[tab] tab-model-eval/data/eval/output/3887/qwen1.7B_lora_bs_32_3887_0_evaluations.jsonl (jsonl)
{
  "metadata": {
    "config_generations": {
      "test_cases_file": "data/eval/handcrafted_test_cases/unit_test_cases.jsonl",
      "generations_file": "data/eval/output/3887/qwen1.7B_lora_bs_32_3887_0_generations.jsonl",
      "limit": -1,
      "system_prompt_file": "data/prompts/swe_mini_v1.md",
      "viewport_radius": 10,
      "model_name": "default",
      "model_path": "/fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-1.7B",
      "server_host": "0.0.0.0",
      "server_port": 30000,
      "context_length": 40960,
      "problem_length": 40960,
      "mem_fraction_static": 0.8,
      "api_key": "EMPTY",
      "tp_size": 4,
      "lora_paths": null,
      "temperature": 0.7,
      "top_p": 0.8,
      "presence_penalty": 0.0,
      "top_k": 20,
      "num_samples": 10,
      "max_new_tokens": 5000,
      "concurrency": 16,
      "max_connections": 256,
      "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_32_3887",
      "wandb_eval_type": "unit-test-v1",
      "wandb_tags": [
        "val_mini",
        "judge_eval"
      ],
      "wandb_id": "my_eval_run_3887",
      "wandb_group": "debug",
      "generations_file": "data/eval/output/3887/qwen1.7B_lora_bs_32_3887_0_generations.jsonl",
      "evaluations_file": "data/eval/output/3887/qwen1.7B_lora_bs_32_3887_0_evaluations.jsonl",
      "eval_step": 0,
      "generations_files": "",
      "evaluations_files": "",
      "eval_steps": "",
      "limit": -1,
      "system_prompt_file": "data/prompts/judge_system_prompt_v3.md",
      "judge_name": "default",
      "judge_prompt_file": "data/prompts/judge_prompt_v2.md",
      "judge_prompt_file_with_context": "data/prompts/judge_prompt_v3_with_context.md",
      "include_context": true,
      "use_local_logger": false,
      "local_log_dir": "data/eval/local_logs",
      "judge_model_path": "Qwen/Qwen3-30B-A3B",
      "server_host": "localhost",
      "server_port": 30000,
      "context_length": 40960,
      "problem_length": 40960,
      "api_key": "EMPTY",
      "mem_fraction_static": 0.95,
      "tp_size": 1,
      "temperature": 0.7,
      "top_p": 0.8,
      "presence_penalty": 1.5,
      "top_k": 20,
      "num_samples": 1,
      "min_p": 0.0,
      "enable_thinking": true,
      "concurrency": 16,
      "max_connections": 256,
      "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,
    "num_judge_samples_per_rollout": 1,
    "total_judge_avg_at_n": 0.2,
    "total_judge_pass_at_n": 1,
    "total_exact_match_avg_at_n": 0.4,
    "total_exact_match_pass_at_n": 2,
    "total_empty_command_rate": 0.0,
    "total_generated_command_empty": 0,
    "total_format_correct_rate": 0.6,
    "total_format_correct": 30,
    "total_format_incorrect": 20,
    "max_attempts": 6,
    "num_errors": 0
  },
  "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": [
        {
          "sample_idx": 0,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, starting at line 201 instead of 202. This would include an extra line (line 201) which is not present in the expected command's output.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 1,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The only difference is the starting line number in the sed command. The generated command starts at line 201 instead of 202, which includes an extra line (line 201) in the output. This makes them not equivalent.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 2,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, starting at line 201 instead of 202. This would include an extra line (line 201) which is not present in the expected command's output.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 3,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, selecting line 201 instead of 202. This would include an extra line in the output, potentially altering the displayed code segment.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 4,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, which may affect the output lines displayed.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 5,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The only difference is the starting line number in the sed command. The Generated Command starts at line 201 instead of 202, which includes an extra line (line 201) in the output. This makes them not equivalent.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 6,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command prints line 201 instead of 202, which is the line with 'action_batch_E = None'. The expected command would print line 202, which is the line after the 'action_batch_E = None' assignment. This difference in the printed lines makes the commands not equivalent.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 7,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, starting at line 201 instead of 202. This would include an extra line (201) which is not present in the expected command, making them not equivalent.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 8,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The only difference is the starting line number in the sed command (202 vs 201). The generated command includes line 201, which is part of the 'action_batch_E' setup, while the expected command starts at line 202. This may include or exclude the 'action_batch_E = None' line, potentially affecting the output.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        },
        {
          "sample_idx": 9,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '202,222p'
```

**Generated:**
```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
sed -i '212,212d' jasmine/baselines/diffusion/sample_diffusion.py && cat -n jasmine/baselines/diffusion/sample_diffusion.py | sed -n '201,222p'
