clean up load, add inject stats and extend convert script for libero

check_buffers.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+"""Simple script to check buffer naming in the transformed model."""
+
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+
+# Load the model with strict=False to see what buffers we have
+print("Loading model...")
+policy = PI0Policy.from_pretrained("pepijn223/pi0_libero_lerobot", strict=False)
+
+# Check what buffer keys exist
+state_dict = policy.state_dict()
+buffer_keys = [k for k in state_dict.keys() if "buffer" in k]
+normalize_keys = [k for k in state_dict.keys() if "normalize" in k]
+
+print("\nAll buffer keys:")
+for key in buffer_keys:
+    print(f"  {key}")
+
+print("\nAll normalize keys:")
+for key in normalize_keys:
+    print(f"  {key}")
+
+print("\nAll keys (first 20):")
+for i, key in enumerate(state_dict.keys()):
+    if i < 20:
+        print(f"  {key}")

inject_stats.py

@@ -0,0 +1,347 @@
+#!/usr/bin/env python
+"""Script for Pi0 pretrained policy inference and Hub upload."""
+
+import argparse
+from datetime import datetime
+
+import numpy as np
+import torch
+
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+
+# Set seed
+torch.manual_seed(42)
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Pi0 policy inference and Hub upload")
+    parser.add_argument(
+        "--source-model-id",
+        type=str,
+        default="pepijn223/pi0_libero_lerobot",
+        help="Source model repository ID on Hugging Face Hub",
+    )
+    parser.add_argument(
+        "--dataset-id", type=str, default="pepijn223/libero", help="Dataset repository ID on Hugging Face Hub"
+    )
+    parser.add_argument(
+        "--output-model-id",
+        type=str,
+        required=True,
+        help="Output model repository ID to upload to (e.g., 'your-username/pi0-libero-fixed')",
+    )
+    parser.add_argument(
+        "--device", type=str, default="cpu", choices=["cpu", "cuda", "mps"], help="Device to run inference on"
+    )
+    parser.add_argument("--episode", type=int, default=0, help="Episode index to load from dataset")
+    parser.add_argument(
+        "--sample-idx", type=int, default=10, help="Sample index within episode to use for inference"
+    )
+    parser.add_argument("--private", action="store_true", help="Make the uploaded model private")
+    parser.add_argument(
+        "--commit-message", type=str, default=None, help="Custom commit message for the upload"
+    )
+    return parser.parse_args()
+
+
+def _inject_normalization_stats(policy: PI0Policy, dataset_meta: LeRobotDatasetMetadata, key_mapping: dict):
+    """Recreate normalization layers with proper stats from the dataset."""
+    from lerobot.policies.normalize import Normalize, Unnormalize
+
+    # Convert numpy stats to the format expected by normalization layers and remap keys
+    stats = {}
+    for dataset_key, stat_dict in dataset_meta.stats.items():
+        # Use mapped key if available, otherwise use original key
+        policy_key = key_mapping.get(dataset_key, dataset_key)
+
+        stats[policy_key] = {
+            stat_type: torch.from_numpy(stat_array) if isinstance(stat_array, np.ndarray) else stat_array
+            for stat_type, stat_array in stat_dict.items()
+        }
+
+    print(f"Available stats keys: {list(stats.keys())}")
+    print(
+        f"Policy expects keys: input={list(policy.config.input_features.keys())}, output={list(policy.config.output_features.keys())}"
+    )
+
+    # Recreate normalization layers with proper stats
+    normalize_inputs = Normalize(policy.config.input_features, policy.config.normalization_mapping, stats)
+
+    normalize_targets = Normalize(policy.config.output_features, policy.config.normalization_mapping, stats)
+
+    unnormalize_outputs = Unnormalize(
+        policy.config.output_features, policy.config.normalization_mapping, stats
+    )
+
+    # Replace the normalization layers on the policy
+    policy.normalize_inputs = normalize_inputs
+    policy.normalize_targets = normalize_targets
+    policy.unnormalize_outputs = unnormalize_outputs
+
+    print("Normalization layers recreated with dataset stats.")
+
+
+def configure_policy_features(policy: PI0Policy, dataset: LeRobotDataset):
+    """Configure policy input and output features based on dataset metadata."""
+    print(f"Dataset features: {list(dataset.meta.features.keys())}")
+
+    # Create a proper mapping from dataset keys to policy keys
+    dataset_to_policy_mapping = {}
+
+    # Handle images
+    if "image" in dataset.meta.features:
+        dataset_to_policy_mapping["image"] = "observation.images.image"
+    if "wrist_image" in dataset.meta.features:
+        dataset_to_policy_mapping["wrist_image"] = "observation.images.image2"
+
+    # Handle state
+    if "state" in dataset.meta.features:
+        dataset_to_policy_mapping["state"] = "observation.state"
+
+    # Handle actions
+    if "actions" in dataset.meta.features:
+        dataset_to_policy_mapping["actions"] = "action"
+
+    print(f"Key mapping: {dataset_to_policy_mapping}")
+
+    # Clear existing input features and reconfigure with proper mapping
+    policy.config.input_features = {}
+    policy.config.output_features = {}
+
+    # Map visual features
+    for dataset_key, policy_key in dataset_to_policy_mapping.items():
+        if dataset_key in ["image", "wrist_image"]:
+            feature_info = dataset.meta.features[dataset_key]
+            # Convert HWC to CHW format and resize
+            shape = (3, 224, 224)  # Pi0 expects CHW format
+            policy.config.input_features[policy_key] = PolicyFeature(type=FeatureType.VISUAL, shape=shape)
+
+    # Map state features
+    for dataset_key, policy_key in dataset_to_policy_mapping.items():
+        if dataset_key == "state":
+            feature_info = dataset.meta.features[dataset_key]
+            shape = tuple(feature_info["shape"])
+            policy.config.input_features[policy_key] = PolicyFeature(type=FeatureType.STATE, shape=shape)
+
+    # Map action features
+    for dataset_key, policy_key in dataset_to_policy_mapping.items():
+        if dataset_key == "actions":
+            feature_info = dataset.meta.features[dataset_key]
+            shape = tuple(feature_info["shape"])
+            policy.config.output_features[policy_key] = PolicyFeature(type=FeatureType.ACTION, shape=shape)
+
+    print(f"Policy input_features: {list(policy.config.input_features.keys())}")
+    print(f"Policy output_features: {list(policy.config.output_features.keys())}")
+    print(f"Policy image_features: {list(policy.config.image_features.keys())}")
+    print(f"Policy action_feature: {policy.config.action_feature}")
+
+    return dataset_to_policy_mapping
+
+
+def fix_buffer_naming(policy: PI0Policy):
+    """Fix buffer naming issues in the loaded policy state dict."""
+    print("Fixing normalization buffer naming issues...")
+
+    state_dict = policy.state_dict()
+    corrected_state_dict = {}
+    fixes_applied = 0
+
+    for key, value in state_dict.items():
+        new_key = key
+
+        # Fix buffer naming: buffer_observation_state_mean -> buffer_observation_state.mean
+        if "buffer_observation_state_mean" in key:
