Model-Optimizer/examples/diffusers/quantization/quantize.py

# SPDX-FileCopyrightText: Copyright (c) 2024 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 copy
import logging
import sys
import time as time
from pathlib import Path
from typing import Any

import torch
from calibration import Calibrator
from config import (
    FP8_DEFAULT_CONFIG,
    INT8_DEFAULT_CONFIG,
    NVFP4_DEFAULT_CONFIG,
    NVFP4_FP8_MHA_CONFIG,
    reset_set_int8_config,
    set_quant_config_attr,
)
from diffusers import DiffusionPipeline
from models_utils import (
    MODEL_DEFAULTS,
    ModelType,
    build_block_range_quant_cfg,
    get_model_filter_func,
    parse_extra_params,
)
from pipeline_manager import PipelineManager
from quantize_config import (
    CalibrationConfig,
    CollectMethod,
    DataType,
    ExportConfig,
    ModelConfig,
    QuantAlgo,
    QuantFormat,
    QuantizationConfig,
)
from utils import check_conv_and_mha, check_lora

import modelopt.torch.opt as mto
import modelopt.torch.quantization as mtq
from modelopt.torch.export import export_hf_checkpoint


def setup_logging(verbose: bool = False) -> logging.Logger:
    """
    Set up logging configuration.

    Args:
        verbose: Enable verbose logging

    Returns:
        Configured logger instance
    """
    log_level = logging.DEBUG if verbose else logging.INFO

    # Create custom formatter
    formatter = logging.Formatter(
        fmt="%(asctime)s | %(levelname)-8s | %(name)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
    )

    # Set up console handler
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(formatter)

    # Configure root logger
    logger = logging.getLogger(__name__)
    logger.setLevel(log_level)
    logger.addHandler(console_handler)

    # Optionally reduce noise from other libraries
    logging.getLogger("diffusers").setLevel(logging.WARNING)
    logging.getLogger("transformers").setLevel(logging.WARNING)

    return logger


class Quantizer:
    """Handles model quantization operations."""

    def __init__(
        self, config: QuantizationConfig, model_config: ModelConfig, logger: logging.Logger
    ):
        """
        Initialize quantizer.

        Args:
            config: Quantization configuration
            model_config: Model configuration
            logger: Logger instance
        """
        self.config = config
        self.model_config = model_config
        self.logger = logger

    def get_quant_config(self, n_steps: int, backbone: torch.nn.Module) -> Any:
        """
        Build quantization configuration based on format.

        Args:
            n_steps: Number of denoising steps

        Returns:
            Quantization configuration object
        """
        self.logger.info(f"Building quantization config for {self.config.format.value}")

        apply_int8_percentile_calibrator = False
        if self.config.format == QuantFormat.INT8:
            if self.config.algo == QuantAlgo.SMOOTHQUANT:
                base_cfg = mtq.INT8_SMOOTHQUANT_CFG
            else:
                base_cfg = INT8_DEFAULT_CONFIG
            apply_int8_percentile_calibrator = self.config.collect_method != CollectMethod.DEFAULT
        elif self.config.format == QuantFormat.FP8:
            base_cfg = FP8_DEFAULT_CONFIG
        elif self.config.format == QuantFormat.FP4:
            if self.model_config.model_type.value.startswith("flux"):
                base_cfg = NVFP4_FP8_MHA_CONFIG
            else:
                base_cfg = NVFP4_DEFAULT_CONFIG
        else:
            raise NotImplementedError(f"Unknown format {self.config.format}")

        # Build a fresh config dict so runtime overrides never mutate the global constants.
        base_cfg = copy.deepcopy(base_cfg)

        if apply_int8_percentile_calibrator:
            reset_set_int8_config(
                base_cfg,
                self.config.percentile,
                n_steps,
                collect_method=self.config.collect_method.value,
                backbone=backbone,
            )

        quant_cfg_list = list(base_cfg["quant_cfg"])

        if self.config.format == QuantFormat.FP4:
            for i, entry in enumerate(quant_cfg_list):
                if isinstance(entry, dict) and "block_sizes" in entry.get("cfg", {}):
                    new_block_sizes = {**entry["cfg"]["block_sizes"], -1: self.config.block_size}
                    quant_cfg_list[i] = {
                        **entry,
                        "cfg": {**entry["cfg"], "block_sizes": new_block_sizes},
                    }

        if self.config.quantize_mha:
            quant_cfg_list.append(
                {
                    "quantizer_name": "*[qkv]_bmm_quantizer",
                    "cfg": {"num_bits": (4, 3), "axis": None},
                }
            )

