"""Structured pipeline logger.

Wraps Python logging + Redis SSE streaming for consistent, prefixed log output
from all Celery pipeline tasks.  Every method:
  - emits a Python `logging` line with a [STEP_NAME] prefix
  - publishes to Redis via log_task_event for SSE streaming in the UI
"""
import functools
import logging
import resource
import time
from typing import Any

from app.core.task_logs import log_task_event

_log = logging.getLogger(__name__)


class PipelineLogger:
    """Structured logger for a single pipeline execution context.

    Usage in a Celery task::

        pl = PipelineLogger(task_id=self.request.id, order_line_id=str(line.id))
        pl.step_start("occ_glb_export", {"cad_file_id": cad_file_id})
        ...
        pl.step_done("occ_glb_export", duration_s=8.4, result={"size_bytes": 204800})
    """

    def __init__(self, task_id: str | None, order_line_id: str | None = None):
        self.task_id = task_id or "unknown"
        self.order_line_id = order_line_id
        self._step_starts: dict[str, float] = {}

    # ------------------------------------------------------------------
    # Public API
    # ------------------------------------------------------------------

    def step_start(self, step: str, context: dict[str, Any] | None = None) -> None:
        self._step_starts[step] = time.time()
        msg = f"[{step}] start"
        if context:
            msg += f" | {context}"
        _log.info(msg)
        log_task_event(self.task_id, msg, level="info")

    def step_progress(self, step: str, pct: int, msg: str) -> None:
        full = f"[{step}] {pct}% — {msg}"
        _log.info(full)
        log_task_event(self.task_id, full, level="info")

    def step_done(self, step: str, duration_s: float | None = None, result: dict[str, Any] | None = None) -> None:
        if duration_s is None:
            start = self._step_starts.get(step)
            duration_s = round(time.time() - start, 2) if start else None
        parts = [f"[{step}] done"]
        if duration_s is not None:
            parts.append(f"{duration_s:.1f}s")
        if result:
            parts.append(str(result))
        msg = " | ".join(parts)
        _log.info(msg)
        log_task_event(self.task_id, msg, level="info")

    def step_error(self, step: str, error: str, exc: Exception | None = None) -> None:
        msg = f"[{step}] ERROR — {error}"
        if exc:
            _log.exception(msg)
        else:
            _log.error(msg)
        log_task_event(self.task_id, msg, level="error")

    def info(self, step: str, msg: str) -> None:
        full = f"[{step}] {msg}"
        _log.info(full)
        log_task_event(self.task_id, full, level="info")

    def warning(self, step: str, msg: str) -> None:
        full = f"[{step}] WARNING — {msg}"
        _log.warning(full)
        log_task_event(self.task_id, full, level="warning")

    # ------------------------------------------------------------------
    # Context manager for a single step
    # ------------------------------------------------------------------

    def step(self, step_name: str, context: dict[str, Any] | None = None) -> "_StepContext":
        return _StepContext(self, step_name, context)


class _StepContext:
    """Context manager that auto-calls step_start / step_done / step_error."""

    def __init__(self, pl: PipelineLogger, step_name: str, context: dict | None):
        self._pl = pl
        self._name = step_name
        self._context = context

    def __enter__(self) -> "_StepContext":
        self._pl.step_start(self._name, self._context)
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        if exc_type is None:
            self._pl.step_done(self._name)
        else:
            self._pl.step_error(self._name, str(exc_val), exc_val)
        return False  # do not suppress exceptions


# ---------------------------------------------------------------------------
# @timed_step decorator
# ---------------------------------------------------------------------------

def timed_step(step_name: str, pipeline_logger: PipelineLogger | None = None):
    """Decorator that auto-times a function and logs via PipelineLogger.

    Captures wall-clock duration and peak RSS delta.  If a Redis connection
    is available, stores metrics to ``pipeline:metrics:{context_id}`` as a
    hash field ``{step_name}`` → JSON ``{duration_s, rss_delta_kb}``.

    Usage::

        pl = PipelineLogger(task_id=self.request.id)

        @timed_step("extract_objects", pl)
        def do_extraction(step_path):
            ...

    Or without a logger (metrics still stored to Redis if context_id given)::

        @timed_step("extract_objects")
        def do_extraction(step_path):
            ...
    """
    def decorator(fn):
        @functools.wraps(fn)
        def wrapper(*args, **kwargs):
            pl = pipeline_logger
            rss_before = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
            t0 = time.monotonic()
            if pl:
                pl.step_start(step_name)
            try:
                result = fn(*args, **kwargs)
                duration = round(time.monotonic() - t0, 3)
                rss_after = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
                rss_delta_kb = rss_after - rss_before
                metrics = {
                    "duration_s": duration,
                    "rss_delta_kb": rss_delta_kb,
                }
                if pl:
                    pl.step_done(step_name, duration_s=duration, result=metrics)
                else:
                    _log.info(f"[{step_name}] done | {duration:.1f}s | rss_delta={rss_delta_kb}KB")
                _store_metrics(step_name, metrics, kwargs.get("context_id"))
                return result
            except Exception as exc:
                duration = round(time.monotonic() - t0, 3)
                if pl:
                    pl.step_error(step_name, str(exc), exc)
                else:
                    _log.exception(f"[{step_name}] ERROR — {exc}")
                raise
        return wrapper
    return decorator


def _store_metrics(step_name: str, metrics: dict, context_id: str | None = None) -> None:
    """Store step metrics to Redis hash (best-effort, never raises)."""
    if not context_id:
        return
    try:
        import json
        from app.config import settings
        import redis
        r = redis.from_url(settings.redis_url)
        key = f"pipeline:metrics:{context_id}"
        r.hset(key, step_name, json.dumps(metrics))
        r.expire(key, 86400)  # 24h TTL
    except Exception:
        pass  # metrics storage is non-critical