+            new_key = key.replace("buffer_observation_state_mean", "buffer_observation_state.mean")
+            fixes_applied += 1
+            print(f"  Fixed: {key} -> {new_key}")
+        elif "buffer_observation_state_std" in key:
+            new_key = key.replace("buffer_observation_state_std", "buffer_observation_state.std")
+            fixes_applied += 1
+            print(f"  Fixed: {key} -> {new_key}")
+        # Remove image buffers that aren't expected (they cause conflicts)
+        elif "buffer_observation_image_mean" in key or "buffer_observation_image_std" in key:
+            print(f"  Removed unexpected buffer: {key}")
+            continue  # Skip this buffer
+
+        corrected_state_dict[new_key] = value
+
+    # Add missing action buffers with dummy values (will be replaced by dataset stats)
+    missing_buffers = [
+        "normalize_targets.buffer_action.mean",
+        "normalize_targets.buffer_action.std",
+        "unnormalize_outputs.buffer_action.mean",
+        "unnormalize_outputs.buffer_action.std",
+    ]
+
+    for buffer_key in missing_buffers:
+        if buffer_key not in corrected_state_dict:
+            # Use dummy values - these will be overwritten by proper dataset stats later
+            if "mean" in buffer_key:
+                corrected_state_dict[buffer_key] = torch.zeros(8)  # Assume 8-dim action
+            else:  # std
+                corrected_state_dict[buffer_key] = torch.ones(8)  # Assume 8-dim action
+            fixes_applied += 1
+            print(f"  Added missing buffer: {buffer_key}")
+
+    print(f"Applied {fixes_applied} buffer fixes")
+
+    # Load the corrected state dict back into the policy
+    policy.load_state_dict(corrected_state_dict)
+    return policy
+
+
+def main():
+    """Main function to run the Pi0 inference and upload."""
+    args = parse_args()
+
+    # Load pretrained Pi0 model directly from Hugging Face Hub
+    print(f"Loading pretrained Pi0 model from {args.source_model_id}...")
+
+    # Load with strict=False to allow missing/unexpected keys, then fix them manually
+    policy = PI0Policy.from_pretrained(args.source_model_id, strict=False)
+    policy = fix_buffer_naming(policy)
+    policy.eval()
+    policy.to(args.device)
+
+    # Load dataset and get a sample
+    print(f"Loading dataset: {args.dataset_id}")
+    dataset = LeRobotDataset(args.dataset_id, episodes=[args.episode])
+    meta: LeRobotDatasetMetadata = dataset.meta
+    sample = dataset[args.sample_idx]
+
+    # Configure policy features
+    key_mapping = configure_policy_features(policy, dataset)
+
+    # Inject normalization stats with proper key mapping
+    _inject_normalization_stats(policy, meta, key_mapping)
+
+    # Prepare batch for PI0 (handle temporal dimensions)
+    batch = {}
+
+    # Map dataset sample keys to policy keys
+    reverse_mapping = {v: k for k, v in key_mapping.items()}
+
+    for policy_key in policy.config.input_features:
+        # Find the corresponding dataset key
+        dataset_key = reverse_mapping.get(policy_key, policy_key)
+
+        if dataset_key in sample:
+            data = sample[dataset_key]
+
+            # Handle image data: convert from HWC to CHW and normalize
+            if policy_key.startswith("observation.images."):
+                if data.dim() == 3 and data.shape[-1] == 3:  # HWC format
+                    data = data.permute(2, 0, 1)  # Convert to CHW
+                # Normalize to [0, 1] range if needed
+                if data.dtype == torch.uint8:
+                    data = data.float() / 255.0
+                # Resize to expected size if needed
+                if data.shape[-2:] != (224, 224):
+                    import torch.nn.functional as F  # noqa: N812
+
+                    data = F.interpolate(
+                        data.unsqueeze(0), size=(224, 224), mode="bilinear", align_corners=False
+                    )[0]
+
+            # Remove temporal dimension if present
+            if data.dim() > len(policy.config.input_features[policy_key].shape):
+                data = data[0]
+
+            batch[policy_key] = data.unsqueeze(0)  # Add batch dimension
+
+    # Debug: print what's in the sample
+    print(f"Sample keys: {list(sample.keys())}")
+    print(f"Batch keys prepared: {list(batch.keys())}")
+
+    # Pi0 requires task description - add a default if not available
+    if "task" in sample:
+        batch["task"] = [sample["task"]]  # Keep as list of strings
+    else:
+        print("No task in sample, using default task description")
+        batch["task"] = ["Complete the manipulation task"]
+
+    print(f"Task: {batch['task'][0]}")
+    print(f"Final batch keys: {list(batch.keys())}")
+
+    # Run inference
+    with torch.no_grad():
+        action = policy.select_action(batch)
+        print(f"Predicted action shape: {action.shape}")
+        print(f"Predicted action: {action.tolist()}")
+
+    print("✅ Pi0 pretrained inference completed successfully!")
+
+    # Upload to Hugging Face Hub
+    print(f"\n📤 Uploading model to Hugging Face Hub: {args.output_model_id}")
+
+    # Create commit message
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    commit_message = (
+        args.commit_message
+        or f"Pi0 model with injected normalization stats from {args.dataset_id} - {timestamp}"
+    )
+
+    # Update model configuration with dataset info
+    policy.config.push_to_hub = True
+    policy.config.repo_id = args.output_model_id
+    policy.config.private = args.private
+
+    # Add metadata about the adaptation
+    adaptation_info = {
+        "source_model": args.source_model_id,
+        "dataset_used": args.dataset_id,
+        "adaptation_date": timestamp,
+        "stats_injected": True,
+        "key_mapping": key_mapping,
+        "inference_test_passed": True,
+        "sample_action_shape": list(action.shape),
+    }
+
+    try:
+        # Push to hub
+        policy.push_to_hub(
+            repo_id=args.output_model_id,
+            private=args.private,
+            commit_message=commit_message,
+            create_pr=False,
+        )
+
+        # Also save the adaptation info as a separate file
+        import json
+        import os
+        import tempfile
+
+        from huggingface_hub import HfApi
+
+        api = HfApi()
+
+        # Create a temporary file with adaptation info
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
+            json.dump(adaptation_info, f, indent=2)
+            temp_path = f.name
+
+        try:
+            api.upload_file(
+                path_or_fileobj=temp_path,
+                path_in_repo="adaptation_info.json",
+                repo_id=args.output_model_id,
+                commit_message=f"Add adaptation metadata - {timestamp}",
+            )
+        finally:
+            os.unlink(temp_path)
+
+        print(f"✅ Model successfully uploaded to: https://huggingface.co/{args.output_model_id}")
+        print("📋 Adaptation info:")
+        for key, value in adaptation_info.items():
+            print(f"   {key}: {value}")
+
+    except Exception as e:
+        print(f"❌ Error uploading to Hub: {e}")
+        raise
+
+
+if __name__ == "__main__":
+    main()

