# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

"""
Unit tests for SLA planner replica calculation logic.

These tests focus specifically on the replica calculation formulas without
testing load prediction, interpolation, or correction factors.
"""

import argparse
import asyncio
import math
import os

# Mock dependencies before importing planner modules
import sys

# We'll import the actual Planner class to test its calculation logic
from unittest.mock import MagicMock, Mock, patch

import pytest

# Create mock modules for dependencies that might not be available in test environment
mock_prometheus = MagicMock()
mock_prometheus.Gauge = MagicMock()
mock_prometheus.start_http_server = MagicMock()

mock_runtime = MagicMock()
mock_runtime.logging = MagicMock()
mock_runtime.logging.configure_dynamo_logging = MagicMock()

# Patch them into sys.modules before importing
sys.modules["prometheus_client"] = mock_prometheus
sys.modules["dynamo.runtime"] = mock_runtime
sys.modules["dynamo.runtime.logging"] = mock_runtime.logging

# Now import after mocking
from dynamo.planner.utils.planner_core import (  # noqa: E402
    DecodePlanner,
    Metrics,
    PlannerSharedState,
    PrefillPlanner,
    _apply_global_gpu_budget,
)

pytestmark = [pytest.mark.pre_merge, pytest.mark.gpu_0]


class PlannerHarness:
    def __init__(self, prefill_planner, decode_planner, shared_state):
        self.prefill_planner = prefill_planner
        self.decode_planner = decode_planner
        self.shared_state = shared_state
        self.last_target_replicas = []

    async def make_adjustments(self):
        if not self.shared_state.last_metrics.is_valid():
            return

        p_endpoints, d_endpoints = await self.prefill_planner.get_workers_info()
        self.shared_state.p_endpoints = p_endpoints
        self.shared_state.d_endpoints = d_endpoints

        next_num_p = self.prefill_planner.plan_adjustment()
        next_num_d = self.decode_planner.plan_adjustment()
        if next_num_p is None or next_num_d is None:
            return

        next_num_p, next_num_d = _apply_global_gpu_budget(
            next_num_p, next_num_d, self.prefill_planner.args
        )
        self.prefill_planner.update_predicted_replicas_metric(next_num_p)
        self.decode_planner.update_predicted_replicas_metric(next_num_d)

        target_replicas = [
            {
                "sub_component_type": "prefill",
                "component_name": self.prefill_planner.prefill_component_name,
                "desired_replicas": next_num_p,
            },
            {
                "sub_component_type": "decode",
                "component_name": self.prefill_planner.decode_component_name,
                "desired_replicas": next_num_d,
            },
        ]
        self.last_target_replicas = target_replicas

        if not self.prefill_planner.args.no_operation:
            await self.prefill_planner.connector.set_component_replicas(
                target_replicas, blocking=False
            )

    def __getattr__(self, name):
        shared_attrs = {
            "num_req_predictor",
            "isl_predictor",
            "osl_predictor",
            "connector",
            "prometheus_api_client",
            "args",
        }
        prefill_attrs = {
            "prefill_interpolator",
            "prefill_component_name",
            "p_correction_factor",
        }
        decode_attrs = {
            "decode_interpolator",
            "decode_component_name",
            "d_correction_factor",
        }
        if name == "last_metrics":
            return self.shared_state.last_metrics
        if name == "get_workers_info":
            return self.prefill_planner.get_workers_info
        if name in shared_attrs:
            return getattr(self.prefill_planner, name)
        if name in prefill_attrs:
            return getattr(self.prefill_planner, name)
        if name in decode_attrs:
            return getattr(self.decode_planner, name)
        raise AttributeError(name)

    def __setattr__(self, name, value):
        if name in {"prefill_planner", "decode_planner", "shared_state"}:
            return super().__setattr__(name, value)
        shared_attrs = {
            "num_req_predictor",
            "isl_predictor",
            "osl_predictor",
            "connector",
            "prometheus_api_client",
            "args",
            "get_workers_info",
        }
        prefill_attrs = {"prefill_interpolator", "p_correction_factor"}
        decode_attrs = {"decode_interpolator", "d_correction_factor"}
        if name == "last_metrics":
            self.shared_state.last_metrics = value
            return None
        if name in shared_attrs:
            # Store locally to support patch.object lifecycle (set/del).
            object.__setattr__(self, name, value)
            setattr(self.prefill_planner, name, value)
            setattr(self.decode_planner, name, value)
            return None
        if name in prefill_attrs:
            setattr(self.prefill_planner, name, value)
            return None
        if name in decode_attrs:
            setattr(self.decode_planner, name, value)
            return None
        return super().__setattr__(name, value)


