# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

import asyncio
import json
import logging
from typing import List, Optional, Tuple

import sglang as sgl
import zmq
import zmq.asyncio
from prometheus_client import CollectorRegistry, multiprocess
from sglang.srt.utils import get_local_ip_auto, get_zmq_socket

from dynamo.common.utils.prometheus import register_engine_metrics_callback
from dynamo.llm import (
    ForwardPassMetrics,
    KvStats,
    SpecDecodeStats,
    WorkerMetricsPublisher,
    WorkerStats,
    ZmqKvEventPublisher,
    ZmqKvEventPublisherConfig,
)
from dynamo.runtime import Component, Endpoint
from dynamo.sglang.args import Config


class DynamoSglangPublisher:
    """
    Handles SGLang kv events and metrics reception and publishing.
    """

    def __init__(
        self,
        engine: sgl.Engine,
        config: Config,
        component: Component,
        generate_endpoint: Endpoint,
        metrics_labels: Optional[List[Tuple[str, str]]] = None,
    ) -> None:
        """Initialize the SGLang publisher for metrics and KV events.

        Args:
            engine: The SGLang engine instance.
            config: SGLang configuration including server args.
            component: The Dynamo runtime component.
            generate_endpoint: The Dynamo endpoint for generation requests.
            metrics_labels: Optional list of label key-value pairs for metrics.
        """
        self.engine = engine
        self.server_args = config.server_args
        self.generate_endpoint = generate_endpoint
        self.component = component
        self.metrics_publisher = WorkerMetricsPublisher()
        self.metrics_publisher.create_endpoint(component, metrics_labels)

        # Set default values (can be overridden later if needed)
        self.request_total_slots = 1024
        self.dp_rank = 0
        # TODO: Get actual GPU blocks from SGLang engine instead of hardcoded value
        # This hardcoded value causes dynamo_component_kvstats_total_blocks to be incorrect.
        self.num_gpu_block = 1024

        # ZMQ setup for receiving scheduler metrics
        self._ctx = zmq.asyncio.Context()  # type: ignore
        self._sock = get_zmq_socket(
            self._ctx, zmq.PULL, self.engine.port_args.metrics_ipc_name, True  # type: ignore
        )

    async def run(self) -> None:
        """Continuously receive scheduler metrics from ZMQ socket and publish them."""
        while True:
            try:
                kv_metrics = await self._sock.recv_pyobj()  # type: ignore
                self._record_values(
                    request_active_slots=kv_metrics.request_active_slots,
                    request_total_slots=kv_metrics.request_total_slots,
                    kv_active_blocks=kv_metrics.kv_active_blocks,
                    kv_total_blocks=kv_metrics.kv_total_blocks,
                    num_requests_waiting=kv_metrics.num_requests_waiting,
                    gpu_cache_usage_perc=kv_metrics.gpu_cache_usage_perc,
                    gpu_prefix_cache_hit_rate=kv_metrics.gpu_prefix_cache_hit_rate,
                    data_parallel_rank=kv_metrics.data_parallel_rank,
                )
            except Exception:
                logging.exception(
                    "Failed to receive or publish SGLang scheduler metrics"
                )

    def init_engine_metrics_publish(self) -> None:
        """Publish initial dummy metrics to bootstrap the metrics endpoint."""
        worker_stats = WorkerStats(
            request_active_slots=0,
            request_total_slots=self.request_total_slots,
            num_requests_waiting=0,
            data_parallel_rank=self.dp_rank,
        )
        kv_stats = KvStats(
            kv_active_blocks=0,
            # TODO: num_gpu_block to get actual GPU blocks from SGLang engine instead of hardcoded value
            kv_total_blocks=self.num_gpu_block,
            gpu_cache_usage_perc=0.0,
            gpu_prefix_cache_hit_rate=0.0,
        )
        metrics = ForwardPassMetrics(
            worker_stats=worker_stats,
            kv_stats=kv_stats,
            spec_decode_stats=None,
        )
        logging.info("Sending dummy metrics to initialize")
        self.metrics_publisher.publish(metrics)

    def init_kv_event_publish(self) -> Optional[ZmqKvEventPublisher]:
        """Initialize KV event publisher if configured.