load_pi0.py

@@ -0,0 +1,704 @@
+import json
+import os
+import random
+from datetime import datetime
+
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download  # noqa: E402
+from safetensors.torch import load_file  # noqa: E402
+from transformers.model_debugging_utils import model_addition_debugger_context
+
+from lerobot.configs.policies import FeatureType, PolicyFeature
+from lerobot.constants import ACTION, OBS_IMAGE, OBS_STATE
+from lerobot.policies.pi0.configuration_pi0 import PI0Config
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+
+RANDOM_SEED = 42  # Set to fixed value for reproducible results
+
+
+def set_all_seeds(seed=42):
+    """Set all random seeds for reproducible results."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+    torch.use_deterministic_algorithms(True)
+    print(f"All random seeds set to {seed} for reproducible results (deterministic mode enabled)")
+
+
+# Set seeds at the start
+set_all_seeds(RANDOM_SEED)
+
+config_model_path = "lerobot/pi0"  # Use config from official model
+official_model_path = "lerobot/pi0"  # Official model
+custom_model_path = "pepijn223/pi0_base_fp32"  # Custom model to compare # pepijn223/pi0_base_fp32
+device = "mps"
+
+USE_FULL_TENSORS = True
+SAVE_TENSORS_TO_DISK = False
+
+# Model transformation and upload settings
+SAVE_TRANSFORMED_MODEL = True  # Set to True to save the transformed model
+UPLOAD_TO_HUB = True  # Set to True to upload to HuggingFace Hub
+TRANSFORMED_MODEL_NAME = "pepijn223/pi0_base_fp32_lerobot_format"  # Target repo name
+COMMIT_MESSAGE = "Add transformed PI0 model with correct key format for lerobot"
+
+timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+debug_path = os.path.join("debug_outputs", f"pi0_debug_direct_{timestamp}")
+os.makedirs(debug_path, exist_ok=True)
+print(f"Model debugging enabled - outputs will be saved to: {debug_path}")
+
+# Download and load the config manually to avoid draccus parsing issues
+config_file = hf_hub_download(repo_id=config_model_path, filename="config.json")
+with open(config_file) as f:
+    config_dict = json.load(f)
+
+# Remove the 'type' field that causes draccus issues
+if "type" in config_dict:
+    config_dict.pop("type")
+    print("Removed 'type' field from config")
+
+# Create shared PI0Config
+print("Creating shared PI0Config...")
+shared_config = PI0Config(**config_dict)
+
+
+def load_policy_with_weights(
+    model_path: str, config: PI0Config, model_name: str, apply_transformations: bool = False
+):
+    """Load a policy with specified weights but shared config."""
+    print(f"\n=== Loading {model_name} from {model_path} ===")
+
+    # Set deterministic seed before creating the policy to ensure identical initialization
+    torch.manual_seed(RANDOM_SEED)
+    np.random.seed(RANDOM_SEED)
+    random.seed(RANDOM_SEED)
+
+    policy = PI0Policy(config)
+
+    # Download and load weights
+    model_file = hf_hub_download(repo_id=model_path, filename="model.safetensors")
+    print(f"Downloaded {model_name} weights to: {model_file}")
+
+    # Load state dict and apply transformations
+    print(f"Investigating safetensors file: {model_file}")
+
+    # First, check what's in the metadata
+    try:
+        from safetensors import safe_open
+
+        with safe_open(model_file, framework="pt", device="cpu") as f:
+            metadata = f.metadata()
+            all_keys_in_file = f.keys()
+            print(f"   Total keys in safetensors file: {len(list(all_keys_in_file))}")
+
+            # Check for embed_tokens in the file keys
+            embed_keys_in_file = [k for k in f.keys() if "embed_tokens" in k]
+            print(f"   embed_tokens keys in safetensors: {embed_keys_in_file}")
+
+            if metadata:
+                print(f"   Metadata exists: {list(metadata.keys()) if metadata else 'None'}")
+    except Exception as e:
+        print(f"   Could not inspect safetensors file directly: {e}")
+
+    # Now load normally and see what we get
+    state_dict = load_file(model_file)
+    print(f"   Keys loaded by load_file(): {len(state_dict)} keys")
+
+    # Check for embed_tokens in loaded state_dict
+    loaded_embed_keys = [k for k in state_dict.keys() if "embed_tokens" in k]
+    print(f"   embed_tokens keys in loaded state_dict: {loaded_embed_keys}")
+
+    # Check if we need to add "model." prefix (for custom models that don't have it)
+    sample_key = next(iter(state_dict.keys()))
+    if not sample_key.startswith("model."):
+        print(f"Adding 'model.' prefix to all keys (detected format: {sample_key})")
+        state_dict = {f"model.{k}": v for k, v in state_dict.items()}
+
+    # IMPORTANT: Call PI0Policy._transform_state_dict_keys AFTER adding model. prefix
+    # This ensures tied weights logic can find the correct key pattern
+    transformed_state_dict = PI0Policy._transform_state_dict_keys(state_dict)
+
+    # Apply specific PaliGemma key transformations only for custom models
+    if apply_transformations:
+        print("Applying custom model key transformations...")
+
+        # First, let's debug what keys we actually have
+        all_keys = list(transformed_state_dict.keys())
+        sample_keys = all_keys[:10]
+        print(f"Sample keys to transform: {sample_keys}")
+
+        # Look for specific keys we need to transform and missing keys
+        embed_tokens_keys = [k for k in all_keys if "embed_tokens" in k]
+        embedding_keys = [k for k in all_keys if "embed" in k]
+        lm_head_keys = [k for k in all_keys if "lm_head" in k]
+        paligemma_keys = [
+            k for k in all_keys if "paligemma_with_expert.paligemma" in k and "gemma_expert" not in k
+        ]
+        language_model_keys = [k for k in all_keys if "language_model" in k]
+
+        print(f"Found embed_tokens keys: {embed_tokens_keys}")
+        print(f"Found any embedding keys: {embedding_keys}")
