# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
# Copyright (c) 2024 Baidu. All Rights Reserved.
# ------------------------------------------------------------------------

"""
export ONNX model and TensorRT engine for deployment
"""
import os
import ast
import random
import argparse
import subprocess
import torch.nn as nn
from pathlib import Path
import time
from collections import defaultdict

import onnx
import torch
import onnxsim
import numpy as np
from PIL import Image

import rfdetr.util.misc as utils
import rfdetr.datasets.transforms as T
from rfdetr.models import build_model
from rfdetr.deploy._onnx import OnnxOptimizer
import re
import sys


def run_command_shell(command, dry_run:bool = False) -> int:
    if dry_run:
        print("")
        print(f"CUDA_VISIBLE_DEVICES={os.environ['CUDA_VISIBLE_DEVICES']} {command}")
        print("")
    try:
        result = subprocess.run(command, shell=True, capture_output=True, text=True)
        return result
    except subprocess.CalledProcessError as e:
        print(f"Command failed with exit code {e.returncode}")
        print(f"Error output:\n{e.stderr.decode('utf-8')}")
        raise


def make_infer_image(infer_dir, shape, batch_size, device="cuda"):
    if infer_dir is None:
        dummy = np.random.randint(0, 256, (shape[0], shape[1], 3), dtype=np.uint8)
        image = Image.fromarray(dummy, mode="RGB")
    else:
        image = Image.open(infer_dir).convert("RGB")

    transforms = T.Compose([
        T.SquareResize([shape[0]]),
        T.ToTensor(),
        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    inps, _ = transforms(image, None)
    inps = inps.to(device)
    # inps = utils.nested_tensor_from_tensor_list([inps for _ in range(args.batch_size)])
    inps = torch.stack([inps for _ in range(batch_size)])
    return inps

def export_onnx(output_dir, model, input_names, input_tensors, output_names, dynamic_axes, backbone_only=False, verbose=True, opset_version=17):
    export_name = "backbone_model" if backbone_only else "inference_model"
    output_file = os.path.join(output_dir, f"{export_name}.onnx")
    
    # Prepare model for export
    if hasattr(model, "export"):
        model.export()
    
    torch.onnx.export(
        model,
        input_tensors,
        output_file,
        input_names=input_names,
        output_names=output_names,
        export_params=True,
        keep_initializers_as_inputs=False,
        do_constant_folding=True,
        verbose=verbose,
        opset_version=opset_version,
        dynamic_axes=dynamic_axes)

    print(f'\nSuccessfully exported ONNX model: {output_file}')
    return output_file


def onnx_simplify(onnx_dir:str, input_names, input_tensors, force=False):
    sim_onnx_dir = onnx_dir.replace(".onnx", ".sim.onnx")
    if os.path.isfile(sim_onnx_dir) and not force:
        return sim_onnx_dir
    
    if isinstance(input_tensors, torch.Tensor):
        input_tensors = [input_tensors]
    
    print(f'start simplify ONNX model: {onnx_dir}')
    opt = OnnxOptimizer(onnx_dir)
    opt.info('Model: original')
    opt.common_opt()
    opt.info('Model: optimized')
    opt.save_onnx(sim_onnx_dir)
    input_dict = {name: tensor.detach().cpu().numpy() for name, tensor in zip(input_names, input_tensors)}
    model_opt, check_ok = onnxsim.simplify(
        onnx_dir,
        check_n = 3,
        input_data=input_dict,
        dynamic_input_shape=False)
    if check_ok:
        onnx.save(model_opt, sim_onnx_dir)
    else:
        raise RuntimeError("Failed to simplify ONNX model.")
    print(f'Successfully simplified ONNX model: {sim_onnx_dir}')
    return sim_onnx_dir


def trtexec(onnx_dir:str, args) -> None:
    engine_dir = onnx_dir.replace(".onnx", f".engine")
    
    # Base trtexec command
    trt_command = " ".join([
        "trtexec",
            f"--onnx={onnx_dir}",
            f"--saveEngine={engine_dir}",
            f"--memPoolSize=workspace:4096 --fp16",
            f"--useCudaGraph --useSpinWait --warmUp=500 --avgRuns=1000 --duration=10",
            f"{'--verbose' if args.verbose else ''}"])
    
    if args.profile:
        profile_dir = onnx_dir.replace(".onnx", f".nsys-rep")
        # Wrap with nsys profile command
        command = " ".join([
            "nsys profile",
                f"--output={profile_dir}",
                "--trace=cuda,nvtx",
                "--force-overwrite true",
                trt_command
        ])
        print(f'Profile data will be saved to: {profile_dir}')
    else:
        command = trt_command

    output = run_command_shell(command, args.dry_run)
    stats = parse_trtexec_output(output.stdout)

