[tab] tab-model-eval/data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_1000_evaluations.jsonl (jsonl)
{
  "metadata": {
    "config_generations": {
      "test_cases_file": "data/eval/handcrafted_test_cases/unit_test_cases.jsonl",
      "generations_file": "data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_1000_generations.jsonl",
      "limit": -1,
      "system_prompt_file": "data/prompts/swe_mini_v1.md",
      "model_name": "default",
      "model_path": "/p/scratch/envcomp/mihir/huggingface/Qwen3-1.7B",
      "server_host": "0.0.0.0",
      "server_port": 30000,
      "context_length": 15000,
      "problem_length": 40960,
      "mem_fraction_static": 0.85,
      "api_key": "EMPTY",
      "tp_size": 4,
      "lora_paths": [
        "/p/scratch/envcomp/mihir/huggingface/shared_data/13032800/checkpoints/iter_0001000/"
      ],
      "temperature": 0.7,
      "top_p": 0.8,
      "presence_penalty": 0.0,
      "top_k": 20,
      "min_p": 0.0,
      "num_samples": 10,
      "max_new_tokens": 5000,
      "concurrency": 4,
      "max_connections": 32,
      "keepalive": 60,
      "max_attempts": 6,
      "timeout": 300.0,
      "launch_server": true,
      "extra_server_args": null
    },
    "config_evaluations": {
      "wandb_project": "llm-coding-agent",
      "wandb_name": "qwen1.7B_lora_bs_16_13032800",
      "wandb_eval_type": "unit-test-v1",
      "wandb_tags": [
        "val_mini",
        "judge_eval"
      ],
      "wandb_id": "eval_13032800",
      "wandb_group": "debug",
      "generations_file": "",
      "evaluations_file": "",
      "eval_step": 0,
      "generations_files": "data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_0_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_1000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_2000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_3000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_4000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_5000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_6000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_7000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_8000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_9000_generations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_10000_generations.jsonl",
      "evaluations_files": "data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_0_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_1000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_2000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_3000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_4000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_5000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_6000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_7000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_8000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_9000_evaluations.jsonl,data/eval/output/13032800/qwen1.7B_lora_bs_16_13032800_10000_evaluations.jsonl",
      "eval_steps": "0,1000,2000,3000,4000,5000,6000,7000,8000,9000,10000",
      "limit": -1,
      "system_prompt_file": "data/prompts/judge_system_prompt_v2.md",
      "judge_name": "default",
      "judge_prompt_file": "data/prompts/judge_prompt_v2.md",
      "judge_prompt_file_with_context": "data/prompts/judge_prompt_v2_with_context.md",
      "use_local_logger": true,
      "local_log_dir": "data/eval/local_logs",
      "judge_model_path": "/p/scratch/envcomp/mihir/huggingface/Qwen3-30B-A3B",
      "server_host": "localhost",
      "server_port": 30000,
      "context_length": 40960,
      "problem_length": 40960,
      "api_key": "EMPTY",
      "mem_fraction_static": 0.85,
      "tp_size": 4,
      "temperature": 0.7,
      "top_p": 0.8,
      "presence_penalty": 1.5,
      "top_k": 20,
      "min_p": 0.0,
      "enable_thinking": true,
      "concurrency": 4,
      "max_connections": 32,
      "keepalive": 60,
      "max_attempts": 6,
      "timeout": 30.0,
      "launch_server": true,
      "extra_server_args": null
    }
  },
  "evaluation_scores": {
    "total_test_cases": 5,
    "num_samples_per_task": 10,
    "total_judge_avg_at_n": 0.3,
    "total_judge_pass_at_n": 2,
    "total_exact_match_avg_at_n": 0.3,
    "total_exact_match_pass_at_n": 2,
    "max_attempts": 6
  },
  "generation_results": [
    {
      "task_id": "eval_delete/0",
      "context": [
        {
          "role": "assistant",
          "content": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  from dataclasses import dataclass
     2  import time
     3  import os
     4  import optax
     5
     6  import dm_pix as pix
     7  import einops
     8  import jax
     9  import jax.numpy as jnp
    10  import numpy as np
    11  import orbax.checkpoint as ocp
    12  from PIL import Image, ImageDraw
    13  import tyro
    14  from flax import nnx
    15
    16  from jasmine.models.genie import GenieDiffusion
    17  from jasmine.utils.dataloader import get_dataloader
    18
    19
    20  @dataclass
    21  class Args:
    22      # Experiment
    23      seed: int = 0
    24      seq_len: int = 16
    25      image_channels: int = 3
    26      image_height: int = 90
    27      image_width: int = 160
    28      data_dir: str = \"data/coinrun_episodes\"
    29      checkpoint: str = \"\"
    30      print_action_indices: bool = True
    31      output_dir: str = \"gifs/\"
    32      # Sampling
    33      batch_size: int = 1
    34      start_frame: int = 1
    35      diffusion_denoise_steps: int = 64
    36      diffusion_corrupt_context_factor: float = 0.1
    37      # Tokenizer checkpoint
    38      tokenizer_dim: int = 512
    39      tokenizer_ffn_dim: int = 2048
    40      latent_patch_dim: int = 32
    41      num_patch_latents: int = 1024
    42      patch_size: int = 16
    43      tokenizer_num_blocks: int = 4
    44      tokenizer_num_heads: int = 8
    45      # LAM checkpoint
    46      lam_dim: int = 512
    47      lam_ffn_dim: int = 2048
    48      latent_action_dim: int = 32
    49      num_actions: int = 6
    50      lam_patch_size: int = 16
