return active_count

    def _request(
        self,
        method: str,
        path: str,
        *,
        query: dict[str, Any] | None = None,
        data: dict[str, Any] | None = None,
    ) -> dict[str, Any]:
        try:
            payload = self.http_client.request_json(method, path, query=query, data=data)
        except HttpJsonError as exc:
            if isinstance(exc.payload, dict) and "message" in exc.payload:
                raise ModelHubAPIError(exc.payload["message"], payload=exc.payload, code=exc.status_code) from exc
            raise ModelHubAPIError(str(exc), payload=exc.payload, code=exc.status_code) from exc
        if not isinstance(payload, dict):
            raise ModelHubAPIError("Unexpected API response shape", payload=payload)
        code = payload.get("code")
        if code != 0:
            raise ModelHubAPIError(payload.get("message") or "ModelHub API request failed", code=code, payload=payload)
        return payload


ACTIVE_TASK_STATUSES = {
    "waiting",
    "running",
    "processing",
    "queued",
    "pending",
    "validating",
    "submitting",
    "initializing",
}

TERMINAL_TASK_STATUSES = {
    "success",
    "failed",
    "error",
    "cancelled",
    "canceled",
    "rejected",
    "timeout",
    "completed",
    "complete",
    "done",
}


def is_active_task(task: dict[str, Any]) -> bool:
    status = str(task.get("status") or "").strip().lower()
    if not status:
        return False
    if status in ACTIVE_TASK_STATUSES:
        return True
    if status in TERMINAL_TASK_STATUSES:
        return False
    return True


class ModelHubClientPool:
    def __init__(self, clients: list[ModelHubClient], *, active_task_cap: int = 100) -> None:
        if not clients:
            raise ValueError("At least one ModelHub client is required")
        self.clients = clients
        self.active_task_cap = active_task_cap
        self._active_counts: list[int] = [0 for _ in clients]
        self._active_refresh_at: float = 0.0
        self._active_counts_ttl: float = 30.0
        self._state_lock = threading.Lock()
        # Per-cycle cache for search_by_model_id results (model_id -> merged verify result map)
        self._verify_cache: dict[str, dict[str, Any]] = {}
        # Single reader client to avoid fanout on read operations
        self._reader = clients[0]

    def _safe_count_active_tasks(self, client: ModelHubClient) -> int:
        try:
            return client.count_active_tasks(max_count=self.active_task_cap, page_size=200)
        except Exception:
            return self.active_task_cap

    def _safe_search_by_model_id(self, client: ModelHubClient, model_id: str) -> dict[str, Any]:
        try:
            payload = client.search_by_model_id(model_id)
            return payload if isinstance(payload, dict) else {}
        except Exception:
            return {}

    def _safe_list_tasks(self, client: ModelHubClient, kwargs: dict[str, Any]) -> list[dict[str, Any]]:
        try:
            return client.list_tasks(**kwargs)
        except Exception:
            return []

    def _refresh_active_counts(self, *, force: bool = False) -> None:
        now = time.time()
        if (not force) and self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
            return

        def _to_indexed_result(index: int, client: ModelHubClient) -> tuple[int, int]:
            return index, self._safe_count_active_tasks(client)

        results: list[tuple[int, int]] = []
        with ThreadPoolExecutor(max_workers=min(len(self.clients), 12)) as executor:
            futures = {executor.submit(_to_indexed_result, index, client): index for index, client in enumerate(self.clients)}
            for future in as_completed(futures):
                index = futures[future]
                try:
                    results.append(future.result())
                except Exception:
                    results.append((index, self.active_task_cap))

        self._active_counts = [0 for _ in self.clients]
        for index, count in sorted(results, key=lambda item: item[0]):
            self._active_counts[index] = count
        # Use current time after refresh completes, not the stale 'now' from function start
        self._active_refresh_at = time.time()

    def active_task_counts(self) -> list[int]:
        with self._state_lock:
            now = time.time()
            if self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
                pass  # use cached counts
            else:
                self._refresh_active_counts()
        return list(self._active_counts)