        # Apply the transformer-block-range recipe (e.g. Qwen-Image) BEFORE
        # calibration. This restricts quantization to `transformer_blocks` and
        # excludes the first/last N blocks. It must run before calibration so that
        # SVDQuant does not mutate the weights of the excluded blocks. The recipe
        # is format-agnostic (applies to FP8/NVFP4/SVDQuant alike).
        block_range = MODEL_DEFAULTS.get(self.model_config.model_type, {}).get("block_range")
        if block_range is not None:
            recipe_rules = build_block_range_quant_cfg(
                backbone,
                exclude_first_n=block_range.get("exclude_first_n", 2),
                exclude_last_n=block_range.get("exclude_last_n", 2),
                block_module=block_range.get("block_module", "transformer_blocks"),
            )
            self.logger.info(
                f"Applying block-range recipe ({len(recipe_rules)} rules) for "
                f"{self.model_config.model_type.value}: quantize only "
                f"'{block_range.get('block_module', 'transformer_blocks')}' excluding "
                f"first {block_range.get('exclude_first_n', 2)} / last "
                f"{block_range.get('exclude_last_n', 2)} blocks."
            )
            quant_cfg_list.extend(recipe_rules)

        # Per-model SVDQuant exclusions (e.g. Qwen-Image's text-stream linears):
        # matching layers skip the SVDQuant low-rank branch and AWQ smoothing but
        # stay quantized with plain max calibration.
        svdquant_skip_layers = None
        if self.config.algo == QuantAlgo.SVDQUANT:
            svdquant_skip_layers = MODEL_DEFAULTS.get(self.model_config.model_type, {}).get(
                "svdquant_skip_layers"
            )
            if svdquant_skip_layers:
                self.logger.info(
                    f"SVDQuant skip patterns for {self.model_config.model_type.value} "
                    f"(plain quantization): {svdquant_skip_layers}"
                )

        quant_config = {**base_cfg, "quant_cfg": quant_cfg_list}
        set_quant_config_attr(
            quant_config,
            self.model_config.trt_high_precision_dtype.value,
            self.config.algo.value,
            alpha=self.config.alpha,
            lowrank=self.config.lowrank,
            skip_layers=svdquant_skip_layers,
        )
        self.logger.info(f"Quant config {quant_config}")
        return quant_config

    def quantize_model(
        self,
        backbone: torch.nn.Module,
        quant_config: Any,
        forward_loop: callable,  # type: ignore[valid-type]
        backbone_name: str = "transformer",
    ) -> torch.nn.Module:
        """
        Apply quantization to the model.

        Args:
            backbone: Model backbone to quantize
            quant_config: Quantization configuration
            forward_loop: Forward pass function for calibration
            backbone_name: Name of the backbone being quantized
        """
        self.logger.info("Checking for LoRA layers...")
        check_lora(backbone)

        self.logger.info(f"Starting model quantization for {backbone_name}...")
        mtq.quantize(backbone, quant_config, forward_loop)
        # Get model-specific filter function
        model_filter_func = get_model_filter_func(self.model_config.model_type, backbone_name)
        self.logger.info(
            f"Using filter function for {self.model_config.model_type.value}/{backbone_name}"
        )

        self.logger.info("Disabling specific quantizers...")
        mtq.disable_quantizer(backbone, model_filter_func)

        self.logger.info("Quantization completed successfully")
        return backbone


class ExportManager:
    """Handles model export operations."""

    def __init__(
        self,
        config: ExportConfig,
        logger: logging.Logger,
        pipeline_manager: PipelineManager | None = None,
    ):
        """
        Initialize export manager.