    51      lam_num_blocks: int = 4
    52      lam_num_heads: int = 8
    53      use_gt_actions: bool = False
    54      # Dynamics checkpoint
    55      dyna_dim: int = 512
    56      dyna_ffn_dim: int = 2048
    57      dyna_num_blocks: int = 6
    58      dyna_num_heads: int = 8
    59      param_dtype = jnp.float32
    60      dtype = jnp.bfloat16
    61      use_flash_attention: bool = True
    62
    63
    64  args = tyro.cli(Args)
    65
    66  if __name__ == \"__main__\":
    67      \"\"\"
    68      Dimension keys:
    69          B: batch size
    70          T: number of input (conditioning) frames
    71          N: number of patches per frame
    72          S: sequence length
    73          H: height
    74          W: width
    75          E: B * (S - 1)
    76      \"\"\"
    77      jax.distributed.initialize()
    78
    79      rng = jax.random.key(args.seed)
    80
    81      # --- Load Genie checkpoint ---
    82      rngs = nnx.Rngs(rng)
    83      genie = GenieDiffusion(
    84          # Tokenizer
    85          in_dim=args.image_channels,
    86          tokenizer_dim=args.tokenizer_dim,
    87          tokenizer_ffn_dim=args.tokenizer_ffn_dim,
    88          latent_patch_dim=args.latent_patch_dim,
    89          num_patch_latents=args.num_patch_latents,
    90          patch_size=args.patch_size,
    91          tokenizer_num_blocks=args.tokenizer_num_blocks,
    92          tokenizer_num_heads=args.tokenizer_num_heads,
    93          # LAM
    94          lam_dim=args.lam_dim,
    95          lam_ffn_dim=args.lam_ffn_dim,
    96          latent_action_dim=args.latent_action_dim,
    97          num_actions=args.num_actions,
    98          lam_patch_size=args.lam_patch_size,
    99          lam_num_blocks=args.lam_num_blocks,
   100          lam_num_heads=args.lam_num_heads,
   101          lam_co_train=False,
   102          use_gt_actions=args.use_gt_actions,
   103          # Dynamics
   104          dyna_dim=args.dyna_dim,
   105          dyna_ffn_dim=args.dyna_ffn_dim,
   106          dyna_num_blocks=args.dyna_num_blocks,
   107          dyna_num_heads=args.dyna_num_heads,
   108          param_dtype=args.param_dtype,
   109          dtype=args.dtype,
   110          use_flash_attention=args.use_flash_attention,
   111          diffusion_denoise_steps=args.diffusion_denoise_steps,
   112          # FIXME (f.srambical): implement spatiotemporal KV caching and set decode=True
   113          decode=False,
   114          rngs=rngs,
   115      )
   116
   117      # Need to delete lam decoder for checkpoint loading
   118      if not args.use_gt_actions:
   119          assert genie.lam is not None
   120          del genie.lam.decoder
   121
   122      handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
   123      handler_registry.add(
   124          \"model_state\", ocp.args.PyTreeSave, ocp.handlers.PyTreeCheckpointHandler
   125      )
   126      handler_registry.add(
   127          \"model_state\", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
   128      )
   129      checkpoint_options = ocp.CheckpointManagerOptions(
   130          step_format_fixed_length=6,
   131      )
   132      checkpoint_manager = ocp.CheckpointManager(
   133          args.checkpoint,
   134          options=checkpoint_options,
   135          handler_registry=handler_registry,
   136      )
   137
   138      dummy_tx = optax.adamw(
   139          learning_rate=optax.linear_schedule(0.0001, 0.0001, 10000),
   140          b1=0.9,
   141          b2=0.9,
   142          weight_decay=1e-4,
   143          mu_dtype=args.dtype,
   144      )
   145      dummy_optimizer = nnx.ModelAndOptimizer(genie, dummy_tx)
   146
   147      abstract_optimizer = nnx.eval_shape(lambda: dummy_optimizer)
   148      abstract_optimizer_state = nnx.state(abstract_optimizer)
   149      restored = checkpoint_manager.restore(
   150          checkpoint_manager.latest_step(),
   151          args=ocp.args.Composite(
   152              model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
   153          ),
   154      )
   155      restored_optimizer_state = restored[\"model_state\"]
   156      nnx.update(dummy_optimizer, restored_optimizer_state)
   157
   158      # --- Define sampling function ---
   159      def _sampling_fn(model: GenieDiffusion, batch: dict) -> jax.Array:
   160          \"\"\"Runs Genie.sample with pre-defined generation hyper-parameters.\"\"\"
   161          frames = model.sample(
   162              batch,
   163              args.seq_len,
   164              args.diffusion_denoise_steps,
   165              args.diffusion_corrupt_context_factor,
   166          )
   167          return frames
   168
   169      # --- Define autoregressive sampling loop ---
   170      def _autoreg_sample(
   171          genie: GenieDiffusion, rng: jax.Array, batch: dict
   172      ) -> jax.Array:
   173          batch[\"videos\"] = batch[\"videos\"][:, : args.start_frame]
   174          batch[\"rng\"] = rng
   175          generated_vid_BSHWC = _sampling_fn(genie, batch)
   176          return generated_vid_BSHWC
   177
   178      # --- Get video + latent actions ---
   179      array_record_files = [
   180          os.path.join(args.data_dir, x)
   181          for x in os.listdir(args.data_dir)
   182          if x.endswith(\".array_record\")
   183      ]
   184      dataloader = get_dataloader(
   185          array_record_files,
   186          args.seq_len,
   187          args.batch_size,
   188          args.image_height,
   189          args.image_width,
   190          args.image_channels,
   191          # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192          num_workers=0,
   193          prefetch_buffer_size=1,
   194          seed=args.seed,
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   207
   208      # --- Sample + evaluate video ---
   209      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   212      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   213
   214      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: gt shape: {gt.shape}\")