+        print(f"Found lm_head keys: {lm_head_keys}")
+        print(
+            f"Found paligemma keys (non-expert): {paligemma_keys[:5]}{'...' if len(paligemma_keys) > 5 else ''}"
+        )
+        print(
+            f"Found language_model keys: {language_model_keys[:5]}{'...' if len(language_model_keys) > 5 else ''}"
+        )
+        print(f"Total keys in model: {len(all_keys)}")
+
+        # Check if the embed_tokens is in gemma_expert instead
+        gemma_expert_embed = [k for k in all_keys if "gemma_expert" in k and "embed_tokens" in k]
+        print(f"Found gemma_expert embed_tokens keys: {gemma_expert_embed}")
+
+        # Check what we're missing and what we actually have
+        expected_embed_key = "model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+        if expected_embed_key not in all_keys:
+            print(f" Missing expected embed_tokens key: {expected_embed_key}")
+
+            # Let's see what keys we actually have for debugging
+            print("Debugging: Looking for any embedding-related keys...")
+            all_embed_related = [k for k in all_keys if "embed" in k.lower()]
+            print(f"Keys containing 'embed': {all_embed_related}")
+
+            # Look for any keys that might contain embeddings
+            potential_embed_keys = [
+                k for k in all_keys if any(word in k for word in ["embed", "embedding", "token"])
+            ]
+            print(f"   Potential embedding keys: {potential_embed_keys}")
+
+            # Try to find a suitable replacement
+            if gemma_expert_embed:
+                print(f"   Will try to copy from: {gemma_expert_embed[0]}")
+            else:
+                print("   No gemma_expert embed_tokens found either!")
+
+                # Check if there's an embed_tokens in the gemma_expert that we missed
+                gemma_keys = [k for k in all_keys if "gemma_expert" in k]
+                print(f"   First 10 gemma_expert keys: {gemma_keys[:10]}")
+
+                # Check if there are any token-related keys in gemma_expert
+                token_keys = [k for k in all_keys if "gemma_expert" in k and "token" in k.lower()]
+                print(f"   Gemma expert token-related keys: {token_keys}")
+
+                # Check for any keys that look like they might be embeddings
+                possible_embeds = [
+                    k
+                    for k in all_keys
+                    if any(
+                        pattern in k.lower() for pattern in ["embed_token", "embedding", "wte", "word_embed"]
+                    )
+                ]
+                print(f"   Possible embedding alternatives: {possible_embeds}")
+
+        final_state_dict = {}
+        transformation_count = 0
+
+        for key, value in transformed_state_dict.items():
+            new_key = key
+            original_key = key
+
+            # Transform vision tower keys: ADD .model between paligemma and vision_tower
+            if "paligemma_with_expert.paligemma.vision_tower.vision_model" in new_key:
+                new_key = new_key.replace(
+                    "paligemma_with_expert.paligemma.vision_tower.vision_model",
+                    "paligemma_with_expert.paligemma.model.vision_tower.vision_model",
+                )
+                print(f"Transformed vision key: {original_key} -> {new_key}")
+                transformation_count += 1
+
+            # Transform multi_modal_projector keys: ADD .model between paligemma and multi_modal_projector
+            elif "paligemma_with_expert.paligemma.multi_modal_projector" in new_key:
+                new_key = new_key.replace(
+                    "paligemma_with_expert.paligemma.multi_modal_projector",
+                    "paligemma_with_expert.paligemma.model.multi_modal_projector",
+                )
+                print(f"Transformed multi_modal_projector key: {original_key} -> {new_key}")
+                transformation_count += 1
+
+            # NO transformation needed for language_model keys - they're already correct!
+            # The custom model already has: paligemma.model.language_model.* which is what we need
+
+            # NO transformation needed for lm_head - it should stay as paligemma.lm_head
+
+            final_state_dict[new_key] = value
+
+        print(f"Applied {transformation_count} key transformations")
+        transformed_state_dict = final_state_dict
+    else:
+        print("No transformations applied (official model format)")
+
+    # Debug: show what keys the policy expects vs what we have
+    policy_keys = set(policy.state_dict().keys())
+    provided_keys = set(transformed_state_dict.keys())
+
+    missing_in_provided = policy_keys - provided_keys
+    extra_in_provided = provided_keys - policy_keys
+
+    print(f"Policy expects {len(policy_keys)} keys, we provide {len(provided_keys)} keys")
+    if missing_in_provided:
+        print(
+            f"   Missing from provided: {list(missing_in_provided)[:5]}{'...' if len(missing_in_provided) > 5 else ''}"
+        )
+    if extra_in_provided:
+        print(
+            f"   Extra in provided: {list(extra_in_provided)[:5]}{'...' if len(extra_in_provided) > 5 else ''}"
+        )
+
+    # Load the weights into the policy
+    msg = policy.load_state_dict(transformed_state_dict, strict=True)
+    print(
+        f"{model_name} - Missing keys: {len(msg.missing_keys)}, Unexpected keys: {len(msg.unexpected_keys)}"
+    )
+
+    if msg.missing_keys:
+        print(
+            f"   Actually missing keys: {list(msg.missing_keys)[:3]}{'...' if len(msg.missing_keys) > 3 else ''}"
+        )
+    if msg.unexpected_keys:
+        print(
+            f"   Actually unexpected keys: {list(msg.unexpected_keys)[:3]}{'...' if len(msg.unexpected_keys) > 3 else ''}"
+        )
+
+    # Set deterministic mode and move to device
+    policy = policy.to(device)
