# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import argparse
import logging
import random
from argparse import Namespace
from typing import AsyncIterator, Tuple

import numpy as np  # Add numpy import
from components.worker import VllmWorker
from utils.check_worker import check_required_workers
from utils.protocol import LocalBlockHashes
from utils.vllm import RouterType

from dynamo.llm import AggregatedMetrics, KvIndexer, KvMetricsAggregator, OverlapScores
from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
from dynamo.sdk.lib.config import ServiceConfig

WorkerId = str
fallback_msg = "Will fallback to random routing."

logger = logging.getLogger(__name__)


def softmax_sample_from_logits(
    logits: dict[str, float], temperature: float = 1.0, lower_is_better: bool = True
) -> str:
    if not logits:
        raise ValueError("Empty logits dictionary")

    keys = list(logits.keys())
    values = np.array(list(logits.values()))

    min_val = np.min(values)
    max_val = np.max(values)

    if min_val == max_val:
        # All values are the same, uniform probability
        probabilities = np.ones(len(keys)) / len(keys)
    else:
        normalized = values / (max_val - min_val)
        if lower_is_better:
            normalized = -1 * normalized

        scaled = normalized / temperature

        exp_values = np.exp(scaled - np.max(scaled))
        probabilities = exp_values / np.sum(exp_values)

    # Sample from the probability distribution
    return np.random.choice(keys, p=probabilities)


def parse_args(service_name, prefix) -> Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model",
        type=str,
        default="deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
        help="Model that is being served",
    )
    parser.add_argument(
        "--min-workers",
        type=int,
        default=1,
        help="Minimum number of workers required before proceeding",
    )
    # TODO: Read block size
    parser.add_argument(
        "--block-size",
        type=int,
        default=64,
        help="KV block size",
    )
    parser.add_argument(
        "--custom-router",
        type=bool,
        default=False,
        help="Whether to use custom router or not",
    )
    parser.add_argument(
        "--router",
        type=str,
        default="kv",
        help="The router type",
    )
    parser.add_argument(
        "--softmax-sample",
        action="store_true",
        help="Whether to do softmax sampling based on worker logits (default is to pick smallest)",
    )
    config = ServiceConfig.get_instance()
    config_args = config.as_args(service_name, prefix=prefix)
    args = parser.parse_args(config_args)
    return args


@service(
    dynamo={
        "namespace": "dynamo",
    },
    resources={"cpu": "10", "memory": "20Gi"},
    workers=1,
)
class Router:
    """
    Request handler for the generate endpoint
    """

    worker = depends(VllmWorker)

    def __init__(self):
        logger.info("Initializing Custom Router")
        self.args = parse_args(self.__class__.__name__, "")

        self.default_metrics = {
            "kv_active_blocks": 0,
            "kv_total_blocks": 1,
            "num_requests_waiting": 0.0,
            "gpu_cache_usage_perc": 0.0,
            "gpu_prefix_cache_hit_rate": 0.0,
        }

    @async_on_start
    async def async_init(self):
        self.runtime = dynamo_context["runtime"]
        self.workers_client = (
            await self.runtime.namespace("dynamo")
            .component("VllmWorker")
            .endpoint("generate")
            .client()
        )

        self.router_type = self.args.router

        await check_required_workers(self.workers_client, self.args.min_workers)

        kv_listener = self.runtime.namespace("dynamo").component("VllmWorker")
        await kv_listener.create_service()
        if self.router_type == RouterType.KV:
            self.indexer = KvIndexer(kv_listener, self.args.block_size)
        self.metrics_aggregator = KvMetricsAggregator(kv_listener)

        self.active_blocks_dict = {}
        worker_ids = self.workers_client.instance_ids()
        for worker_id in worker_ids:
            # [old_value, predictive_value]
            self.active_blocks_dict[worker_id] = [0, 0]

        logger.info("KV Router initialized")

    def _update_and_get_active_blocks(self, worker_id: str, polled_value: int) -> int:
        """Helper routine to update waiting dict and return the desired waiting value.

        This method implements a predictive mechanism for tracking waiting requests:
        - If a new polled value is detected (different from the stored old value),
          it updates both the old and predictive values to this new measurement and returns it
        - If no change is detected (polled value equals old value), it returns the
          predictive value which has been incremented based on previous routing decisions

        This allows the router to account for requests that have been dispatched but
        not yet reflected in the polled metrics.
        """
        old_value, predictive_value = self.active_blocks_dict[worker_id]

        # Check if polled value is different from old value
        if polled_value != old_value:
            self.active_blocks_dict[worker_id] = [polled_value, polled_value]
            return polled_value
        else:
            return predictive_value

    def _cost_function(
        self,
        scores: OverlapScores | None,
        metrics: AggregatedMetrics | None,
        token_length: int,
    ):
        """The cost function for deciding the best worker to route a request to.
        If there are multiple workers sharing the same optimal cost, then
        one of them is randomly selected.

        Args:
            scores (OverlapScores | None): The number of matching blocks between
                the request and the prefix cache of each worker.
            metrics (AggregatedMetrics | None): Several worker metrics polled
                by the `KvMetricsAggregator`, currently including the
                GPU cache usage, number of waiting requests, and the
                GPU prefix cache hit rate.
            token_length (int): The number of tokens in the request.