   216      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   217      print(f\"DEBUG: recon shape: {recon.shape}\")
   218
   219      ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   220      psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   221      ssim = jnp.asarray(ssim_vmap(gt, recon))
   222      psnr = jnp.asarray(psnr_vmap(gt, recon))
   223      per_frame_ssim = ssim.mean(0)
   224      per_frame_psnr = psnr.mean(0)
   225      avg_ssim = ssim.mean()
   226      avg_psnr = psnr.mean()
   227
   228      print(\"Per-frame SSIM:\
\", per_frame_ssim)
   229      print(\"Per-frame PSNR:\
\", per_frame_psnr)
   230
   231      print(f\"SSIM: {avg_ssim}\")
   232      print(f\"PSNR: {avg_psnr}\")
   233
   234      # --- Construct video ---
   235      true_videos = (gt_video * 255).astype(np.uint8)
   236      pred_videos = (recon_video_BSHWC * 255).astype(np.uint8)
   237      video_comparison = np.zeros((2, *recon_video_BSHWC.shape), dtype=np.uint8)
   238      video_comparison[0] = true_videos[:, : args.seq_len]
   239      video_comparison[1] = pred_videos
   240      frames = einops.rearrange(video_comparison, \"n b t h w c -> t (b h) (n w) c\")
   241
   242      # --- Save video ---
   243      imgs = [Image.fromarray(img) for img in frames]
   244      # Write actions on each frame, on each row (i.e., for each video in the batch, on the GT row)
   245      B = batch[\"videos\"].shape[0]
   246      if action_batch_E is not None:
   247          action_batch_BSm11 = jnp.reshape(action_batch_E, (B, args.seq_len - 1, 1))
   248      else:
   249          action_batch_BSm11 = jnp.reshape(
   250              batch[\"actions\"][:, :-1], (B, args.seq_len - 1, 1)
   251          )
   252      for t, img in enumerate(imgs[1:]):
   253          d = ImageDraw.Draw(img)
   254          for row in range(B):
   255              if args.print_action_indices:
   256                  action = action_batch_BSm11[row, t, 0]
   257                  y_offset = row * batch[\"videos\"].shape[2] + 2
   258                  d.text((2, y_offset), f\"{action}\", fill=255)
   259
   260      os.makedirs(args.output_dir, exist_ok=True)
   261      imgs[0].save(
   262          os.path.join(args.output_dir, f\"generation_{time.time()}.gif\"),
   263          save_all=True,
   264          append_images=imgs[1:],
   265          duration=250,
   266          loop=0,
   267      )
   268      # Save predicted videos as PNG image with all frames (skipping first 4) for each item in batch
   269      skip = 4
   270      # pred_videos: (B, T, H, W, C)
   271      B, T, H, W, C = pred_videos.shape
   272
   273      for i in range(B):
   274          # Predicted
   275          pred_strip = np.concatenate(
   276              [pred_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   277          )  # resulting shape: (H, (T-skip)*W, C)
   278          pred_img = Image.fromarray(pred_strip)
   279
   280          # Write actions on each frame for the predicted strip
   281          if args.print_action_indices:
   282              d = ImageDraw.Draw(pred_img)
   283              for t in range(skip, args.seq_len):
   284                  if action_batch_E is not None:
   285                      action = int(action_batch_BSm11[i, t - 1, 0])
   286                  else:
   287                      action = int(batch[\"actions\"][i, t - 1])
   288                  x_offset = (t - skip) * pred_videos.shape[3] + 2
   289                  y_offset = 2
   290                  d.text((x_offset, y_offset), f\"{action}\", fill=255)
   291
   292          pred_img.save(
   293              os.path.join(
   294                  args.output_dir, f\"generation-{args.seed}-sample{i}-diffusion.png\"
   295              )
   296          )
   297          # GT
   298          gt_strip = np.concatenate(
   299              [true_videos[i, t] for t in range(skip, args.seq_len)], axis=1
   300          )  # resulting shape: (H, (T-skip)*W, C)
   301          gt_img = Image.fromarray(gt_strip)
   302          gt_img.save(
   303              os.path.join(args.output_dir, f\"generation-{args.seed}-sample{i}-gt.png\")
   304          )</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,206p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      print(f\"DEBUG: gt_video shape: {gt_video.shape}\")
   200      batch[\"videos\"] = gt_video.astype(args.dtype)
   201      # Get latent actions for all videos in the batch
   202      action_batch_E = None
   203      if not args.use_gt_actions:
   204          action_batch_E = genie.vq_encode(batch, training=False)
   205          batch[\"latent_actions\"] = action_batch_E
   206          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '199,199d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '189,209p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   189          args.image_width,
   190          args.image_channels,
   191          # We don't use workers in order to avoid grain shutdown issues (https://github.com/google/grain/issues/398)
   192          num_workers=0,
   193          prefetch_buffer_size=1,
   194          seed=args.seed,
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      batch[\"videos\"] = gt_video.astype(args.dtype)
   200      # Get latent actions for all videos in the batch
   201      action_batch_E = None
   202      if not args.use_gt_actions:
   203          action_batch_E = genie.vq_encode(batch, training=False)
   204          batch[\"latent_actions\"] = action_batch_E
   205          print(f\"DEBUG: action_batch_E shape: {action_batch_E.shape}\")
   206
   207      # --- Sample + evaluate video ---
   208      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   209      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '205,205d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '195,215p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   195      )
   196      dataloader = iter(dataloader)
   197      batch = next(dataloader)
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      batch[\"videos\"] = gt_video.astype(args.dtype)