+    policy.eval()
+
+    # Reset the policy to ensure identical internal state
+    policy.reset()
+
+    return policy
+
+
+# Load both models with shared config
+print("Loading both models with shared config...")
+official_policy = load_policy_with_weights(
+    official_model_path, shared_config, "Official Model", apply_transformations=False
+)
+custom_policy = load_policy_with_weights(
+    custom_model_path, shared_config, "Custom Model", apply_transformations=True
+)
+
+print("\nBoth models loaded successfully!")
+print(f"Shared config: {shared_config}")
+print(f"Device: {device}")
+
+
+# Configure input features for both policies since they're not set by default in pretrained models
+def configure_policy_features(policy: PI0Policy):
+    """Configure input and output features for a policy."""
+    policy.config.input_features[OBS_IMAGE] = PolicyFeature(
+        type=FeatureType.VISUAL,
+        shape=(3, 224, 224),  # Channel-first RGB image
+    )
+
+    policy.config.input_features[OBS_STATE] = PolicyFeature(
+        type=FeatureType.STATE,
+        shape=(8,),  # 8-dimensional state vector
+    )
+
+    policy.config.output_features[ACTION] = PolicyFeature(
+        type=FeatureType.ACTION,
+        shape=(8,),  # 8-dimensional action vector
+    )
+
+    # Add dummy normalization buffers to the policy (like openpi does with norm_stats)
+    if hasattr(policy, "normalize_inputs"):
+        # For observation.state (8-dim state vector)
+        policy.normalize_inputs.register_buffer(
+            f"buffer_{OBS_STATE.replace('.', '_')}_mean", torch.zeros(8, device=device)
+        )
+        policy.normalize_inputs.register_buffer(
+            f"buffer_{OBS_STATE.replace('.', '_')}_std", torch.ones(8, device=device)
+        )
+
+        # For observation.image (3x224x224 image)
+        policy.normalize_inputs.register_buffer(
+            f"buffer_{OBS_IMAGE.replace('.', '_')}_mean", torch.zeros(3, 224, 224, device=device)
+        )
+        policy.normalize_inputs.register_buffer(
+            f"buffer_{OBS_IMAGE.replace('.', '_')}_std", torch.ones(3, 224, 224, device=device)
+        )
+
+
+print("Configuring features for both policies...")
+configure_policy_features(official_policy)
+configure_policy_features(custom_policy)
+
+# Verify that the models have identical parameters
+print("\n=== Model Parameter Comparison ===")
+official_params = dict(official_policy.named_parameters())
+custom_params = dict(custom_policy.named_parameters())
+
+param_differences = []
+for name in official_params.keys():
+    if name not in custom_params:
+        param_differences.append(f"Missing parameter in custom model: {name}")
+    else:
+        diff = torch.abs(official_params[name] - custom_params[name]).max().item()
+        if diff > 1e-8:
+            param_differences.append(f"Parameter {name}: max difference = {diff:.2e}")
+
+for name in custom_params.keys():
+    if name not in official_params:
+        param_differences.append(f"Extra parameter in custom model: {name}")
+
+if param_differences:
+    print("Parameter differences found:")
+    for diff in param_differences[:10]:  # Show first 10 differences
+        print(f"   {diff}")
+    if len(param_differences) > 10:
+        print(f"   ... and {len(param_differences) - 10} more differences")
+else:
+    print("All model parameters are identical!")
+
+
+# Get the raw models for direct comparison
+official_raw_model = official_policy.model
+custom_raw_model = custom_policy.model
+print("\n=== Model Details ===")
+print(f"Official raw model type: {type(official_raw_model)}")
+print(f"Custom raw model type: {type(custom_raw_model)}")
+print(f"Official model device: {next(official_raw_model.parameters()).device}")
+print(f"Custom model device: {next(custom_raw_model.parameters()).device}")
+
+# Create lerobot-format input data (similar to DROID format from openpi example)
+example = {
+    "joint_position": np.zeros(7, dtype=np.float32),
+    "gripper_position": np.array([0.0], dtype=np.float32),
+    "image": np.random.randint(0, 255, size=(224, 224, 3), dtype=np.uint8),
+    "task": "pick up the object",
+}
+
+print(f"\nProvided input keys: {list(example.keys())}")
+
+print("\nPreparing inputs for direct model call...")
+
+# Apply input transformation (similar to openpi's policy._input_transform)
+transformed_example = {}
+# Combine joint and gripper positions into state
+transformed_example[OBS_STATE] = np.concatenate([example["joint_position"], example["gripper_position"]])
+transformed_example[OBS_IMAGE] = example["image"]
+transformed_example["task"] = example["task"]
+
+# Convert to PyTorch tensors and add batch dimension (as openpi example does)
+# Device is already defined above, use the official model device for consistency
+pytorch_inputs = {}
+for key, value in transformed_example.items():
+    if isinstance(value, np.ndarray):
+        tensor_value = torch.from_numpy(value).to(device)
+        # Add batch dimension
+        if tensor_value.dim() > 0:
+            tensor_value = tensor_value.unsqueeze(0)
+        pytorch_inputs[key] = tensor_value
+    elif isinstance(value, str):
+        pytorch_inputs[key] = [value]  # Convert to list format expected by policy
+    else:
+        pytorch_inputs[key] = value
+
+# Convert image from HWC to CHW format for lerobot
+if OBS_IMAGE in pytorch_inputs:
+    img = pytorch_inputs[OBS_IMAGE]
+    if img.dim() == 4 and img.shape[-1] == 3:  # BHWC -> BCHW
+        img = img.permute(0, 3, 1, 2)
+    # Convert to float and normalize to [0, 1] range
+    img = img.float() / 255.0
+    pytorch_inputs[OBS_IMAGE] = img
+
+print(f"Transformed input keys: {list(pytorch_inputs.keys())}")
+for key, value in pytorch_inputs.items():
+    if isinstance(value, torch.Tensor):
+        print(f"  {key}: {value.shape} {value.dtype}")
+    else:
+        print(f"  {key}: {type(value)} - {value}")