    def available_submit_slots(self) -> int:
        with self._state_lock:
            # Only refresh if cache is stale (respects TTL) or counts are empty
            now = time.time()
            if self._active_counts and (now - self._active_refresh_at) < self._active_counts_ttl:
                pass  # use cached counts
            else:
                self._refresh_active_counts()
            return sum(max(0, self.active_task_cap - count) for count in self._active_counts)

    def list_tasks(self, **kwargs):  # noqa: ANN003, ANN001
        # Default: use only the reader client to avoid fanout amplification.
        # The fanout_all parameter allows explicit cross-account merging when needed.
        fanout_all = kwargs.pop("_fanout_all", False)
        if not fanout_all:
            return self._safe_list_tasks(self._reader, kwargs)

        merged: list[dict[str, Any]] = []
        seen: set[str] = set()
        with ThreadPoolExecutor(max_workers=min(len(self.clients), 12)) as executor:
            futures = {executor.submit(self._safe_list_tasks, client, kwargs): index for index, client in enumerate(self.clients)}
            for future in as_completed(futures):
                try:
                    tasks = future.result()
                except Exception:
                    tasks = []
                for task in tasks:
                    task_id = str(task.get("taskId")) if task.get("taskId") is not None else None
                    if task_id and task_id in seen:
                        continue
                    if task_id:
                        seen.add(task_id)
                    merged.append(task)
        return merged

    def _read_from_cache(self, model_id: str) -> dict[str, Any] | None:
        cached = self._verify_cache.get(model_id)
        if cached is None:
            return None
        return dict(cached)

    def _write_to_cache(self, model_id: str, payload: dict[str, Any]) -> None:
        self._verify_cache[model_id] = payload

    def search_by_model_id(self, model_id: str) -> dict[str, Any]:
        # Check cache first (per-cycle cache to avoid repeated API calls for the same model)
        with self._state_lock:
            cached = self._read_from_cache(model_id)
        if cached is not None:
            return cached

        # Only query ONE client (the reader) instead of fanning out to all clients.
        # verifyResult is model-specific platform data, not account-specific.
        response = self._safe_search_by_model_id(self._reader, model_id)
        if not isinstance(response, dict):
            response = {"code": 0, "data": {"verifyResult": {}}}

        with self._state_lock:
            self._write_to_cache(model_id, response)
        return response

    def get_verify_result_map(self, model_id: str) -> dict[str, Any]:
        payload = self.search_by_model_id(model_id)
        return ((payload.get("data") or {}).get("verifyResult") or {})

    def processed_gpus_for_model(self, model_id: str) -> set[str]:
        return set(self.get_verify_result_map(model_id).keys())

    def add_task(self, payload: dict[str, Any]) -> dict[str, Any]:
        with self._state_lock:
            self._refresh_active_counts(force=True)
            selected_index = None
            best_remaining = -1
            for index, active_count in enumerate(self._active_counts):
                remaining = self.active_task_cap - active_count
                if remaining > best_remaining:
                    best_remaining = remaining
                    selected_index = index
            if selected_index is None or best_remaining <= 0:
                raise ModelHubAPIError(
                    f"当前等待中或运行中的异步模型验证任务数量已达上限（{self.active_task_cap}）"
                )
            self._active_counts[selected_index] += 1

        selected_client = self.clients[selected_index]
        try:
            response = selected_client.add_task(payload)
        except Exception:
            with self._state_lock:
                self._active_counts[selected_index] -= 1
            raise
        return response

    def list_tasks_page(self, **kwargs):  # noqa: ANN003, ANN001
        """Single-page task listing via the reader client (no fanout)."""
        return self._reader.list_tasks_page(**kwargs)

    def find_recent_task_id(self, model_id: str, gpu_type: str, submitted_after: datetime) -> str | None:
        for client in self.clients:
            task_id = client.find_recent_task_id(model_id, gpu_type, submitted_after)
            if task_id is not None:
                return task_id
        return None