def parse_trtexec_output(output_text):
    print(output_text)
    # Common patterns in trtexec output
    gpu_compute_pattern = r"GPU Compute Time: min = (\d+\.\d+) ms, max = (\d+\.\d+) ms, mean = (\d+\.\d+) ms, median = (\d+\.\d+) ms"
    h2d_pattern = r"Host to Device Transfer Time: min = (\d+\.\d+) ms, max = (\d+\.\d+) ms, mean = (\d+\.\d+) ms"
    d2h_pattern = r"Device to Host Transfer Time: min = (\d+\.\d+) ms, max = (\d+\.\d+) ms, mean = (\d+\.\d+) ms"
    latency_pattern = r"Latency: min = (\d+\.\d+) ms, max = (\d+\.\d+) ms, mean = (\d+\.\d+) ms"
    throughput_pattern = r"Throughput: (\d+\.\d+) qps"
    
    stats = {}
    
    # Extract compute times
    if match := re.search(gpu_compute_pattern, output_text):
        stats.update({
            'compute_min_ms': float(match.group(1)),
            'compute_max_ms': float(match.group(2)),
            'compute_mean_ms': float(match.group(3)),
            'compute_median_ms': float(match.group(4))
        })
    
    # Extract H2D times
    if match := re.search(h2d_pattern, output_text):
        stats.update({
            'h2d_min_ms': float(match.group(1)),
            'h2d_max_ms': float(match.group(2)),
            'h2d_mean_ms': float(match.group(3))
        })
    
    # Extract D2H times
    if match := re.search(d2h_pattern, output_text):
        stats.update({
            'd2h_min_ms': float(match.group(1)),
            'd2h_max_ms': float(match.group(2)),
            'd2h_mean_ms': float(match.group(3))
        })

    if match := re.search(latency_pattern, output_text):
        stats.update({
            'latency_min_ms': float(match.group(1)),
            'latency_max_ms': float(match.group(2)),
            'latency_mean_ms': float(match.group(3))
        })
    
    # Extract throughput
    if match := re.search(throughput_pattern, output_text):
        stats['throughput_qps'] = float(match.group(1))
    
    return stats

def no_batch_norm(model):
    for module in model.modules():
        if isinstance(module, nn.BatchNorm2d):
            raise ValueError("BatchNorm2d found in the model. Please remove it.")

def main(args):
    print("git:\n  {}\n".format(utils.get_sha()))
    print(args)
    # convert device to device_id
    if args.device == 'cuda':
        device_id = "0"
    elif args.device == 'cpu':
        device_id = ""
    else:
        device_id = str(int(args.device))
        args.device = f"cuda:{device_id}"

    # device for export onnx
    # TODO: export onnx with cuda failed with onnx error
    device = torch.device("cpu")
    os.environ["CUDA_VISIBLE_DEVICES"] = device_id

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)

    model, criterion, postprocessors = build_model(args)
    n_parameters = sum(p.numel() for p in model.parameters())
    print(f"number of parameters: {n_parameters}")
    n_backbone_parameters = sum(p.numel() for p in model.backbone.parameters())
    print(f"number of backbone parameters: {n_backbone_parameters}")
    n_projector_parameters = sum(p.numel() for p in model.backbone[0].projector.parameters())
    print(f"number of projector parameters: {n_projector_parameters}")
    n_backbone_encoder_parameters = sum(p.numel() for p in model.backbone[0].encoder.parameters())
    print(f"number of backbone encoder parameters: {n_backbone_encoder_parameters}")
    n_transformer_parameters = sum(p.numel() for p in model.transformer.parameters())
    print(f"number of transformer parameters: {n_transformer_parameters}")
    if args.resume:
        checkpoint = torch.load(args.resume, map_location='cpu')
        model.load_state_dict(checkpoint['model'], strict=True)
        print(f"load checkpoints {args.resume}")

    if args.layer_norm:
        no_batch_norm(model)

    model.to(device)

    input_tensors = make_infer_image(args, device)
    input_names = ['input']
    output_names = ['features'] if args.backbone_only else ['dets', 'labels']
    dynamic_axes = None
    # Run model inference in pytorch mode
    model.eval().to("cuda")
    input_tensors = input_tensors.to("cuda")
    with torch.no_grad():
        if args.backbone_only:
            features = model(input_tensors)
            print(f"PyTorch inference output shape: {features.shape}")
        else:
            outputs = model(input_tensors)
            dets = outputs['pred_boxes']
            labels = outputs['pred_logits']
            print(f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}")
    model.cpu()
    input_tensors = input_tensors.cpu()


    output_file = export_onnx(model, args, input_names, input_tensors, output_names, dynamic_axes)
    
    if args.simplify:
        output_file = onnx_simplify(output_file, input_names, input_tensors, args)

    if args.tensorrt:
        output_file = trtexec(output_file, args)