+
+# Reset both policies (clears the action queue)
+official_policy.reset()
+custom_policy.reset()
+
+# Prepare inputs using the official policy (both models should have same preprocessing)
+print("Preparing inputs for both models...")
+images, img_masks = official_policy.prepare_images(pytorch_inputs)
+lang_tokens, lang_masks = official_policy.prepare_language(pytorch_inputs)
+state = official_policy.prepare_state(pytorch_inputs)
+
+print("Prepared inputs:")
+print(f"  Images: {len(images)} images")
+print(f"  Language tokens shape: {lang_tokens.shape}")
+print(f"  State shape: {state.shape}")
+for i, img in enumerate(images):
+    print(f"  Image {i} shape: {img.shape}")
+for i, mask in enumerate(img_masks):
+    print(f"  Image mask {i} shape: {mask.shape}")
+
+# Compare both models with identical inputs
+print("\n🚀 Running MODEL COMPARISON...")
+
+# Force torch.no_grad for consistent comparison
+with torch.no_grad():
+    # Ensure reproducible noise generation for both models
+    torch.manual_seed(RANDOM_SEED)
+
+    # Generate synthetic noise and time for the forward call
+    batch_size = 1
+    actions_shape = (
+        batch_size,
+        official_raw_model.config.n_action_steps,
+        official_raw_model.config.max_action_dim,
+    )
+
+    # Generate noise and time using direct PyTorch operations instead of model methods
+    # This avoids any potential model-specific randomness
+    torch.manual_seed(RANDOM_SEED)
+    noise = torch.normal(
+        mean=0.0,
+        std=1.0,
+        size=actions_shape,
+        dtype=torch.float32,
+        device=device,
+    )
+
+    # Generate time using the same distribution as PI0FlowMatching.sample_time
+    torch.manual_seed(RANDOM_SEED)  # Reset for consistent time
+    beta_dist = torch.distributions.Beta(concentration1=1.5, concentration0=1.0)
+    time_beta = beta_dist.sample((batch_size,)).to(device=device, dtype=torch.float32)
+    time = time_beta * 0.999 + 0.001
+
+    print("\n=== Generated Inputs ===")
+    print(f"   Action shape: {actions_shape}")
+    print(f"   Noise shape: {noise.shape}")
+    print(f"   Time value: {time.item():.6f}")
+    print(f"   Noise sample (first 5 values): {noise.flatten()[:5].tolist()}")
+
+    # Create dummy actions for forward pass (required for training forward)
+    dummy_actions = torch.zeros(actions_shape, dtype=torch.float32, device=device)
+
+    print("\n=== Running Forward Passes ===")
+
+    print("Running with model_addition_debugger_context for detailed analysis...")
+    # Create separate debug paths for each model
+    official_debug_path = os.path.join(debug_path, "official_model")
+    custom_debug_path = os.path.join(debug_path, "custom_model")
+    os.makedirs(official_debug_path, exist_ok=True)
+    os.makedirs(custom_debug_path, exist_ok=True)
+    # Set deterministic mode for forward pass
+    torch.manual_seed(RANDOM_SEED)
+    # Run official model with debugger
+    print("Running Official Model forward pass with debugger...")
+    with model_addition_debugger_context(
+        official_raw_model,
+        debug_path=official_debug_path,
+        do_prune_layers=False,  # Output ALL layers
+        use_repr=not SAVE_TENSORS_TO_DISK,
+    ):
+        official_loss = official_raw_model.forward(
+            images=images,
+            img_masks=img_masks,
+            lang_tokens=lang_tokens,
+            lang_masks=lang_masks,
+            state=state,
+            actions=dummy_actions,
+            noise=noise,
+            time=time,
+        )
+    # Reset seed before second forward pass to ensure any internal randomness is identical
+    torch.manual_seed(RANDOM_SEED)
+    # Run custom model with debugger
+    print("Running Custom Model forward pass with debugger...")
+    with model_addition_debugger_context(
+        custom_raw_model,
+        debug_path=custom_debug_path,
+        do_prune_layers=False,  # Output ALL layers
+        use_repr=not SAVE_TENSORS_TO_DISK,
+    ):
+        custom_loss = custom_raw_model.forward(
+            images=images,
+            img_masks=img_masks,
+            lang_tokens=lang_tokens,
+            lang_masks=lang_masks,
+            state=state,
+            actions=dummy_actions,
+            noise=noise,
+            time=time,
+        )
+
+    print(f"Official model debug outputs saved to: {official_debug_path}")
+    print(f"Custom model debug outputs saved to: {custom_debug_path}")
+
+    print("\n=== Output Comparison ===")
+    print(f"Official model loss shape: {official_loss.shape}")
+    print(f"Custom model loss shape: {custom_loss.shape}")
+
+    # Compare outputs
+    loss_diff = torch.abs(official_loss - custom_loss)
+
+    print("\n=== Detailed Comparison ===")
+    print("Loss difference stats:")
+    print(f"  Mean absolute difference: {loss_diff.mean().item():.8f}")
+    print(f"  Max absolute difference: {loss_diff.max().item():.8f}")
+    print(f"  Min absolute difference: {loss_diff.min().item():.8f}")
+    print(f"  Standard deviation of difference: {loss_diff.std().item():.8f}")
+
+    # Show some actual values for comparison
+    print("\nSample output values:")
+    print(f"  Official model (first 5): {official_loss.flatten()[:5].tolist()}")
+    print(f"  Custom model (first 5): {custom_loss.flatten()[:5].tolist()}")
+    print(f"  Difference (first 5): {loss_diff.flatten()[:5].tolist()}")
+
+    # Determine if models are equivalent
+    are_equivalent = loss_diff.max().item() < 1e-6
+    print(f"\nModels are {'EQUIVALENT' if are_equivalent else 'DIFFERENT'}")
+    print(f"   (Max difference: {loss_diff.max().item():.8f}, Threshold: 1e-6)")
+
+    print(f"\nDetailed debugging outputs saved to: {debug_path}")
+    # Save comparison results
+    comparison_results = {
+        "official_loss_stats": {
+            "shape": list(official_loss.shape),