        Args:
            config: Export configuration
            logger: Logger instance
            pipeline_manager: Pipeline manager for per-backbone IO
        """
        self.config = config
        self.logger = logger
        self.pipeline_manager = pipeline_manager

    def _has_conv_layers(self, model: torch.nn.Module) -> bool:
        """
        Check if the model contains any convolutional layers.

        Args:
            model: Model to check

        Returns:
            True if model contains Conv layers, False otherwise
        """
        for module in model.modules():
            if isinstance(module, (torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d)) and (
                module.input_quantizer.is_enabled or module.weight_quantizer.is_enabled
            ):
                return True
        return False

    def save_checkpoint(
        self,
        backbone: torch.nn.Module,
        backbone_name: str | None = None,
    ) -> None:
        """
        Save quantized model checkpoint.

        Args:
            backbone: The quantized backbone module to save (must be the same instance
                that was passed to mtq.quantize, as it carries the _modelopt_state).
            backbone_name: Optional name for the backbone file (defaults to "backbone").
        """
        if not self.config.quantized_torch_ckpt_path:
            return

        ckpt_path = self.config.quantized_torch_ckpt_path
        ckpt_path.mkdir(parents=True, exist_ok=True)
        filename = f"{backbone_name}.pt" if backbone_name else "backbone.pt"
        target_path = ckpt_path / filename

        self.logger.info(f"Saving backbone to {target_path}")
        mto.save(backbone, str(target_path))

        self.logger.info("Checkpoint saved successfully")

    def export_onnx(
        self,
        pipe: DiffusionPipeline,
        backbone: torch.nn.Module,
        model_type: ModelType,
        quant_format: QuantFormat,
    ) -> None:
        """
        Export model to ONNX format.

        Args:
            pipe: Diffusion pipeline
            backbone: Model backbone
            model_type: Type of model
            quant_format: Quantization format
        """
        if not self.config.onnx_dir:
            return

        # Deferred: the ONNX stack (onnx, onnx_graphsurgeon, ...) is only needed
        # for --onnx-dir exports; HF-checkpoint-only runs must not require it.
        from onnx_utils.export import generate_fp8_scales, modelopt_export_sd

        self.logger.info(f"Starting ONNX export to {self.config.onnx_dir}")

        if quant_format == QuantFormat.FP8 and self._has_conv_layers(backbone):
            self.logger.info(
                "Detected quantizing conv layers in backbone. Generating FP8 scales..."
            )
            generate_fp8_scales(backbone)
        self.logger.info("Preparing models for export...")
        pipe.to("cpu")
        torch.cuda.empty_cache()
        backbone.to("cuda")
        # Export to ONNX
        backbone.eval()
        with torch.no_grad():
            self.logger.info("Exporting to ONNX...")
            modelopt_export_sd(
                backbone, str(self.config.onnx_dir), model_type.value, quant_format.value
            )

        self.logger.info("ONNX export completed successfully")

    def restore_checkpoint(self) -> None:
        """
        Restore a previously quantized model.

        """
        if not self.config.restore_from:
            return

        restore_path = self.config.restore_from
        if self.pipeline_manager is None:
            raise RuntimeError("Pipeline manager is required for per-backbone checkpoints.")

        if not restore_path.exists() or not restore_path.is_dir():
            raise FileNotFoundError(f"Checkpoint directory not found: {restore_path}")

        for backbone_name, backbone in self.pipeline_manager.iter_backbones():
            source_path = restore_path / f"{backbone_name}.pt"
            if not source_path.exists():
                raise FileNotFoundError(
                    f"Checkpoint not found for '{backbone_name}' in {restore_path}"
                )
            self.logger.info(f"Restoring {backbone_name} from {source_path}")
            mto.restore(backbone, str(source_path))

        self.logger.info("Checkpoints restored successfully")

    # TODO: should not do the any data type
    def export_hf_ckpt(self, pipe: Any, model_config: ModelConfig | None = None) -> None:
        """
        Export quantized model to HuggingFace checkpoint format.

