#!/usr/bin/env bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0

# This script installs vLLM and its dependencies from PyPI (release versions only).
# Installation order:
# 1. LMCache (installed first so vLLM's dependencies take precedence)
# 2. vLLM
# 3. vLLM-Omni
# 4. DeepGEMM
# 5. EP kernels

set -euo pipefail

VLLM_VER="0.16.0"
VLLM_REF="v${VLLM_VER}"
DEVICE="cuda"

# Basic Configurations
ARCH=$(uname -m)
MAX_JOBS=16
INSTALLATION_DIR=/tmp

# VLLM and Dependency Configurations
TORCH_CUDA_ARCH_LIST="9.0;10.0" # For EP Kernels -- TODO: check if we need to add 12.0+PTX
DEEPGEMM_REF=""
CUDA_VERSION="12.9"
FLASHINF_REF="v0.6.3"
LMCACHE_REF="0.3.14"
VLLM_OMNI_REF="v0.16.0rc1"

while [[ $# -gt 0 ]]; do
    case $1 in
        --device)
            DEVICE="$2"
            shift 2
            ;;
        --vllm-ref)
            VLLM_REF="$2"
            shift 2
            ;;
        --max-jobs)
            MAX_JOBS="$2"
            shift 2
            ;;
        --arch)
            ARCH="$2"
            shift 2
            ;;
        --installation-dir)
            INSTALLATION_DIR="$2"
            shift 2
            ;;
        --deepgemm-ref)
            DEEPGEMM_REF="$2"
            shift 2
            ;;
        --flashinf-ref)
            FLASHINF_REF="$2"
            shift 2
            ;;
        --lmcache-ref)
            LMCACHE_REF="$2"
            shift 2
            ;;
        --vllm-omni-ref)
            VLLM_OMNI_REF="$2"
            shift 2
            ;;
        --torch-cuda-arch-list)
            TORCH_CUDA_ARCH_LIST="$2"
            shift 2
            ;;
        --cuda-version)
            CUDA_VERSION="$2"
            shift 2
            ;;
        -h|--help)
            echo "Usage: $0 [--device DEVICE] [--vllm-ref REF] [--max-jobs NUM] [--arch ARCH] [--deepgemm-ref REF] [--flashinf-ref REF] [--lmcache-ref REF] [--vllm-omni-ref REF] [--torch-cuda-arch-list LIST] [--cuda-version VERSION]"
            echo "Options:"
            echo "  --device DEVICE     Device Selection (default: cuda)"
            echo "  --vllm-ref REF      vLLM release version (default: ${VLLM_REF})"
            echo "  --max-jobs NUM      Maximum parallel jobs (default: ${MAX_JOBS})"
            echo "  --arch ARCH         Architecture amd64|arm64 (default: auto-detect)"
            echo "  --installation-dir DIR  Install directory (default: ${INSTALLATION_DIR})"
            echo "  --deepgemm-ref REF  DeepGEMM git ref (default: ${DEEPGEMM_REF})"
            echo "  --flashinf-ref REF  FlashInfer version (default: ${FLASHINF_REF})"
            echo "  --lmcache-ref REF   LMCache version (default: ${LMCACHE_REF})"
            echo "  --vllm-omni-ref REF vLLM-Omni version (default: ${VLLM_OMNI_REF})"
            echo "  --torch-cuda-arch-list LIST  CUDA architectures (default: ${TORCH_CUDA_ARCH_LIST})"
            echo "  --cuda-version VERSION  CUDA version (default: ${CUDA_VERSION})"
            exit 0
            ;;
        *)
            echo "Unknown option: $1"
            exit 1
            ;;
    esac
done

# Convert x86_64 to amd64 for consistency with Docker ARG
if [ "$ARCH" = "x86_64" ]; then
    ARCH="amd64"
elif [ "$ARCH" = "aarch64" ]; then
    ARCH="arm64"
fi

# Set alternative CPU architecture naming
if [ "$ARCH" = "amd64" ]; then
    ALT_ARCH="x86_64"
elif [ "$ARCH" = "arm64" ]; then
    ALT_ARCH="aarch64"
fi

export MAX_JOBS=$MAX_JOBS
if [ "$DEVICE" = "cuda" ]; then
    export CUDA_HOME=/usr/local/cuda

