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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import logging
import os

from dynamo.planner.kube import get_current_k8s_namespace
from dynamo.runtime.logging import configure_dynamo_logging

configure_dynamo_logging()
logger = logging.getLogger(__name__)


# Source of truth for planner defaults
class BasePlannerDefaults:
    namespace = "dynamo"
    environment = "kubernetes"
    backend = "vllm"
    no_operation = False
    log_dir = None
    adjustment_interval = 180  # in seconds
    max_gpu_budget = 8
    min_endpoint = 1  # applies to both decode and prefill
    decode_engine_num_gpu = 1
    prefill_engine_num_gpu = 1


class LoadPlannerDefaults(BasePlannerDefaults):
    metric_pulling_interval = 10  # in seconds
    decode_kv_scale_up_threshold = 0.9
    decode_kv_scale_down_threshold = 0.5
    prefill_queue_scale_up_threshold = 5.0
    prefill_queue_scale_down_threshold = 0.2


def _get_default_prometheus_endpoint(port: str, namespace: str):
    """Compute default prometheus endpoint using environment variables and Kubernetes service discovery"""

    k8s_namespace = get_current_k8s_namespace()
    if k8s_namespace and k8s_namespace != "default":
        prometheus_service = f"{namespace}-prometheus"
        return f"http://{prometheus_service}.{k8s_namespace}.svc.cluster.local:{port}"
    else:
        logger.warning(
            f"Cannot determine Prometheus endpoint. Running in namespace '{k8s_namespace}'. "
            "Ensure the planner is deployed in a Kubernetes cluster with proper namespace configuration."
        )
        return f"{namespace}-prometheus"


class SLAPlannerDefaults(BasePlannerDefaults):
    port = os.environ.get("DYNAMO_PORT", "8000")
    namespace = os.environ.get("DYNAMO_NAMESPACE", "vllm-disagg-planner")
    prometheus_endpoint = _get_default_prometheus_endpoint(port, namespace)
    profile_results_dir = "profiling_results"
    isl = 3000  # in number of tokens
    osl = 150  # in number of tokens
    ttft = 0.5  # in seconds
    itl = 0.05  # in seconds
    load_predictor = "arima"  # ["constant", "arima", "prophet"]
    load_prediction_window_size = 50  # predict load using how many recent load samples


class VllmComponentName:
    prefill_worker = "prefill"
    prefill_worker_endpoint = "generate"
    decode_worker = "backend"
    decode_worker_endpoint = "generate"


WORKER_COMPONENT_NAMES = {
    "vllm": VllmComponentName,
}