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The generated command differs by one line in the sed range, starting at line 201 instead of 202. This would include an extra line (line 201) which is not present in the expected command's output.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "valid_edit_command"
        }
      ],
      "num_generation_samples": 10,
      "num_judge_samples_per_generation": 1,
      "num_total_evaluations": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0,
      "num_generated_command_empty": 0,
      "empty_command_rate": 0.0,
      "num_format_correct": 10,
      "format_correct_rate": 1.0,
      "had_error": false
    },
    {
      "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": [
        {
          "sample_idx": 0,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The Expected command outputs the contents of a specific sbatch file, while the Generated command lists the directory contents. They perform different operations.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 1,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The Expected command outputs the contents of a specific sbatch file, while the Generated command lists the directory contents. They perform different operations.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 2,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The commands differ in the filename they are reading. The expected command reads 'train_tokenizer_32_nodes.sbatch' while the generated command reads 'train_tokenizer_16_nodes.sbatch'. These are different files, so the output would not be the same.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 3,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The Expected command displays the contents of a specific sbatch file, while the Generated command lists the directory contents. They perform different operations.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 4,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The commands differ in the filename they are reading. The expected command reads 'train_tokenizer_32_nodes.sbatch' while the generated command reads 'train_tokenizer_16_nodes.sbatch'. These are different files, so the output would not be the same.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 5,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The commands differ in the filename they are reading. The expected command reads 'train_tokenizer_32_nodes.sbatch' while the generated command reads 'train_tokenizer_16_nodes.sbatch'. These are different files, so the output would not be the same.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 6,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The Expected command outputs the contents of a specific sbatch file, while the Generated command lists the directory contents. They perform different actions.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 7,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
ls -l slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The Expected command displays the contents of a specific sbatch file, while the Generated command lists the directory contents. They achieve different outcomes.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 8,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The commands differ in the filename they are reading. The expected command reads 'train_tokenizer_32_nodes.sbatch' while the generated command reads 'train_tokenizer_16_nodes.sbatch'. This results in different files being displayed, so they are not equivalent.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 9,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_32_nodes.sbatch
```

**Generated:**
```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "generated_command": "```bash
cat -n slurm/jobs/mihir/horeka/batchsize_scaling/tokenizer/sqrt_lr/train_tokenizer_16_nodes.sbatch
```",
          "thinking_trace": null,
          "evaluation_results": {
            "reason": "The commands differ in the filename they are reading. The expected command reads 'train_tokenizer_32_nodes.sbatch' while the generated command reads 'train_tokenizer_16_nodes.sbatch'. These are different files, so the output would not be the same.",
            "equivalent": 0
          },
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        }
      ],
      "num_generation_samples": 10,
      "num_judge_samples_per_generation": 1,
      "num_total_evaluations": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0,
      "num_generated_command_empty": 0,
      "empty_command_rate": 0.0,
      "num_format_correct": 10,
      "format_correct_rate": 1.0,
      "had_error": false
    },
    {
      "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": [
        {
          "sample_idx": 0,
          "choice_idx": null,
          "generated_command": "```bash
pip install json
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 1,
          "choice_idx": null,
          "generated_command": "```bash
pip install -e /fast/home/mihir.mahajan/Projects/tab-model-eval/
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 2,
          "choice_idx": null,
          "generated_command": "```bash
pip install -e .
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 3,
          "choice_idx": null,
          "generated_command": "```bash
pip install json
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 4,
          "choice_idx": null,
          "generated_command": "```bash
pip install json
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 5,
          "choice_idx": null,
          "generated_command": "```bash
pip install -e .
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 6,
          "choice_idx": null,
          "generated_command": "```bash
pip install json
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 7,
          "choice_idx": null,
          "generated_command": "```bash
pip install -e /fast/home/mihir.mahajan/Projects/tab-model-eval/
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 8,
          "choice_idx": null,
          "generated_command": "```bash
pip install -e .