        Args:
            pipe: Diffusion pipeline containing the quantized model
            model_config: Model configuration (used to pass model-specific export kwargs)
        """
        if not self.config.hf_ckpt_dir:
            return

        self.logger.info(f"Exporting HuggingFace checkpoint to {self.config.hf_ckpt_dir}")
        kwargs: dict[str, Any] = {}
        if model_config and model_config.model_type == ModelType.LTX2:
            merged_path = model_config.extra_params.get("merged_base_safetensor_path")
            if merged_path:
                self.logger.info(f"Merging base safetensors from {merged_path} for LTX2 export")
                kwargs["merged_base_safetensor_path"] = merged_path
        if model_config:
            for key in ("enable_swizzle_layout", "enable_layerwise_quant_metadata"):
                val = model_config.extra_params.get(key)
                if val is not None:
                    normalized = str(val).strip().lower()
                    if normalized in ("true", "1", "yes"):
                        kwargs[key] = True
                    elif normalized in ("false", "0", "no"):
                        kwargs[key] = False
                    else:
                        raise ValueError(
                            f"Invalid value for {key}: {val!r}. "
                            "Expected true/false, 1/0, or yes/no."
                        )
            padding = model_config.extra_params.get("padding_strategy")
            if padding is not None:
                padding = str(padding).strip().lower()
                if padding not in ("row", "row_col"):
                    raise ValueError(
                        f"Invalid padding_strategy: {padding!r}. Expected 'row' or 'row_col'."
                    )
                kwargs["padding_strategy"] = padding
        export_hf_checkpoint(pipe, export_dir=self.config.hf_ckpt_dir, **kwargs)
        self.logger.info("HuggingFace checkpoint export completed successfully")


def create_argument_parser() -> argparse.ArgumentParser:
    """
    Create and configure argument parser.

    Returns:
        Configured argument parser
    """
    parser = argparse.ArgumentParser(
        description="Enhanced Diffusion Model Quantization Tool",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
            Examples:
            # Basic INT8 quantization with SmoothQuant
            %(prog)s --model flux-dev --format int8 --quant-algo smoothquant --collect-method global_min

            # FP8 quantization with ONNX export
            %(prog)s --model sd3-medium --format fp8 --onnx-dir ./onnx_models/

            # FP8 quantization with weight compression (reduces memory footprint)
            %(prog)s --model flux-dev --format fp8 --compress

            # Quantize LTX-Video model with full multi-stage pipeline
            %(prog)s --model ltx-video-dev --format fp8 --batch-size 1 --calib-size 32

            # Faster LTX-Video quantization (skip upsampler)
            %(prog)s --model ltx-video-dev --format fp8 --batch-size 1 --calib-size 32 --ltx-skip-upsampler