def _replica_count(target_replicas, component_name, default=1):
    for replica in target_replicas:
        if replica.get("component_name") == component_name:
            return replica.get("desired_replicas", default)
    return default


@pytest.fixture
def planner():
    """Set up test environment with mocked dependencies."""
    # Create mock arguments
    args = argparse.Namespace()
    args.adjustment_interval = 60
    args.prefill_engine_num_gpu = 1
    args.decode_engine_num_gpu = 1
    args.min_endpoint = 1
    args.max_gpu_budget = 10
    args.ttft = 80.0  # ms
    args.itl = 10.0  # ms
    args.backend = "vllm"
    args.no_operation = True  # Don't actually scale
    args.no_correction = False  # Allow correction factors
    args.metric_pulling_prometheus_endpoint = "http://localhost:9090"  # dummy endpoint
    args.metric_reporting_prometheus_port = 0  # 0 means disabled
    args.load_predictor = "constant"
    args.load_prediction_window_size = 10
    args.profile_results_dir = os.path.join(
        os.path.dirname(__file__),
        "profiling_results/H200_TP1P_TP1D",
    )
    args.environment = "kubernetes"
    args.namespace = "test-namespace"  # Required for Planner.__init__
    args.no_correction = False  # Required for Planner.__init__

    # Mock the runtime
    mock_runtime = Mock()

    # Patch Prometheus Gauge to avoid registry conflicts
    with patch("dynamo.planner.utils.planner_core.Gauge") as mock_gauge:
        mock_gauge.return_value = Mock()

        shared_state = PlannerSharedState()
        prefill_planner = PrefillPlanner(mock_runtime, args, shared_state=shared_state)
        decode_planner = DecodePlanner(mock_runtime, args, shared_state=shared_state)
        planner = PlannerHarness(prefill_planner, decode_planner, shared_state)

        # Mock the interpolators to return fixed values for testing
        planner.prefill_interpolator = Mock()
        planner.decode_interpolator = Mock()

        # Mock the predictors to return fixed values
        planner.num_req_predictor = Mock()
        planner.isl_predictor = Mock()
        planner.osl_predictor = Mock()

        # Mock the connector since we're not testing actual scaling
        planner.connector = Mock()

        # Mock prometheus client
        planner.prometheus_api_client = Mock()

        # Set up some baseline correction factors
        planner.p_correction_factor = 1.0
        planner.d_correction_factor = 1.0

        # Store args for easy access in tests
        planner.args = args

        yield planner
        # Cleanup is automatic with context manager


class TestReplicaCalculation:
    """Test replica calculation formulas in isolation."""

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_prefill_replica_calculation_basic(self, planner):
        """Test basic prefill replica calculation."""
        # Setup test data
        next_num_req = 10
        next_isl = 3000
        prefill_thpt_per_gpu = 40000  # tokens/s/gpu (from the test data)

        # Mock the predictor outputs
        planner.num_req_predictor.predict_next.return_value = next_num_req
        planner.isl_predictor.predict_next.return_value = next_isl
        planner.osl_predictor.predict_next.return_value = 150

        # Mock interpolator output
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = (
            prefill_thpt_per_gpu
        )
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            10000,
            0.01,
            0.5,
        )

        # Calculate expected result manually
        pred_prefill_load_per_gpu = (
            next_num_req
            * next_isl
            / planner.args.adjustment_interval
            * min(1, planner.p_correction_factor)
        )
        expected_prefill_replicas = math.ceil(
            pred_prefill_load_per_gpu
            / prefill_thpt_per_gpu
            / planner.args.prefill_engine_num_gpu
        )