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        },
        {
          "sample_idx": 9,
          "choice_idx": null,
          "generated_command": "```bash
pip install json
```",
          "equivalent": 0,
          "exact_match": 0,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "missing_sed_edit"
        }
      ],
      "num_generation_samples": 10,
      "num_judge_samples_per_generation": 1,
      "num_total_evaluations": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 0,
      "num_generated_command_empty": 0,
      "empty_command_rate": 0.0,
      "num_format_correct": 0,
      "format_correct_rate": 0.0,
      "had_error": false
    },
    {
      "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": [
        {
          "sample_idx": 0,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 1,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 2,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 3,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 4,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 5,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 6,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 7,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 8,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        },
        {
          "sample_idx": 9,
          "choice_idx": null,
          "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'
```",
          "equivalent": 0,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": false,
          "format_reason": "invalid_format"
        }
      ],
      "num_generation_samples": 10,
      "num_judge_samples_per_generation": 1,
      "num_total_evaluations": 10,
      "num_judge_matches": 0,
      "judge_avg_at_n": 0.0,
      "judge_pass_at_n": 0,
      "num_exact_matches": 10,
      "num_generated_command_empty": 0,
      "empty_command_rate": 0.0,
      "num_format_correct": 0,
      "format_correct_rate": 0.0,
      "had_error": false
    },
    {
      "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": [
        {
          "sample_idx": 0,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 1,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 2,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "reason": "The commands are identical in structure and parameters, both executing the same Python script with the same input and output file arguments.",
            "equivalent": 1
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 3,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 4,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 5,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "reason": "The commands are identical in structure and parameters, both executing the same Python script with the same input and output file arguments.",
            "equivalent": 1
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 6,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 7,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 8,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        },
        {
          "sample_idx": 9,
          "choice_idx": 0,
          "messages": [
            {
              "role": "system",
              "content": "You are an expert command-line evaluator. Determine whether two bash commands are semantically equivalent \u2014 meaning a user would consider both valid solutions to the same task. Be lenient: if the commands accomplish the same goal with minor differences in approach, they are equivalent. Only mark as not equivalent when the commands clearly do different things or one would fail. Respond only in valid JSON format."
            },
            {
              "role": "user",
              "content": "# Semantic Equivalence Evaluation

Determine if the **Generated Command** achieves the same outcome as the **Expected Command**.

## Definitions

**Semantically Equivalent** \u2014 Commands produce the same result when executed. Differences that DO NOT affect equivalence:
- Flag order (`ls -la` \u2261 `ls -al`)
- Short vs long flags (`-h` \u2261 `--help`)
- Equivalent syntax (`cat file | grep x` \u2261 `grep x file`)
- Whitespace or quoting style
- Line insertion position when order doesn't matter (e.g., import statements in most languages)
- Minor formatting differences in generated files that don't affect program behavior

**NOT Equivalent** \u2014 Commands produce different results:
- Different files, directories, or arguments
- Missing or extra flags that change behavior
- Different operations entirely
- Malformed or incomplete commands
- Order differences only when order semantically matters (e.g., command pipelines, ordered data insertions)

## Conversation History

[
  {
    \"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
  }
]

## Commands

**Expected:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

**Generated:**
```bash
python src/input_pipeline/validation_to_testcases.py --input_file data/temp/validation.jsonl --output_file data/temp/validation_testcases.jsonl
```

## Task

Compare the commands and respond with this exact JSON:

{
  \"reason\": \"<brief reasoning>\",
  \"equivalent\": <1 or 0>
}

Where:
- `1` = Same outcome (semantically equivalent)
- `0` = Different outcome (not equivalent)"
            }
          ],
          "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": {
            "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
          },
          "equivalent": 1,
          "exact_match": 1,
          "generated_command_empty": 0,
          "format_valid": true,
          "format_reason": "non_edit_task"
        }
      ],
      "num_generation_samples": 10,
      "num_judge_samples_per_generation": 1,
      "num_total_evaluations": 10,
      "num_judge_matches": 10,
      "judge_avg_at_n": 1.0,
      "judge_pass_at_n": 1,
      "num_exact_matches": 10,
      "num_generated_command_empty": 0,
      "empty_command_rate": 0.0,
      "num_format_correct": 10,
      "format_correct_rate": 1.0,
      "had_error": false
    }
  ]
}