        Returns:
            (str, float): The best worker id and the corresponding score.
        """

        # Get all worker IDs from the client. This is needed because scores / metrics may not have values for all workers
        # and we want all workers to be considered in the logit calculation
        worker_ids = self.workers_client.instance_ids()
        request_blocks = (
            token_length + self.args.block_size - 1
        ) // self.args.block_size

        overlap_blocks_dict = {worker_id: 0 for worker_id in worker_ids}
        new_blocks_dict = {worker_id: request_blocks for worker_id in worker_ids}

        if scores:
            for worker_id, score in scores.scores.items():
                # score is number of matching blocks we multiply by block_size to get tokens
                # and compare to token_length. The larger the cache hit the better
                overlap_blocks_dict[worker_id] = score
                new_blocks_dict[worker_id] = request_blocks - score
        else:
            logger.warning("Cannot get KV scores")

        worker_metrics = {}
        if metrics:
            for endpoint in metrics.endpoints:
                worker_id = endpoint.worker_id
                worker_metrics[worker_id] = {
                    key: getattr(endpoint, key, self.default_metrics[key])
                    for key in self.default_metrics.keys()
                }

                # Update waiting value using helper routine
                polled_active_blocks = int(
                    worker_metrics[worker_id]["kv_active_blocks"]
                )
                worker_metrics[worker_id][
                    "kv_active_blocks"
                ] = self._update_and_get_active_blocks(worker_id, polled_active_blocks)
        else:
            logger.warning("Cannot get metrics")

        worker_logits = {}
        for worker_id in worker_ids:
            # Use default values if worker not in scores or metrics
            metrics_dict = worker_metrics.get(worker_id, self.default_metrics)
            kv_total_blocks = metrics_dict["kv_total_blocks"]

            new_blocks = new_blocks_dict[worker_id]
            normalized_new_blocks = new_blocks / kv_total_blocks
            gpu_cache_usage = metrics_dict["kv_active_blocks"] / kv_total_blocks

            # Use raw waiting value without normalization
            num_requests_waiting = metrics_dict["num_requests_waiting"]

            # Have 1 metric that weights towards cache hit
            # 2 metrics that penalize overloaded worker and queuing
            worker_logits[worker_id] = (
                normalized_new_blocks + gpu_cache_usage + num_requests_waiting
            )
            logger.info(
                f"Formula for {worker_id}: {worker_logits[worker_id]:.3f} = {normalized_new_blocks:.3f} + {gpu_cache_usage:.3f} + {num_requests_waiting:.3f}"
            )

        if not worker_logits or not any(worker_logits.values()):
            logger.warning(f"All worker logits are zero. {fallback_msg}.")
            return "", 0.0

        # Select the worker with the highest logit
        if self.args.softmax_sample:
            best_worker_id = int(softmax_sample_from_logits(worker_logits))
        else:
            min_logit = min(worker_logits.values())
            best_workers = [
                wid for wid, logit in worker_logits.items() if logit == min_logit
            ]
            best_worker_id = random.choice(best_workers)

        # Log the metrics for the selected worker
        if best_worker_id:
            metrics_dict = worker_metrics.get(best_worker_id, self.default_metrics)

            # Create log messages
            log_messages = [
                f"Selected worker: {best_worker_id}, logit: {worker_logits[best_worker_id]:.3f}",
                f"Score: {scores.scores.get(best_worker_id, 0.0) if scores else 0.0:.3f}",
                f"GPU Cache Hit Rate: {metrics_dict['gpu_prefix_cache_hit_rate']:.3f}",
                f"GPU Cache Usage: {metrics_dict['kv_active_blocks'] / metrics_dict['kv_total_blocks']:.3f}",
                f"Requests Waiting: {metrics_dict['num_requests_waiting']}",
            ]

            # Log to vllm_logger
            for message in log_messages:
                logger.info(message)

            # Increment predictive waiting for the selected worker before returning
            self.active_blocks_dict[best_worker_id][1] += new_blocks_dict[
                best_worker_id
            ]

        return (
            best_worker_id,
            overlap_blocks_dict[best_worker_id] * self.args.block_size / token_length,
        )

    def _get_underloaded_worker(self, metrics: AggregatedMetrics | None):
        if not metrics:
            logger.warning(f"Cannot get metrics. {fallback_msg}")
            return "", 0.0

        kv_load = {
            endpoint.worker_id: getattr(endpoint, "gpu_cache_usage_perc", 0.0)
            for endpoint in metrics.endpoints
        }

        if not kv_load or not any(kv_load.values()):
            logger.warning(f"All KV loads are zero. {fallback_msg}")
            return "", 0.0

        min_load = min(kv_load.values())
        min_load_workers = [
            worker_id for worker_id, load in kv_load.items() if load == min_load
        ]
        best_worker_id = random.choice(min_load_workers)

        logger.info(
            f"Selected worker: {best_worker_id}, KV load: {kv_load[best_worker_id]:.3f}"
        )
        return best_worker_id, kv_load[best_worker_id]

    @endpoint()
    async def generate(
        self, request: LocalBlockHashes
    ) -> AsyncIterator[Tuple[WorkerId, float]]:
        metrics = await self.metrics_aggregator.get_metrics()

        # Quick return for KV_LOAD mode
        if self.router_type == RouterType.KV_LOAD:
            try:
                yield self._get_underloaded_worker(metrics)
            except Exception as e:
                logger.exception(
                    f"Error finding underloaded worker: {e}. {fallback_msg}"
                )
                yield "", 0.0
            return

        # Existing KV routing logic
        try:
            scores = await self.indexer.find_matches(request.hashes)
        except Exception as e:
            scores = {}
            logger.exception(f"Error finding matches: {e}. {fallback_msg}")
            yield "", 0.0
            return

        worker_id, prefix_hit_rate = self._cost_function(
            scores, metrics, request.num_tokens
        )

        if worker_id:
            logger.info(
                f"Scheduling to worker_id: {worker_id} with estimated prefix hit rate: {prefix_hit_rate}"
            )

        yield worker_id, prefix_hit_rate