        Returns:
            ZmqKvEventPublisher instance if kv_events_config is set, None otherwise.
        """
        self.kv_publisher = None
        if self.server_args.kv_events_config:
            kv_events = json.loads(self.server_args.kv_events_config)
            ep = kv_events.get("endpoint")
            zmq_ep = ep.replace("*", get_local_ip_auto()) if ep else None

            zmq_config = ZmqKvEventPublisherConfig(
                worker_id=self.generate_endpoint.connection_id(),
                kv_block_size=self.server_args.page_size,
                zmq_endpoint=zmq_ep,
            )
            logging.info(f"Setting up ZMQ kv event publisher at {zmq_ep}")
            self.kv_publisher = ZmqKvEventPublisher(
                component=self.component, config=zmq_config
            )
        return self.kv_publisher

    def _record(
        self,
        worker_stats: WorkerStats,
        kv_stats: KvStats,
        spec_decode_stats: Optional[SpecDecodeStats] = None,
    ) -> None:
        """Package and publish metrics.

        Args:
            worker_stats: Worker-level statistics.
            kv_stats: KV cache statistics.
            spec_decode_stats: Optional speculative decoding statistics.
        """
        metrics = ForwardPassMetrics(
            worker_stats=worker_stats,
            kv_stats=kv_stats,
            spec_decode_stats=spec_decode_stats,
        )
        self.metrics_publisher.publish(metrics)

    def _record_values(
        self,
        request_active_slots: int,
        request_total_slots: int,
        kv_active_blocks: int,
        kv_total_blocks: int,
        num_requests_waiting: int,
        gpu_cache_usage_perc: float,
        gpu_prefix_cache_hit_rate: float,
        data_parallel_rank: Optional[int] = None,
        spec_decode_stats: Optional[SpecDecodeStats] = None,
    ) -> None:
        """Create stats objects from raw values and publish.

        Args:
            request_active_slots: Number of active request slots.
            request_total_slots: Total number of request slots.
            kv_active_blocks: Number of active KV cache blocks.
            kv_total_blocks: Total number of KV cache blocks.
            num_requests_waiting: Number of queued requests.
            gpu_cache_usage_perc: GPU cache utilization percentage.
            gpu_prefix_cache_hit_rate: Prefix cache hit rate.
            data_parallel_rank: Optional data parallel rank.
            spec_decode_stats: Optional speculative decoding statistics.
        """
        worker_stats = WorkerStats(
            request_active_slots=request_active_slots,
            request_total_slots=request_total_slots,
            num_requests_waiting=num_requests_waiting,
            data_parallel_rank=data_parallel_rank
            if data_parallel_rank is not None
            else self.dp_rank,
        )
        kv_stats = KvStats(
            kv_active_blocks=kv_active_blocks,
            kv_total_blocks=kv_total_blocks,
            gpu_cache_usage_perc=gpu_cache_usage_perc,
            gpu_prefix_cache_hit_rate=gpu_prefix_cache_hit_rate,
        )
        self._record(worker_stats, kv_stats, spec_decode_stats)


def setup_prometheus_registry(
    engine: sgl.Engine, generate_endpoint: Endpoint
) -> CollectorRegistry:
    """Set up Prometheus registry for SGLang metrics collection.

    SGLang uses multiprocess architecture where metrics are stored in shared memory.
    MultiProcessCollector aggregates metrics from all worker processes. The Prometheus
    registry collects sglang:* metrics which are exposed via the metrics server endpoint
    (typically port 8081) when DYN_SYSTEM_ENABLED=true.

    Args:
        engine: The SGLang engine instance.
        generate_endpoint: The Dynamo endpoint for generation requests.

    Returns:
        Configured CollectorRegistry with multiprocess support.
    """
    registry = CollectorRegistry()
    multiprocess.MultiProcessCollector(registry)
    register_engine_metrics_callback(
        endpoint=generate_endpoint,
        registry=registry,
        metric_prefix_filter="sglang:",
    )
    return registry


async def setup_sgl_metrics(
    engine: sgl.Engine,
    config: Config,
    component: Component,
    generate_endpoint: Endpoint,
) -> tuple[DynamoSglangPublisher, asyncio.Task, list[tuple[str, str]]]:
    """Create publisher, initialize metrics, and start the metrics publishing loop.

    Args:
        engine: The SGLang engine instance.
        config: SGLang configuration including server args.
        component: The Dynamo runtime component.
        generate_endpoint: The Dynamo endpoint for generation requests.

    Returns:
        Tuple of (publisher instance, running asyncio task, metrics labels).
    """
    metrics_labels = [("model", engine.server_args.served_model_name)]
    publisher = DynamoSglangPublisher(
        engine, config, component, generate_endpoint, metrics_labels
    )
    publisher.init_engine_metrics_publish()
    publisher.init_kv_event_publish()

    task = asyncio.create_task(publisher.run())
    logging.info("SGLang metrics loop started")
    return publisher, task, metrics_labels