        # Set up valid metrics to trigger calculation
        planner.last_metrics = Metrics(
            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls for correction factor calculation
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Run the calculation
        asyncio.run(planner.make_adjustments())

        # Extract the calculated values from the log calls or by checking the mock calls
        # Since we mocked the connector, we can check what replicas were requested
        prefill_component = "VllmPrefillWorker"
        calculated_prefill_replicas = _replica_count(
            planner.last_target_replicas, prefill_component
        )
        print(f"Expected prefill replicas: {expected_prefill_replicas}")
        print(f"Calculated prefill replicas: {calculated_prefill_replicas}")

        # Allow for small differences due to min_endpoint constraints
        assert (
            max(expected_prefill_replicas, planner.args.min_endpoint)
            == calculated_prefill_replicas
        )

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_decode_replica_calculation_basic(self, planner):
        """Test basic decode replica calculation."""
        # Setup test data
        next_num_req = 10
        next_osl = 150
        decode_thpt_per_gpu = 10000  # tokens/s/gpu

        # Mock the predictor outputs
        planner.num_req_predictor.predict_next.return_value = next_num_req
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = next_osl

        # Mock interpolator outputs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            decode_thpt_per_gpu,
            0.01,
            0.5,
        )

        # Calculate expected result manually
        expected_decode_replicas = math.ceil(
            next_num_req
            * next_osl
            / planner.args.adjustment_interval
            / decode_thpt_per_gpu
            / planner.args.decode_engine_num_gpu
        )

        # Set up valid metrics
        planner.last_metrics = Metrics(
            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls for correction factor calculation
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Run the calculation
        asyncio.run(planner.make_adjustments())

        # Check the results
        decode_component = "VllmDecodeWorker"
        calculated_decode_replicas = _replica_count(
            planner.last_target_replicas, decode_component
        )
        print(f"Expected decode replicas: {expected_decode_replicas}")
        print(f"Calculated decode replicas: {calculated_decode_replicas}")

        # Allow for small differences due to min_endpoint constraints
        assert (
            max(expected_decode_replicas, planner.args.min_endpoint)
            == calculated_decode_replicas
        )

    @pytest.mark.parametrize(
        "num_req,decode_thpt,expected_p,expected_d",
        [
            (10, 10000, 1, 1),  # low_load_10_req_per_second
            (500, 1000, 1, 2),  # high_load_500_req_per_second (lower decode throughput)
        ],
    )
    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_scaling_scenario_low_to_high_load(
        self, planner, num_req, decode_thpt, expected_p, expected_d
    ):
        """Test scaling from low to high load scenarios."""
        # Reset the planner state
        planner.p_correction_factor = 1.0
        planner.d_correction_factor = 1.0

        # Mock predictor outputs for this case
        planner.num_req_predictor.predict_next.return_value = num_req
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = 150

        # Mock interpolator outputs (based on H200 1P1D profiling data)
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = (
            40000  # tokens/s/gpu
        )
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            decode_thpt,
            0.01,
            0.5,
        )

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=num_req,
            isl=3000,
            osl=150,
            ttft=80.0,
            itl=10.0,
            request_duration=100.0,
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls for correction factor calculation
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Reset the mock
        planner.connector.reset_mock()

        # Run calculation
        asyncio.run(planner.make_adjustments())

        # Verify results
        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )
        decode_replicas = _replica_count(
            planner.last_target_replicas, "VllmDecodeWorker"
        )
        print(f"Load {num_req} req/s: P={prefill_replicas}, D={decode_replicas}")

        assert (
            prefill_replicas == expected_p
        ), f"Prefill replicas mismatch: expected {expected_p}, got {prefill_replicas}"
        assert (
            decode_replicas == expected_d
        ), f"Decode replicas mismatch: expected {expected_d}, got {decode_replicas}"

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_gpu_budget_constraint(self, planner):
        """Test that GPU budget constraints are properly applied."""
        # Set a low GPU budget
        planner.args.max_gpu_budget = 3

        # Mock predictor outputs that would normally require more GPUs
        planner.num_req_predictor.predict_next.return_value = 50  # High load
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = 150

        # Mock interpolator outputs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            10000,
            0.01,
            0.5,
        )