    # Derive torch backend from CUDA version (e.g., "12.9" -> "cu129")
    TORCH_BACKEND="cu$(echo $CUDA_VERSION | tr -d '.')"
    CUDA_VERSION_MAJOR=${CUDA_VERSION%%.*}

    echo "=== Installing prerequisites ==="
    uv pip install pip cuda-python
fi

if [ "$DEVICE" = "cuda" ]; then
    echo "\n=== Configuration Summary ==="
    echo "  VLLM_REF=$VLLM_REF | ARCH=$ARCH | CUDA_VERSION=$CUDA_VERSION | TORCH_BACKEND=$TORCH_BACKEND"
    echo "  TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST | INSTALLATION_DIR=$INSTALLATION_DIR"
elif [ "$DEVICE" = "xpu" ]; then
    echo "\n=== Configuration Summary ==="
    echo "  VLLM_REF=$VLLM_REF | ARCH=$ARCH | INSTALLATION_DIR=$INSTALLATION_DIR"
fi

if [ "$DEVICE" = "cuda" ]; then
    if [[ "$CUDA_VERSION_MAJOR" == "12" ]]; then
        echo "  FLASHINF_REF=$FLASHINF_REF | LMCACHE_REF=$LMCACHE_REF | DEEPGEMM_REF=$DEEPGEMM_REF"
        echo "\n=== Installing LMCache ==="
        if [ "$ARCH" = "amd64" ]; then
            # LMCache installation currently fails on arm64 due to CUDA dependency issues
            # Install LMCache BEFORE vLLM so vLLM's dependencies take precedence
            uv pip install lmcache==${LMCACHE_REF} --torch-backend=${TORCH_BACKEND}
            echo "✓ LMCache ${LMCACHE_REF} installed"
        else
            echo "⚠ Skipping LMCache on ARM64 (compatibility issues)"
        fi
    else
        echo "  FLASHINF_REF=$FLASHINF_REF | LMCache will not be installed as it doesn't support CUDA 13 yet | DEEPGEMM_REF=$DEEPGEMM_REF"
    fi
elif [ "$DEVICE" = "xpu" ]; then
    echo " LMCACHE_REF=$LMCACHE_REF "
    echo "\n=== Installing LMCache ==="
    if [ "$ARCH" = "amd64" ]; then
        uv pip install lmcache==${LMCACHE_REF}
        echo "✓ LMCache ${LMCACHE_REF} installed"
    fi
fi

echo "\n=== Cloning vLLM repository ==="
# Clone needed for DeepGEMM and EP kernels install scripts
cd $INSTALLATION_DIR
git clone https://github.com/vllm-project/vllm.git vllm
cd vllm
git checkout $VLLM_REF
echo "✓ vLLM repository cloned"

if [ "$DEVICE" = "xpu" ]; then
    echo "\n=== Installing vLLM ==="
    git apply --ignore-whitespace /tmp/vllm-xpu.patch
    uv pip install -r requirements/xpu.txt --index-strategy unsafe-best-match
    uv pip install --verbose --no-build-isolation .
fi

if [ "$DEVICE" = "cuda" ]; then
    echo "\n=== Installing vLLM & FlashInfer ==="

    # Build GitHub release wheel URL per CUDA version
    # CUDA 12 wheels have no +cu suffix and use manylinux_2_31
    # CUDA 13 wheels have +cu130 suffix and use manylinux_2_35
    if [[ "$CUDA_VERSION_MAJOR" == "12" ]]; then
        VLLM_GITHUB_WHEEL="vllm-${VLLM_VER}-cp38-abi3-manylinux_2_31_${ALT_ARCH}.whl"
        EXTRA_PIP_ARGS=""
    elif [[ "$CUDA_VERSION_MAJOR" == "13" ]]; then
        VLLM_GITHUB_WHEEL="vllm-${VLLM_VER}+${TORCH_BACKEND}-cp38-abi3-manylinux_2_35_${ALT_ARCH}.whl"
        EXTRA_PIP_ARGS="--index-strategy=unsafe-best-match --extra-index-url https://download.pytorch.org/whl/${TORCH_BACKEND}"
    else
        echo "❌ Unsupported CUDA version for vLLM installation: ${CUDA_VERSION}"
        exit 1
    fi
    VLLM_GITHUB_URL="https://github.com/vllm-project/vllm/releases/download/v${VLLM_VER}/${VLLM_GITHUB_WHEEL}"