   200      # Get latent actions for all videos in the batch
   201      action_batch_E = None
   202      if not args.use_gt_actions:
   203          action_batch_E = genie.vq_encode(batch, training=False)
   204          batch[\"latent_actions\"] = action_batch_E
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '205,216p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   209      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   210      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   211
   212      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: gt shape: {gt.shape}\")
   214      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   215      print(f\"DEBUG: recon shape: {recon.shape}\")
   216
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '208,208d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '198,218p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   198      gt_video = jnp.asarray(batch[\"videos\"], dtype=jnp.float32) / 255.0
   199      batch[\"videos\"] = gt_video.astype(args.dtype)
   200      # Get latent actions for all videos in the batch
   201      action_batch_E = None
   202      if not args.use_gt_actions:
   203          action_batch_E = genie.vq_encode(batch, training=False)
   204          batch[\"latent_actions\"] = action_batch_E
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   209      print(f\"DEBUG: recon_video_BSHWC shape: {recon_video_BSHWC.shape}\")
   210
   211      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   212      print(f\"DEBUG: gt shape: {gt.shape}\")
   213      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   214      print(f\"DEBUG: recon shape: {recon.shape}\")
   215
   216      ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   217      psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   218      ssim = jnp.asarray(ssim_vmap(gt, recon))
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '209,209d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '199,219p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   199      batch[\"videos\"] = gt_video.astype(args.dtype)
   200      # Get latent actions for all videos in the batch
   201      action_batch_E = None
   202      if not args.use_gt_actions:
   203          action_batch_E = genie.vq_encode(batch, training=False)
   204          batch[\"latent_actions\"] = action_batch_E
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   209
   210      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   211      print(f\"DEBUG: gt shape: {gt.shape}\")
   212      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   213      print(f\"DEBUG: recon shape: {recon.shape}\")
   214
   215      ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   216      psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   217      ssim = jnp.asarray(ssim_vmap(gt, recon))
   218      psnr = jnp.asarray(psnr_vmap(gt, recon))
   219      per_frame_ssim = ssim.mean(0)
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
   201      action_batch_E = None
   202      if not args.use_gt_actions:
   203          action_batch_E = genie.vq_encode(batch, training=False)
   204          batch[\"latent_actions\"] = action_batch_E
   205
   206      # --- Sample + evaluate video ---
   207      recon_video_BSHWC = _autoreg_sample(genie, rng, batch)
   208      recon_video_BSHWC = recon_video_BSHWC.astype(jnp.float32)
   209
   210      gt = gt_video.clip(0, 1)[:, args.start_frame :]
   211      recon = recon_video_BSHWC.clip(0, 1)[:, args.start_frame :]
   212      print(f\"DEBUG: recon shape: {recon.shape}\")
   213
   214      ssim_vmap = jax.vmap(pix.ssim, in_axes=(0, 0))
   215      psnr_vmap = jax.vmap(pix.psnr, in_axes=(0, 0))
   216      ssim = jnp.asarray(ssim_vmap(gt, recon))
   217      psnr = jnp.asarray(psnr_vmap(gt, recon))
   218      per_frame_ssim = ssim.mean(0)
   219      per_frame_psnr = psnr.mean(0)
   220      avg_ssim = ssim.mean()
   221      avg_psnr = psnr.mean()
</stdout>",
          "eval_tag": null
        }
      ],
      "expected_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```",
      "sample_evaluations": [
        {
          "generated_command": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command deletes line 211 instead of 212, and the sed range is 201-221 instead of 202-222. This would result in different lines being displayed."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command deletes line 211, while the Expected Command deletes line 212. This changes which line is removed, leading to different outputs when viewing the specified range of lines."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command does not include the 'sed -i' part to modify the file, so it doesn't remove line 212. The expected command modifies the file before displaying the lines, while the generated command only displays lines without modifying the file."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command deletes line 211 instead of 212, leading to different lines being displayed. The expected command removes line 212, while the generated command removes line 211, resulting in different output."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command prints lines 201-221, while the Expected Command deletes line 212 before printing lines 202-222. The deleted line affects the output, making them non-equivalent."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command deletes line 212 but the expected command also deletes line 212. However, the range of lines printed differs: generated prints lines 201-221, while expected prints 202-222. This results in different output as the generated command excludes line 202 and includes line 221 instead of 222."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command prints lines 201-221, while the expected command deletes line 212 and prints lines 202-222. The deleted line affects the output, making them different."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command prints lines 201-221, while the Expected Command deletes line 212 and prints lines 202-222. The deleted line affects the output, making them non-equivalent."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command deletes line 211, while the Expected Command deletes line 212. This results in different lines being removed, altering the output content."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '211,211d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,221p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command deletes line 211 instead of 212, leading to different lines being printed. The expected command prints lines 202-222, while the generated command prints lines 201-221, resulting in a different output."