+            "mean": official_loss.mean().item(),
+            "std": official_loss.std().item(),
+            "min": official_loss.min().item(),
+            "max": official_loss.max().item(),
+        },
+        "custom_loss_stats": {
+            "shape": list(custom_loss.shape),
+            "mean": custom_loss.mean().item(),
+            "std": custom_loss.std().item(),
+            "min": custom_loss.min().item(),
+            "max": custom_loss.max().item(),
+        },
+        "difference_stats": {
+            "mean_abs_diff": loss_diff.mean().item(),
+            "max_abs_diff": loss_diff.max().item(),
+            "min_abs_diff": loss_diff.min().item(),
+            "std_diff": loss_diff.std().item(),
+            "are_equivalent": are_equivalent,
+        },
+    }
+
+    comparison_file = os.path.join(debug_path, "model_comparison_results.json")
+    with open(comparison_file, "w") as f:
+        json.dump(comparison_results, f, indent=2)
+    print(f"   Comparison results saved to: {comparison_file}")
+
+# Save and upload transformed model if requested
+if SAVE_TRANSFORMED_MODEL:
+    print("\nSaving Transformed Model...")
+    if are_equivalent:
+        print("Models are equivalent - proceeding with transformation and upload")
+    else:
+        print("Models are NOT equivalent, but proceeding with upload anyway")
+        print(f"   Max difference: {loss_diff.max().item():.2e}")
+        print("   This might be useful for debugging or partial transformations")
+
+    # Create timestamp for README
+    transformation_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    try:
+        # Use the already working custom policy as the base for transformation
+        print("Using already working custom policy as base for transformed model...")
+
+        # Deep copy the custom policy to create the transformed version
+        from copy import deepcopy
+
+        transformed_policy = deepcopy(custom_policy)
+
+        print("Custom policy copied successfully - no additional configuration needed")
+
+        # Save locally first
+        local_save_path = "./transformed_pi0_model"
+        print(f"Saving transformed model locally to: {local_save_path}")
+        transformed_policy.save_pretrained(local_save_path, safe_serialization=True)
+
+        # Save the tokenizer as well (required for complete model)
+        transformed_policy.language_tokenizer.save_pretrained(local_save_path)
+
+        # Create a README with transformation details
+        readme_content = f"""
+# PI0 Model - LeRobot Compatible Format
+
+This model is a transformed version of `{custom_model_path}` with key names corrected to match the official LeRobot PI0 format.
+
+## Transformation Applied
+
+The original model had a different key naming convention. This model applies the following transformations:
+
+1. **Model prefix**: Added `model.` prefix to all parameter keys
+2. **Tied weights**: Applied PI0Policy's built-in tied weights logic to create `embed_tokens.weight` from `lm_head.weight`
+3. **Key structure**: Applied standard PI0 key transformations for compatibility
+
+## Verification
+
+{"This transformed model produces **identical outputs**" if are_equivalent else "This transformed model has **slightly different outputs**"} (max difference = {loss_diff.max().item():.2e}) compared to the official model `{official_model_path}` when tested with the same inputs.
+{"**Models are EQUIVALENT** (difference < 1e-6)" if are_equivalent else "**Models are NOT equivalent** (difference >= 1e-6) - use with caution"}
+
+## Usage
+
+```python
+from lerobot.policies.pi0.modeling_pi0 import PI0Policy
+
+# Load the model
+policy = PI0Policy.from_pretrained("{TRANSFORMED_MODEL_NAME}")
+
+# Use for inference
+action = policy.select_action(observation_batch)
+```
+
+## Original Model
+
+- **Source**: {custom_model_path}
+- **Verified Against**: {official_model_path}
+
+## Technical Details
+
+- **Total Parameters**: {sum(p.numel() for p in transformed_policy.parameters()):,}
+- **Model Type**: PI0FlowMatching with PaliGemma + Expert Gemma
+- **Configuration**: Matches official PI0 configuration
+"""
+
+        readme_path = os.path.join(local_save_path, "README.md")
+        with open(readme_path, "w") as f:
+            f.write(readme_content.strip())
+
+        print(f"Model saved locally to: {local_save_path}")
+
+        # Upload to HuggingFace Hub if requested
+        if UPLOAD_TO_HUB:
+            print(f"\nUploading to HuggingFace Hub: {TRANSFORMED_MODEL_NAME}")
+
+            try:
+                # Push to hub
+                transformed_policy.push_to_hub(
+                    repo_id=TRANSFORMED_MODEL_NAME,
+                    commit_message=COMMIT_MESSAGE,
+                    private=False,  # Make it public
+                    safe_serialization=True,
+                )
+
+                print(f"Model successfully uploaded to: https://huggingface.co/{TRANSFORMED_MODEL_NAME}")
+                print("You can now use this model directly without any transformations!")
+                print("\n Usage:")
+                print("   from lerobot.policies.pi0.modeling_pi0 import PI0Policy")
+                print(f"   policy = PI0Policy.from_pretrained('{TRANSFORMED_MODEL_NAME}')")
+
+            except Exception as upload_error:
+                print(f"Failed to upload to HuggingFace Hub: {upload_error}")
+                print(f"You can manually upload the model from: {local_save_path}")
+                print("   Or set UPLOAD_TO_HUB = False and upload later")
+
+    except Exception as e:
+        import traceback
+
+        print(f"Error saving transformed model: {str(e)}")
+        print("Full traceback:")
+        traceback.print_exc()
+        print("The model transformation logic works, but saving failed")
+
+else:
+    print("\nModel transformation and upload disabled (SAVE_TRANSFORMED_MODEL = False)")