    # Install vLLM wheel
    # CUDA 12: Try PyPI first, fall back to GitHub release
    # CUDA 13: Always use GitHub release (PyPI only has cu12 wheels, --torch-backend
    #           does not prevent uv from resolving the cu12 variant)
    echo "Installing vLLM $VLLM_VER (torch backend: $TORCH_BACKEND)..."
    if [[ "$CUDA_VERSION_MAJOR" == "12" ]]; then
        if uv pip install "vllm[flashinfer,runai]==${VLLM_VER}" ${EXTRA_PIP_ARGS} --torch-backend=${TORCH_BACKEND} 2>&1; then
            echo "✓ vLLM ${VLLM_VER} installed from PyPI"
        else
            echo "⚠ PyPI install failed, installing from GitHub release..."
            uv pip install ${EXTRA_PIP_ARGS} \
                "${VLLM_GITHUB_URL}[flashinfer,runai]" \
                --torch-backend=${TORCH_BACKEND}
            echo "✓ vLLM ${VLLM_VER} installed from GitHub"
        fi
    else
        echo "Installing vLLM from GitHub release (cu130 wheel not available on PyPI)..."
        uv pip install ${EXTRA_PIP_ARGS} \
            "${VLLM_GITHUB_URL}[flashinfer,runai]" \
            --torch-backend=${TORCH_BACKEND}
        echo "✓ vLLM ${VLLM_VER} installed from GitHub"
    fi
    uv pip install flashinfer-cubin==$FLASHINF_REF
    uv pip install flashinfer-jit-cache==$FLASHINF_REF --extra-index-url https://flashinfer.ai/whl/${TORCH_BACKEND}
fi
echo "✓ vLLM installation completed"

echo "\n=== Installing vLLM-Omni ==="
if [ -n "$VLLM_OMNI_REF" ] && [ "$ARCH" = "amd64" ]; then
    # Save original vllm entrypoint before vllm-omni overwrites it
    VLLM_BIN=$(which vllm)
    cp "$VLLM_BIN" /tmp/vllm-entrypoint-backup
    # Try PyPI first, fall back to building from source
    if uv pip install vllm-omni==${VLLM_OMNI_REF#v} 2>&1; then
        echo "✓ vLLM-Omni ${VLLM_OMNI_REF} installed from PyPI"
    else
        echo "⚠ PyPI install failed, building from source..."
        git clone --depth 1 --branch ${VLLM_OMNI_REF} https://github.com/vllm-project/vllm-omni.git $INSTALLATION_DIR/vllm-omni
        uv pip install $INSTALLATION_DIR/vllm-omni
        rm -rf $INSTALLATION_DIR/vllm-omni
        echo "✓ vLLM-Omni ${VLLM_OMNI_REF} installed from source"
    fi
    # Restore original vllm CLI entrypoint (vllm-omni replaces it with its own)
    cp /tmp/vllm-entrypoint-backup "$VLLM_BIN"
    echo "✓ Original vllm entrypoint preserved"
else
    echo "⚠ Skipping vLLM-Omni (no ref provided or ARM64 not supported)"
fi

if [ "$DEVICE" = "cuda" ]; then
    echo "\n=== Installing DeepGEMM ==="
    cd $INSTALLATION_DIR/vllm/tools
    if [ -n "$DEEPGEMM_REF" ]; then
        bash install_deepgemm.sh --cuda-version "${CUDA_VERSION}" --ref "$DEEPGEMM_REF"
    else
        bash install_deepgemm.sh --cuda-version "${CUDA_VERSION}"
    fi
    echo "✓ DeepGEMM installation completed"

    echo "\n=== Installing EP Kernels (PPLX and DeepEP) ==="
    cd ep_kernels/
    # TODO we will be able to specify which pplx and deepep commit we want in future
    TORCH_CUDA_ARCH_LIST="$TORCH_CUDA_ARCH_LIST" bash install_python_libraries.sh
fi
echo "\n✅ All installations completed successfully!"