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=50, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Run calculation
        asyncio.run(planner.make_adjustments())

        # Verify that total GPU usage doesn't exceed budget
        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )
        decode_replicas = _replica_count(
            planner.last_target_replicas, "VllmDecodeWorker"
        )
        total_gpus = (
            prefill_replicas * planner.args.prefill_engine_num_gpu
            + decode_replicas * planner.args.decode_engine_num_gpu
        )

        print(
            f"GPU budget test: P={prefill_replicas}, D={decode_replicas}, Total GPUs={total_gpus}"
        )

        assert (
            total_gpus <= planner.args.max_gpu_budget
        ), "Total GPU usage exceeds budget"

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_min_endpoint_constraint(self, planner):
        """Test that minimum endpoint constraints are respected."""
        planner.args.min_endpoint = 2

        # Mock predictor outputs that would normally require fewer workers
        planner.num_req_predictor.predict_next.return_value = 1  # Very low load
        planner.isl_predictor.predict_next.return_value = 100
        planner.osl_predictor.predict_next.return_value = 10

        # Mock interpolator outputs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            10000,
            0.01,
            0.5,
        )

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=1, isl=100, osl=10, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Run calculation
        asyncio.run(planner.make_adjustments())

        # Verify minimum constraints are respected
        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )
        decode_replicas = _replica_count(
            planner.last_target_replicas, "VllmDecodeWorker"
        )
        print(f"Min endpoint test: P={prefill_replicas}, D={decode_replicas}")

        assert (
            prefill_replicas >= planner.args.min_endpoint
        ), "Prefill replicas below minimum"
        assert (
            decode_replicas >= planner.args.min_endpoint
        ), "Decode replicas below minimum"

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_prefill_correction_factor_clamping(self, planner):
        """Test that prefill correction factor > 1 is clamped to 1."""
        # Set a high correction factor > 1
        planner.p_correction_factor = 2.5
        planner.d_correction_factor = 1.0

        # Mock predictor outputs
        planner.num_req_predictor.predict_next.return_value = 10
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = 150

        # Mock interpolator outputs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            10000,
            0.01,
            0.5,
        )

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Calculate expected result manually with clamping
        # Should use min(1, 2.5) = 1
        pred_prefill_load_per_gpu = (
            10 * 3000 / planner.args.adjustment_interval * min(1, 2.5)  # Should be * 1
        )
        expected_prefill_replicas = math.ceil(
            pred_prefill_load_per_gpu / 40000 / planner.args.prefill_engine_num_gpu
        )

        # Run calculation
        asyncio.run(planner.make_adjustments())

        # Verify that correction factor was effectively clamped
        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )

        print(
            f"Correction factor clamping test: Expected={expected_prefill_replicas}, Got={prefill_replicas}"
        )

        assert prefill_replicas == max(
            expected_prefill_replicas, planner.args.min_endpoint
        ), "Prefill correction factor should be clamped to 1"

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_decode_correction_factor_zero_handling(self, planner):
        """Test handling of d_correction_factor <= 0."""
        # Test both 0 and negative values
        for correction_factor in [0.0, -1.0]:
            planner.p_correction_factor = 1.0
            planner.d_correction_factor = correction_factor

            # Mock predictor outputs
            planner.num_req_predictor.predict_next.return_value = 10
            planner.isl_predictor.predict_next.return_value = 3000
            planner.osl_predictor.predict_next.return_value = 150

            # Mock interpolator outputs
            planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
            planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
                10000,
                0.01,
                0.5,
            )

            # Set up metrics
            planner.last_metrics = Metrics(
                num_req=10,
                isl=3000,
                osl=150,
                ttft=80.0,
                itl=10.0,
                request_duration=100.0,
            )

            # Mock workers info
            async def mock_get_workers_info():
                return (["prefill1"], ["decode1"])

            planner.get_workers_info = mock_get_workers_info

            # Mock interpolation calls
            planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
            planner.decode_interpolator.interpolate_itl.return_value = 10.0