          },
          "equivalent": 0,
          "exact_match": 0
        }
      ],
      "num_samples": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0
    },
    {
      "task_id": "eval_file_switch/0",
      "context": [
        {
          "role": "assistant",
          "content": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
total 144
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1321 Dec 23 18:47 train_tokenizer_16_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1307 Dec 23 18:47 train_tokenizer_1_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1320 Dec 23 18:47 train_tokenizer_2_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1322 Dec 23 18:47 train_tokenizer_32_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1309 Dec 23 18:47 train_tokenizer_4_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1317 Dec 23 18:47 train_tokenizer_8_nodes.sbatch
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2  #SBATCH --nodes=1
     3  #SBATCH --ntasks-per-node=4
     4  #SBATCH --time=05:00:00
     5  #SBATCH --partition=accelerated
     6  #SBATCH --cpus-per-task=5
     7  #SBATCH --gres=gpu:4
     8  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
     9  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    11
    12  # Log the sbatch script
    13  cat $0
    14
    15  module unload mpi/openmpi/5.0
    16  module unload devel/cuda/12.4
    17  source .venv/bin/activate
    18
    19  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    20
    21  job_name=$SLURM_JOB_NAME
    22  slurm_job_id=$SLURM_JOB_ID
    23
    24  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    25  mkdir -p $CHECKPOINT_DIR
    26
    27  env | grep SLURM
    28
    29  srun python jasmine/train_tokenizer.py \\
    30      --ckpt_dir $CHECKPOINT_DIR \\
    31      --batch_size=48 \\
    32      --min_lr=0 \\
    33      --max_lr=1e-4 \\
    34      --log_image_interval=1000 \\
    35      --log_checkpoint_interval=1000 \\
    36      --log \\
    37      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    38      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    39      --entity instant-uv \\
    40      --project jafar \\
    41      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2  #SBATCH --nodes=1
     3  #SBATCH --ntasks-per-node=4
     4  #SBATCH --time=05:00:00
     5  #SBATCH --time=48:00:00
     6  #SBATCH --cpus-per-task=5
     7  #SBATCH --gres=gpu:4
     8  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
     9  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    11
    12  # Log the sbatch script
    13  cat $0
    14
    15  module unload mpi/openmpi/5.0
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
    43
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        }
      ],
      "expected_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```",
      "sample_evaluations": [
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The commands reference different files: 'train_tokenizer_32_nodes.sbatch' vs 'train_tokenizer_16_nodes.sbatch'. The content of these files would differ based on the number of nodes specified, leading to different outcomes."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only outputs the first 15 lines of the 16-node script, while the expected command shows the full 32-node script. The content and line count differ."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The commands reference different files: 'train_tokenizer_32_nodes.sbatch' vs 'train_tokenizer_16_nodes.sbatch'. The file names indicate different numbers of nodes (32 vs 16), leading to different configurations and outcomes."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only outputs the first 15 lines of the 16-node script, while the expected command shows the full 32-node script. The content and line count differ."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only outputs the first 15 lines of the 16-node script, while the expected command shows the full 32-node script. The content differs in node count, batch size, and other parameters."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command modifies the sbatch script by changing the time limit and then displays only the first 15 lines, whereas the expected command simply outputs the entire file. This alters the content shown and affects the outcome."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
ls -1 slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_*/sbatch | xargs -I {} sh -c 'sed -i '5,5c#SBATCH --time=48:00:00'' {} && cat -n {} | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command uses ls -1 to list files and applies sed to all sbatch files, while the expected command directly displays a specific file. The generated command's approach alters multiple files and has different output behavior."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The commands reference different files: 'train_tokenizer_32_nodes.sbatch' vs 'train_tokenizer_16_nodes.sbatch'."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command outputs only the first 15 lines of the 16-node script, while the expected command displays the entire 32-node script. The content and line count differ."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only displays the first 15 lines of the 16-node script, while the expected command shows the entire 32-node script. The content and line count differ, leading to different outputs."
