{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d38f0ec2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-14T16:31:34.792065Z",
     "iopub.status.busy": "2026-01-14T16:31:34.791502Z",
     "iopub.status.idle": "2026-01-14T16:31:34.800360Z",
     "shell.execute_reply": "2026-01-14T16:31:34.798868Z"
    }
   },
   "outputs": [],
   "source": [
    "import subprocess\n",
    "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee5804bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-14T16:31:34.805950Z",
     "iopub.status.busy": "2026-01-14T16:31:34.805393Z",
     "iopub.status.idle": "2026-01-14T16:31:34.817239Z",
     "shell.execute_reply": "2026-01-14T16:31:34.815689Z"
    }
   },
   "outputs": [],
   "source": [
    "commands = []\n",
    "\n",
    "# for itr in range(3):\n",
    "#     for model in ['hybrid', 'T_rope', 'mamba']:\n",
    "#         cmd = f\"python3 main.py --model {model} --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number {itr}\"\n",
    "#         cmd += \" --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.2 --eval_p 0.3\"\n",
    "#         commands.append(cmd)\n",
    "\n",
    "# for eval_p in [0.01, 0.05, 0.1, 0.3, 0.5, 0.8, 0.9]:\n",
    "# for eval_p in [0.01, 0.9]:\n",
    "#     for itr in range(3):\n",
    "#         for model in ['hybrid', 'T_rope', 'mamba']:\n",
    "#             cmd = f\"python3 main.py --model {model} --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number {itr}\"\n",
    "#             cmd += \" --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10\"\n",
    "#             cmd += f\" --p 0.2 --eval_p {eval_p}\"\n",
    "#             commands.append(cmd)\n",
    "\n",
    "for eval_p in [0.01, 0.05, 0.1, 0.3, 0.5, 0.8, 0.9]:\n",
    "    for itr in range(3): # 3\n",
    "        for model in ['hybrid', 'T_rope', 'mamba']:\n",
    "            cmd = f\"python3 main.py --model {model} --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number {itr}\"\n",
    "            cmd += \" --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10\"\n",
    "            # cmd += f\" --p 0.2 --eval_p {eval_p}\"\n",
    "            cmd += f\" --p {eval_p} --eval_p 0.2\"\n",
    "            commands.append(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "90a8b3ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-14T16:31:34.821703Z",
     "iopub.status.busy": "2026-01-14T16:31:34.821259Z",
     "iopub.status.idle": "2026-01-14T16:31:34.828534Z",
     "shell.execute_reply": "2026-01-14T16:31:34.828184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python3 main.py --model hybrid --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 0 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model T_rope --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 0 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model mamba --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 0 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model hybrid --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 1 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model T_rope --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 1 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model mamba --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 1 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model hybrid --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 2 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model T_rope --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 2 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model mamba --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 2 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.01 --eval_p 0.2',\n",
       " 'python3 main.py --model hybrid --train_task decode-recall --eval_task decode-recall --sequence_length 200 --eval_equence_length 200 --lr 0.0003 --epochs 1 --save False --run_number 0 --min_train_length 10 --max_train_length 50 --min_eval_length 10 --max_eval_length 200 --eval_jump_type linear --eval_linear_jump_size 10 --p 0.05 --eval_p 0.2']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commands[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f32ed2ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-01-14T16:31:34.829909Z",
     "iopub.status.busy": "2026-01-14T16:31:34.829478Z",
     "iopub.status.idle": "2026-01-14T19:09:41.606354Z",
     "shell.execute_reply": "2026-01-14T19:09:41.606004Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_command(cmd):\n",
    "    \"\"\"Run a single shell command and return (cmd, returncode, stdout, stderr).\"\"\"\n",
    "    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n",
    "    return cmd, result.returncode, result.stdout, result.stderr\n",
    "\n",
    "max_workers = 1 # 5\n",
    "\n",
    "results = \"\"\n",
    "\n",
    "with ThreadPoolExecutor(max_workers=max_workers) as executor:\n",
    "    futures = {executor.submit(run_command, cmd): cmd for cmd in commands}\n",
    "\n",
    "    for future in as_completed(futures):\n",
    "        cmd, returncode, stdout, stderr = future.result()\n",
    "\n",
    "        if returncode != 0:\n",
    "            print(f\"[{cmd}] exited with {returncode}\")\n",
    "        \n",
    "        if returncode == 1:\n",
    "            executor.shutdown()\n",
    "            print(stdout)\n",
    "            print(stderr)\n",
    "            assert False\n",
    "\n",
    "        results += \"<split>Command: \" + cmd + \"\\n\" + stdout + \"\\n\"\n",
    "\n",
    "with open(\"results/exp.txt\", \"w\") as outfile:\n",
    "    outfile.write(results)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "hybrid",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
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