            # Run calculation
            asyncio.run(planner.make_adjustments())

            # Should handle gracefully without crashing
            # The code should use args.itl directly instead of dividing by 0
            decode_replicas = _replica_count(
                planner.last_target_replicas, "VllmDecodeWorker"
            )

            print(
                f"Correction factor {correction_factor} test: Decode replicas={decode_replicas}"
            )

            # Should get a valid result (not crash)
            assert (
                decode_replicas >= 1
            ), f"Should handle correction factor {correction_factor} gracefully"

    @pytest.mark.nightly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_multi_gpu_engines(self, planner):
        """Test replica calculation with multi-GPU engines."""
        # Set multi-GPU configuration
        planner.args.prefill_engine_num_gpu = 2
        planner.args.decode_engine_num_gpu = 4

        # Mock predictor outputs
        planner.num_req_predictor.predict_next.return_value = 20
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = 150

        # Mock interpolator outputs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            5000,
            0.01,
            0.5,
        )  # Lower for scaling

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=20, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Calculate expected results manually
        pred_prefill_load_per_gpu = 20 * 3000 / planner.args.adjustment_interval * 1.0
        expected_prefill_replicas = math.ceil(
            pred_prefill_load_per_gpu / 40000 / 2
        )  # 2 GPUs per engine

        expected_decode_replicas = math.ceil(
            20 * 150 / planner.args.adjustment_interval / 5000 / 4
        )  # 4 GPUs per engine

        # Run calculation
        asyncio.run(planner.make_adjustments())

        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )
        decode_replicas = _replica_count(
            planner.last_target_replicas, "VllmDecodeWorker"
        )
        print(
            f"Multi-GPU test: P={prefill_replicas} (expected ~{expected_prefill_replicas}), D={decode_replicas} (expected ~{expected_decode_replicas})"
        )

        # Verify calculations account for multiple GPUs per engine
        assert prefill_replicas == max(
            expected_prefill_replicas, planner.args.min_endpoint
        )
        assert decode_replicas == max(
            expected_decode_replicas, planner.args.min_endpoint
        )

    @pytest.mark.weekly
    @pytest.mark.gpu_2
    @pytest.mark.performance
    def test_complex_gpu_budget_scaling(self, planner):
        """Test complex GPU budget scaling with proportional reduction and decode adjustment."""
        # Set tight GPU budget that will trigger complex scaling
        planner.args.max_gpu_budget = 5
        planner.args.prefill_engine_num_gpu = 2
        planner.args.decode_engine_num_gpu = 2
        planner.args.min_endpoint = 1

        # High load that would normally require more GPUs
        planner.num_req_predictor.predict_next.return_value = 100
        planner.isl_predictor.predict_next.return_value = 3000
        planner.osl_predictor.predict_next.return_value = 150

        # Lower throughput to trigger higher replica needs
        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 10000
        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
            1000,
            0.01,
            0.5,
        )

        # Set up metrics
        planner.last_metrics = Metrics(
            num_req=100, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
        )

        # Mock workers info
        async def mock_get_workers_info():
            return (["prefill1"], ["decode1"])

        planner.get_workers_info = mock_get_workers_info

        # Mock interpolation calls
        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
        planner.decode_interpolator.interpolate_itl.return_value = 10.0

        # Run calculation
        asyncio.run(planner.make_adjustments())

        prefill_replicas = _replica_count(
            planner.last_target_replicas, "VllmPrefillWorker"
        )
        decode_replicas = _replica_count(
            planner.last_target_replicas, "VllmDecodeWorker"
        )
        # Verify total GPU usage doesn't exceed budget
        total_gpus = (
            prefill_replicas * planner.args.prefill_engine_num_gpu
            + decode_replicas * planner.args.decode_engine_num_gpu
        )

        print(
            f"Complex GPU budget test: P={prefill_replicas}, D={decode_replicas}, Total GPUs={total_gpus}"
        )

        assert (
            total_gpus <= planner.args.max_gpu_budget
        ), "Total GPU usage should not exceed budget"
        assert (
            prefill_replicas >= planner.args.min_endpoint
        ), "Should respect min_endpoint for prefill"
        assert (
            decode_replicas >= planner.args.min_endpoint
        ), "Should respect min_endpoint for decode"


# No need for unittest.main() with pytest!