          },
          "equivalent": 0,
          "exact_match": 0
        }
      ],
      "num_samples": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0
    },
    {
      "task_id": "eval_insert/0",
      "context": [
        {
          "role": "assistant",
          "content": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n src/input_pipeline/validation_to_testcases.py
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  import tyro
     2  import os
     3  from dataclasses import dataclass
     4
     5
     6  @dataclass
     7  class Args:
     8      input_file: str = \"data/eval/val/validation.jsonl\"
     9      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
    10      task_name: str = \"validation_set\"
    11
    12
    13  def convert_to_incremental_jsonl(line, output_file, task_name):
    14      data = json.loads(line)
    15
    16      system_prompt = data.get(\"system_prompt\", \"\")
    17      conversations = data.get(\"conversations\", [])
    18
    19      output_lines = []
    20      context = []
    21
    22      for i, conv in enumerate(conversations):
    23          # Convert \"Assistant\"/\"User\" to \"assistant\"/\"user\"
    24          role = conv[\"from\"].lower()
    25          content = conv[\"value\"]
    26
    27          # Add current message to context
    28          context.append({\"role\": role, \"content\": content})
    29
    30          if i > 0 and role == \"assistant\":
    31              task_entry = {
    32                  \"system_prompt\": system_prompt,
    33                  \"task_id\": f\"{task_name}/{i-1}\",
    34                  \"context\": context[:-1].copy(),
    35                  \"expected_final_response\": content,
    36              }
    37              output_lines.append(task_entry)
    38
    39      with open(output_file, \"a\") as f:
    40          for line in output_lines:
    41              f.write(json.dumps(line) + \"\
\")
    42
    43      print(f\"Converted {len(output_lines)} entries to {output_file}\")
    44
    45
    46  if __name__ == \"__main__\":
    47      args = tyro.cli(Args)
    48
    49      if os.path.exists(args.output_file):
    50          os.remove(args.output_file)
    51
    52      # iterate over jsonl file and convert to incremental jsonl
    53      conversation_id = 0
    54      with open(args.input_file, \"r\") as f:
    55          for line in f:
    56              task_name = f\"conversation_{conversation_id}/{args.task_name}\"
    57              convert_to_incremental_jsonl(line, args.output_file, task_name)
    58              conversation_id += 1
</stdout>",
          "eval_tag": null
        }
      ],
      "expected_command": "```bash
sed -i '1i\\import json' src/input_pipeline/validation_to_testcases.py && cat -n src/input_pipeline/validation_to_testcases.py | sed -n '1,10p'
```",
      "sample_evaluations": [
        {
          "generated_command": "```bash
import json
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected Command inserts 'import json' into the script and displays lines 1-10. The Generated Command runs the script without fixing the missing import, leading to a runtime error. They do not produce the same outcome."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
import json
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected Command inserts 'import json' into the script and checks the first 10 lines. The Generated Command runs the script without fixing the missing import, leading to a runtime error."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
echo \"import json\
\" >> src/input_pipeline/validation_to_testcases.py
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected command inserts 'import json' at the beginning of the file and displays lines 1-10. The Generated command appends 'import json' to the end of the file, which does not fix the missing import error."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
echo \"import json\" >> src/input_pipeline/validation_to_testcases.py
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected command inserts 'import json' at the beginning of the file and displays lines 1-10. The Generated command appends 'import json' to the end of the file, which does not fix the missing import error."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected Command adds an 'import json' line to the Python script and displays the first 10 lines, while the Generated Command runs the script without fixing the missing import. They do not produce the same result."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
echo \"import json\" >> src/input_pipeline/validation_to_testcases.py
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected command inserts 'import json' at the beginning of the file and displays lines 1-10. The Generated command appends 'import json' to the end of the file, which does not fix the missing import error in the original code."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command is missing. Please provide the generated command for comparison."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
import json
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Expected Command inserts 'import json' into the script and checks the first 10 lines. The Generated Command runs the script without fixing the missing import, leading to a runtime error."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command is missing. Please provide the generated command for comparison."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command is missing. Please provide the command to compare."
