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#!/bin/bash

echo "正在启动 8 个并行的独立任务，并为每个任务分配不同参数..."

# 1. 在这里预先定义你的参数数组 (a_i)
#    数组元素的数量应该与你的任务数量(8)相匹配。
#    参数可以是任何字符串，比如文件名、配置名、数值等。
save_folder='validation_samples/samples_full9'
sampler_name='Flow'
num_generated_videos=1000
# enable_teacache=False
# params=(
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_4/checkpoint-500/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_7/checkpoint-1000/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_5/checkpoint-500/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-500/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-1000/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_7/checkpoint-500/transformer/diffusion_pytorch_model.safetensors"
#     "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_7/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors"
# )
params=(
    "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-2500/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-3000/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-500/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-1000/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_full/output_Oct14_6/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /s3-code/ywang29/ckpts/Wan2.1-1.3B-ft/ckpts_full_chunk3/output_Oct14_7/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors"
    "--transformer_path /s3-code/ywang29/ckpts/Wan2.1-1.3B-ft/ckpts_full_chunk3/output_Oct14_7/checkpoint-1000/transformer/diffusion_pytorch_model.safetensors"
    ""
)



# 2. 修改循环以遍历数组的索引
#    ${!params[@]} 会获取数组 params 的所有索引 (0 1 2 3 4 5 6 7)
for i in "${!params[@]}"
do
  # 从数组中获取当前索引对应的参数值
  current_param="${params[$i]}"

  # CUDA_VISIBLE_DEVICES=$i 告诉程序只能“看见”并使用第 i 张 GPU
  # python X.py --p "$current_param" 将当前参数传递给脚本
  # & 让命令在后台运行
  echo "启动任务 $i (GPU $i)，参数为: $current_param"
  CUDA_VISIBLE_DEVICES=$i python ./examples/wan2.1/predict_t2v_val2.py --num_generated_videos $num_generated_videos --save_folder $save_folder --sampler_name $sampler_name $current_param &
done

# 'wait' 命令会等待所有后台任务都执行完毕
echo "所有任务已启动。等待它们全部完成..."
wait
echo "所有任务已完成。"