            # Restore and export a previously quantized model
            %(prog)s --model flux-schnell --restore-from checkpoint.pt --onnx-dir ./exports/
        """,
    )
    model_group = parser.add_argument_group("Model Configuration")
    model_group.add_argument(
        "--model",
        type=str,
        default="flux-dev",
        choices=[m.value for m in ModelType],
        help="Model to load and quantize",
    )
    model_group.add_argument(
        "--backbone",
        nargs="+",
        default=None,
        help=(
            "Model backbone(s) in the DiffusionPipeline to quantize. "
            "Provide one or more names (e.g., 'transformer', 'video_decoder'). "
            "If not provided, uses default based on model type."
        ),
    )
    model_group.add_argument(
        "--model-dtype",
        type=str,
        default="Half",
        choices=[d.value for d in DataType],
        help="Precision for loading the pipeline. If you want different dtypes for separate components, "
        "please specify using --component-dtype",
    )
    model_group.add_argument(
        "--component-dtype",
        action="append",
        default=[],
        help="Precision for loading each component of the model by format of name:dtype. "
        "You can specify multiple components. "
        "Example: --component-dtype vae:Half --component-dtype transformer:BFloat16",
    )
    model_group.add_argument(
        "--override-model-path", type=str, help="Custom path to model (overrides default)"
    )
    model_group.add_argument(
        "--cpu-offloading", action="store_true", help="Enable CPU offloading for limited VRAM"
    )
    model_group.add_argument(
        "--ltx-skip-upsampler",
        action="store_true",
        help="Skip upsampler pipeline for LTX-Video (faster calibration, only quantizes main transformer)",
    )
    model_group.add_argument(
        "--extra-param",
        action="append",
        default=[],
        metavar="KEY=VALUE",
        help=(
            "Extra model-specific parameters in KEY=VALUE form. Can be provided multiple times. "
            "These override model-specific CLI arguments when present."
        ),
    )
    quant_group = parser.add_argument_group("Quantization Configuration")
    quant_group.add_argument(
        "--format",
        type=str,
        default="int8",
        choices=[f.value for f in QuantFormat],
        help="Quantization format",
    )
    quant_group.add_argument(
        "--quant-algo",
        type=str,
        default="max",
        choices=[a.value for a in QuantAlgo],
        help="Quantization algorithm",
    )
    quant_group.add_argument(
        "--percentile",
        type=float,
        default=1.0,
        help="Percentile for calibration, works for INT8, not including smoothquant",
    )
    quant_group.add_argument(
        "--collect-method",
        type=str,
        default="default",
        choices=[c.value for c in CollectMethod],
        help="Calibration collection method, works for INT8, not including smoothquant",
    )
    quant_group.add_argument("--alpha", type=float, default=1.0, help="SmoothQuant alpha parameter")
    quant_group.add_argument("--lowrank", type=int, default=32, help="SVDQuant lowrank parameter")
    quant_group.add_argument(
        "--quantize-mha", action="store_true", help="Quantizing MHA into FP8 if its True"
    )
    quant_group.add_argument(
        "--compress",
        action="store_true",
        help="Compress quantized weights to reduce memory footprint (FP8/FP4 only)",
    )
    quant_group.add_argument(
        "--block-size",
        type=int,
        default=16,
        help="Block size for NVFP4 quantization (default: 16)",
    )

    calib_group = parser.add_argument_group("Calibration Configuration")
    calib_group.add_argument("--batch-size", type=int, default=2, help="Batch size for calibration")
    calib_group.add_argument(
        "--calib-size", type=int, default=128, help="Total number of calibration samples"
    )
    calib_group.add_argument("--n-steps", type=int, default=30, help="Number of denoising steps")
    calib_group.add_argument(
        "--prompts-file",
        type=str,
        default=None,
        help="Calibrate using prompts in the file instead of the default dataset.",
    )

    export_group = parser.add_argument_group("Export Configuration")
    export_group.add_argument(
        "--quantized-torch-ckpt-save-path",
        type=str,
        help="Path to save quantized PyTorch checkpoint",
    )
    export_group.add_argument("--onnx-dir", type=str, help="Directory for ONNX export")
    export_group.add_argument(
        "--hf-ckpt-dir",
        type=str,
        help="Directory for HuggingFace checkpoint export",
    )
    export_group.add_argument(
        "--restore-from", type=str, help="Path to restore from previous checkpoint"
    )
    export_group.add_argument(
        "--trt-high-precision-dtype",
        type=str,
        default="Half",
        choices=[d.value for d in DataType],
        help="Precision for TensorRT high-precision layers",
    )
    parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")

    return parser


def main() -> None:
    from diffusers.models.normalization import RMSNorm as DiffuserRMSNorm

    torch.nn.RMSNorm = DiffuserRMSNorm
    torch.nn.modules.normalization.RMSNorm = DiffuserRMSNorm

    parser = create_argument_parser()
    args, unknown_args = parser.parse_known_args()

    model_type = ModelType(args.model)
    if args.backbone is None:
        args.backbone = [MODEL_DEFAULTS[model_type]["backbone"]]
    s = time.time()

    model_dtype = {"default": DataType(args.model_dtype).torch_dtype}
    for component_dtype in args.component_dtype:
        component, dtype = component_dtype.split(":")
        model_dtype[component] = DataType(dtype).torch_dtype

    logger = setup_logging(args.verbose)
    logger.info("Starting Enhanced Diffusion Model Quantization")