          },
          "equivalent": 0,
          "exact_match": 0
        }
      ],
      "num_samples": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0
    },
    {
      "task_id": "eval_replace/0",
      "context": [
        {
          "role": "assistant",
          "content": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
total 144
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1321 Dec 23 18:47 train_tokenizer_16_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1307 Dec 23 18:47 train_tokenizer_1_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1320 Dec 23 18:47 train_tokenizer_2_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1322 Dec 23 18:47 train_tokenizer_32_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1309 Dec 23 18:47 train_tokenizer_4_nodes.sbatch
-rw-rw---- 1 alfred.nguyen alfred.nguyen 1317 Dec 23 18:47 train_tokenizer_8_nodes.sbatch
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2  #SBATCH --nodes=1
     3  #SBATCH --ntasks-per-node=4
     4  #SBATCH --time=05:00:00
     5  #SBATCH --partition=accelerated
     6  #SBATCH --cpus-per-task=5
     7  #SBATCH --gres=gpu:4
     8  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
     9  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    11
    12  # Log the sbatch script
    13  cat $0
    14
    15  module unload mpi/openmpi/5.0
    16  module unload devel/cuda/12.4
    17  source .venv/bin/activate
    18
    19  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    20
    21  job_name=$SLURM_JOB_NAME
    22  slurm_job_id=$SLURM_JOB_ID
    23
    24  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    25  mkdir -p $CHECKPOINT_DIR
    26
    27  env | grep SLURM
    28
    29  srun python jasmine/train_tokenizer.py \\
    30      --ckpt_dir $CHECKPOINT_DIR \\
    31      --batch_size=48 \\
    32      --min_lr=0 \\
    33      --max_lr=1e-4 \\
    34      --log_image_interval=1000 \\
    35      --log_checkpoint_interval=1000 \\
    36      --log \\
    37      --name=tokenizer-batch-size-scaling-1-node-sqrt-lr-$slurm_job_id \\
    38      --tags tokenizer batch-size-scaling 1-node sqrt-lr \\
    39      --entity instant-uv \\
    40      --project jafar \\
    41      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_1_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2  #SBATCH --nodes=1
     3  #SBATCH --ntasks-per-node=4
     4  #SBATCH --time=05:00:00
     5  #SBATCH --time=48:00:00
     6  #SBATCH --cpus-per-task=5
     7  #SBATCH --gres=gpu:4
     8  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
     9  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --job-name=train_tokenizer_batch_size_scaling_1_node
    11
    12  # Log the sbatch script
    13  cat $0
    14
    15  module unload mpi/openmpi/5.0
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=96 \\
    33      --min_lr=0 \\
    34      --max_lr=1.4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-2-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 2-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
    43
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_2_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=2
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_2_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=192 \\
    33      --min_lr=0 \\
    34      --max_lr=2e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-4-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 4-node sqrt-lr \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_4_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=4
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_4_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=384 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-8-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 8-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_8_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=8
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_8_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=768 \\
    33      --min_lr=0 \\
    34      --max_lr=4e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-16-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 16-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch | sed -n '1,15p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=16
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=48:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_16_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  #!/usr/bin/env bash
     2
     3  #SBATCH --nodes=32
     4  #SBATCH --ntasks-per-node=4
     5  #SBATCH --time=05:00:00
     6  #SBATCH --partition=accelerated
     7  #SBATCH --cpus-per-task=5
     8  #SBATCH --gres=gpu:4
     9  #SBATCH --output=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    10  #SBATCH --error=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/logs/logs_mihir/%x_%j.log
    11  #SBATCH --job-name=train_tokenizer_batch_size_scaling_32_node
    12
    13  # Log the sbatch script
    14  cat $0
    15
    16  module unload mpi/openmpi/5.0
    17  module unload devel/cuda/12.4
    18  source .venv/bin/activate
    19
    20  array_records_dir=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data_new/open_ai_minecraft_arrayrecords/10fps_160x90
    21
    22  job_name=$SLURM_JOB_NAME
    23  slurm_job_id=$SLURM_JOB_ID
    24
    25  CHECKPOINT_DIR=/hkfs/work/workspace/scratch/tum_ind3695-jafa_ws_shared/data/checkpoints/$job_name/$slurm_job_id
    26  mkdir -p $CHECKPOINT_DIR
    27
    28  env | grep SLURM
    29
    30  srun python jasmine/train_tokenizer.py \\
    31      --ckpt_dir $CHECKPOINT_DIR \\
    32      --batch_size=1536 \\
    33      --min_lr=0 \\
    34      --max_lr=3e-4 \\
    35      --log_image_interval=1000 \\
    36      --log_checkpoint_interval=1000 \\
    37      --log \\
    38      --name=tokenizer-batch-size-scaling-32-node-sqrt-lr-$slurm_job_id \\
    39      --tags tokenizer batch-size-scaling 32-node sqrt-lr-scaling \\
    40      --entity instant-uv \\
    41      --project jafar \\
    42      --data_dir $array_records_dir
</stdout>",
          "eval_tag": null
        }
      ],
      "expected_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
      "sample_evaluations": [
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time parameter and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time parameter and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time parameter and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time parameter and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command only displays the file content, while the Expected Command modifies the file and then displays a specific portion of it. The modification (changing the time) is not present in the Generated Command."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time flag and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time flag and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_64_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_64_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "Different filename: train_tokenizer_32_nodes.sbatch vs train_tokenizer_64_nodes.sbatch"
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The Generated Command does not modify the file as the Expected Command does. The Expected Command edits the file to change the time, while the Generated Command only displays the first 15 lines without making any changes."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
sed -i '5,5c#SBATCH --time=48:00:00' slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch && cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch | sed -n '1,15p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The generated command is identical to the expected command, both modifying the time parameter and displaying the first 15 lines of the same file."
          },
          "equivalent": 1,
          "exact_match": 1
        }
      ],
      "num_samples": 10,
      "num_judge_matches": 7,
      "judge_avg_at_n": 0.7,
      "judge_pass_at_n": 1,
      "num_exact_matches": 7
    },
    {
      "task_id": "eval_terminal_invocation/0",
      "context": [
        {
          "role": "assistant",
          "content": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
Traceback (most recent call last):
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 57, in <module>
    convert_to_incremental_jsonl(line, args.output_file, task_name)
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File \"/fast/home/mihir.mahajan/Projects/tab-model-eval/src/input_pipeline/validation_to_testcases.py\", line 14, in convert_to_incremental_jsonl
    data = json.loads(line)
           ^^^^
NameError: name 'json' is not defined. Did you forget to import 'json'?
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
cat -n src/input_pipeline/validation_to_testcases.py
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  import tyro
     2  import os
     3  from dataclasses import dataclass
     4
     5
     6  @dataclass
     7  class Args:
     8      input_file: str = \"data/eval/val/validation.jsonl\"
     9      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
    10      task_name: str = \"validation_set\"
    11
    12
    13  def convert_to_incremental_jsonl(line, output_file, task_name):
    14      data = json.loads(line)
    15
    16      system_prompt = data.get(\"system_prompt\", \"\")
    17      conversations = data.get(\"conversations\", [])
    18
    19      output_lines = []
    20      context = []
    21
    22      for i, conv in enumerate(conversations):
    23          # Convert \"Assistant\"/\"User\" to \"assistant\"/\"user\"
    24          role = conv[\"from\"].lower()
    25          content = conv[\"value\"]
    26
    27          # Add current message to context
    28          context.append({\"role\": role, \"content\": content})
    29
    30          if i > 0 and role == \"assistant\":
    31              task_entry = {
    32                  \"system_prompt\": system_prompt,
    33                  \"task_id\": f\"{task_name}/{i-1}\",
    34                  \"context\": context[:-1].copy(),
    35                  \"expected_final_response\": content,
    36              }
    37              output_lines.append(task_entry)
    38
    39      with open(output_file, \"a\") as f:
    40          for line in output_lines:
    41              f.write(json.dumps(line) + \"\
\")
    42
    43      print(f\"Converted {len(output_lines)} entries to {output_file}\")
    44
    45
    46  if __name__ == \"__main__\":
    47      args = tyro.cli(Args)
    48
    49      if os.path.exists(args.output_file):
    50          os.remove(args.output_file)
    51
    52      # iterate over jsonl file and convert to incremental jsonl
    53      conversation_id = 0
    54      with open(args.input_file, \"r\") as f:
    55          for line in f:
    56              task_name = f\"conversation_{conversation_id}/{args.task_name}\"
    57              convert_to_incremental_jsonl(line, args.output_file, task_name)
    58              conversation_id += 1
</stdout>",
          "eval_tag": null
        },
        {
          "role": "assistant",
          "content": "```bash
sed -i '1i\\import json' src/input_pipeline/validation_to_testcases.py && cat -n src/input_pipeline/validation_to_testcases.py | sed -n '1,10p'
```",
          "eval_tag": "NO_EVAL"
        },
        {
          "role": "user",
          "content": "<stdout>
     1  import json
     2  import tyro
     3  import os
     4  from dataclasses import dataclass
     5
     6
     7  @dataclass
     8  class Args:
     9      input_file: str = \"data/eval/val/validation.jsonl\"
    10      output_file: str = \"data/eval/val/validation_testcases.jsonl\"
</stdout>",
          "eval_tag": null
        }
      ],
      "expected_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
      "sample_evaluations": [
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file paths."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file parameters."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file parameters."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '1i\\import json' src/input_pipeline/validation_to_testcases.py
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only adds an 'import json' line to the script, while the expected command runs the script. They are not equivalent as one modifies the file and the other executes it."
          },
          "equivalent": 0,
          "exact_match": 0
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file paths."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file parameters."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, so they will produce the same outcome."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file parameters."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 1,
            "reason": "The commands are identical in structure and arguments, both executing the same Python script with the same input and output file paths."
          },
          "equivalent": 1,
          "exact_match": 1
        },
        {
          "generated_command": "```bash
sed -i '1i\\import json' src/input_pipeline/validation_to_testcases.py
```",
          "thinking_trace": null,
          "evaluation_results": {
            "equivalent": 0,
            "reason": "The generated command only adds an 'import json' line to the script, while the expected command runs the script. They are not equivalent as one modifies the file and the other executes it."
          },
          "equivalent": 0,
          "exact_match": 0
        }
      ],
      "num_samples": 10,
      "num_judge_matches": 8,
      "judge_avg_at_n": 0.8,
      "judge_pass_at_n": 1,
      "num_exact_matches": 8
    }
  ]
}