    try:
        extra_params = parse_extra_params(args.extra_param, unknown_args, logger)
        model_config = ModelConfig(
            model_type=model_type,
            model_dtype=model_dtype,
            backbone=args.backbone,
            trt_high_precision_dtype=DataType(args.trt_high_precision_dtype),
            override_model_path=Path(args.override_model_path)
            if args.override_model_path
            else None,
            cpu_offloading=args.cpu_offloading,
            ltx_skip_upsampler=args.ltx_skip_upsampler,
            extra_params=extra_params,
        )

        quant_config = QuantizationConfig(
            format=QuantFormat(args.format),
            algo=QuantAlgo(args.quant_algo),
            percentile=args.percentile,
            collect_method=CollectMethod(args.collect_method),
            alpha=args.alpha,
            lowrank=args.lowrank,
            quantize_mha=args.quantize_mha,
            compress=args.compress,
            block_size=args.block_size,
        )

        if args.prompts_file is not None:
            prompts_file = Path(args.prompts_file)
            assert prompts_file.exists(), (
                f"User specified prompts file {prompts_file} does not exist."
            )
            prompts_dataset = prompts_file
        else:
            prompts_dataset = MODEL_DEFAULTS[model_type]["dataset"]
        calib_config = CalibrationConfig(
            prompts_dataset=prompts_dataset,
            batch_size=args.batch_size,
            calib_size=args.calib_size,
            n_steps=args.n_steps,
        )

        export_config = ExportConfig(
            quantized_torch_ckpt_path=Path(args.quantized_torch_ckpt_save_path)
            if args.quantized_torch_ckpt_save_path
            else None,
            onnx_dir=Path(args.onnx_dir) if args.onnx_dir else None,
            hf_ckpt_dir=Path(args.hf_ckpt_dir) if args.hf_ckpt_dir else None,
            restore_from=Path(args.restore_from) if args.restore_from else None,
        )

        logger.info("Validating configurations...")
        quant_config.validate()
        export_config.validate()
        if not export_config.restore_from:
            calib_config.validate()

        pipeline_manager = PipelineManager(model_config, logger)
        pipe = pipeline_manager.create_pipeline()
        pipeline_manager.setup_device()

        export_manager = ExportManager(export_config, logger, pipeline_manager)

        if export_config.restore_from and export_config.restore_from.exists():
            export_manager.restore_checkpoint()

        else:
            logger.info("Initializing calibration...")
            calibrator = Calibrator(pipeline_manager, calib_config, model_config.model_type, logger)
            batched_prompts = calibrator.load_and_batch_prompts()
            quantizer = Quantizer(quant_config, model_config, logger)

            for backbone_name, backbone in pipeline_manager.iter_backbones():
                logger.info(f"Quantizing backbone: {backbone_name}")
                backbone_quant_config = quantizer.get_quant_config(calib_config.n_steps, backbone)

                # Calibration runs the full pipeline (not just `mod`), so the
                # closure intentionally ignores the backbone argument.
                def forward_loop(mod):
                    calibrator.run_calibration(batched_prompts)

                quantizer.quantize_model(
                    backbone,
                    backbone_quant_config,
                    forward_loop,
                    backbone_name=backbone_name,
                )

                # Compress model weights if requested (only for FP8/FP4)
                if quant_config.compress:
                    logger.info(f"Compressing {backbone_name} weights...")
                    mtq.compress(backbone)
                    logger.info(f"{backbone_name} compression completed")

                # For VAE backbones, skip check_conv_and_mha — the whole point
                # of VAE quantization is to quantize Conv layers.
                if backbone_name not in ("video_decoder", "vae"):
                    check_conv_and_mha(
                        backbone, quant_config.format == QuantFormat.FP4, quant_config.quantize_mha
                    )

                export_manager.save_checkpoint(backbone, backbone_name)

        pipeline_manager.print_quant_summary()

        for backbone_name, backbone in pipeline_manager.iter_backbones():
            export_manager.export_onnx(
                pipe,
                backbone,
                model_config.model_type,
                quant_config.format,
            )

        export_manager.export_hf_ckpt(pipe, model_config)

        logger.info(
            f"Quantization process completed successfully! Time taken = {time.time() - s} seconds"
        )

    except Exception as e:
        logger.error(f"Quantization failed: {e}", exc_info=True)
        sys.exit(1)


if __name__ == "__main__":
    main()