src/lerobot/datasets/v21/convert_dataset_v20_to_v21.py

@@ -13,20 +13,22 @@
 # limitations under the License.
 
 """
-This script will help you convert any LeRobot dataset already pushed to the hub from codebase version 2.0 to
+This script will help you download any LeRobot dataset from the hub, convert it to the latest format, and
-2.1. It will:
+upload it to your own repository. It will:
 
+- Download the dataset from any source repository
 - Generate per-episodes stats and writes them in `episodes_stats.jsonl`
-- Check consistency between these new stats and the old ones.
+- Update codebase_version in `info.json` to the latest version
-- Remove the deprecated `stats.json`.
+- Create proper version tags
-- Update codebase_version in `info.json`.
+- Push the converted dataset to your specified destination repository
-- Push this new version to the hub on the 'main' branch and tags it with "v2.1".
 
 Usage:
 
 ```bash
 python -m lerobot.datasets.v21.convert_dataset_v20_to_v21 \
-    --repo-id=aliberts/koch_tutorial
+    --source-repo-id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \
+    --dest-repo-id=your-username/libero_spatial_converted \
+    --episodes=0,1,2,3,4
 ```
 
 """
@@ -37,8 +39,8 @@ import logging
 from huggingface_hub import HfApi
 
 from lerobot.datasets.lerobot_dataset import CODEBASE_VERSION, LeRobotDataset
-from lerobot.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, load_stats, write_info
+from lerobot.datasets.utils import EPISODES_STATS_PATH, STATS_PATH, write_info
-from lerobot.datasets.v21.convert_stats import check_aggregate_stats, convert_stats
+from lerobot.datasets.v21.convert_stats import convert_stats
 
 V20 = "v2.0"
 V21 = "v2.1"
@@ -54,48 +56,133 @@ class SuppressWarnings:
 
 
 def convert_dataset(
-    repo_id: str,
+    source_repo_id: str,
+    dest_repo_id: str | None = None,
+    episodes: str | None = None,
     branch: str | None = None,
     num_workers: int = 4,
+    force_cache_sync: bool = True,
 ):
-    with SuppressWarnings():
+    """
-        dataset = LeRobotDataset(repo_id, revision=V20, force_cache_sync=True)
+    Download a dataset from source_repo_id, convert it, and upload to dest_repo_id.
 
+    Args:
+        source_repo_id: Source repository to download from
+        dest_repo_id: Destination repository to upload to (defaults to source_repo_id)
+        episodes: Comma-separated list of episode indices to include (e.g. "0,1,2,3")
+        branch: Branch to upload to
+        num_workers: Number of workers for stats computation
+        force_cache_sync: Whether to force cache synchronization
+    """
+    if dest_repo_id is None:
+        dest_repo_id = source_repo_id
+
+    # Parse episodes list if provided
+    episode_list = None
+    if episodes:
+        try:
+            episode_list = [int(ep.strip()) for ep in episodes.split(",")]
+            print(f"Loading episodes: {episode_list}")
+        except ValueError as e:
+            raise ValueError(
+                f"Invalid episodes format '{episodes}'. Use comma-separated integers like '0,1,2,3'"
+            ) from e
+
+    print(f"Downloading dataset from: {source_repo_id}")
+
+    # Try to load the dataset with different approaches to handle versioning issues
+    dataset = None
+    load_attempts = [
+        {"revision": None},  # Try latest first
+        {"revision": V20},  # Try v2.0
+        {"revision": "main"},  # Try main branch
+    ]
+
+    for attempt in load_attempts:
+        try:
+            print(f"Attempting to load with revision: {attempt['revision']}")
+            with SuppressWarnings():
+                dataset = LeRobotDataset(
+                    source_repo_id, episodes=episode_list, force_cache_sync=force_cache_sync, **attempt
+                )
+            print("Successfully loaded dataset!")
+            break
+        except Exception as e:
+            print(f"Failed with revision {attempt['revision']}: {e}")
+            continue
+
+    if dataset is None:
+        raise RuntimeError(f"Could not load dataset {source_repo_id} with any revision")
+
+    # Clean up old stats if present
     if (dataset.root / EPISODES_STATS_PATH).is_file():
         (dataset.root / EPISODES_STATS_PATH).unlink()
+        print("Removed existing episodes_stats.jsonl")
 
+    print("Converting stats to new format...")
     convert_stats(dataset, num_workers=num_workers)
-    ref_stats = load_stats(dataset.root)
-    check_aggregate_stats(dataset, ref_stats)
 
+    # Update dataset info
     dataset.meta.info["codebase_version"] = CODEBASE_VERSION
     write_info(dataset.meta.info, dataset.root)
+    print(f"Updated codebase_version to {CODEBASE_VERSION}")
 
-    dataset.push_to_hub(branch=branch, tag_version=False, allow_patterns="meta/")
+    # Change repo_id for destination if different
+    if dest_repo_id != source_repo_id:
+        print(f"Changing repository from {source_repo_id} to {dest_repo_id}")
+        dataset.repo_id = dest_repo_id
 
-    # delete old stats.json file
+    print(f"Pushing converted dataset to: {dest_repo_id}")
-    if (dataset.root / STATS_PATH).is_file:
+    dataset.push_to_hub(branch=branch, tag_version=False)
+
+    # Clean up old stats.json file locally and on hub
+    if (dataset.root / STATS_PATH).is_file():
         (dataset.root / STATS_PATH).unlink()
+        print("Removed local stats.json file")
 
     hub_api = HfApi()
-    if hub_api.file_exists(
+    try:
-        repo_id=dataset.repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset"
+        if hub_api.file_exists(
-    ):
+            repo_id=dest_repo_id, filename=STATS_PATH, revision=branch, repo_type="dataset"
-        hub_api.delete_file(
+        ):
-            path_in_repo=STATS_PATH, repo_id=dataset.repo_id, revision=branch, repo_type="dataset"
+            hub_api.delete_file(
-        )
+                path_in_repo=STATS_PATH, repo_id=dest_repo_id, revision=branch, repo_type="dataset"
+            )
+            print("Removed stats.json from hub")
+    except Exception as e:
+        print(f"Warning: Could not remove stats.json from hub: {e}")
 
-    hub_api.create_tag(repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
+    # Create version tag
+    try:
+        hub_api.create_tag(dest_repo_id, tag=CODEBASE_VERSION, revision=branch, repo_type="dataset")
+        print(f"Created tag {CODEBASE_VERSION} for {dest_repo_id}")
+    except Exception as e:
+        print(f"Warning: Could not create tag: {e}")
+
+    print(f"✅ Successfully converted and uploaded dataset to {dest_repo_id}")
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        description="Download, convert, and re-upload LeRobot datasets with proper versioning"
+    )
     parser.add_argument(
-        "--repo-id",
+        "--source-repo-id",
         type=str,
         required=True,
-        help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset "
+        help="Source repository identifier to download from (e.g. 'IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot')",
-        "(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
+    )
+    parser.add_argument(
+        "--dest-repo-id",
+        type=str,
+        default=None,
+        help="Destination repository identifier to upload to. Defaults to source-repo-id if not specified.",
+    )
+    parser.add_argument(
+        "--episodes",
+        type=str,
+        default=None,
+        help="Comma-separated list of episode indices to include (e.g. '0,1,2,3,4'). If not specified, all episodes are included.",
     )
     parser.add_argument(
         "--branch",
@@ -109,6 +196,22 @@ if __name__ == "__main__":
         default=4,
         help="Number of workers for parallelizing stats compute. Defaults to 4.",
     )
+    parser.add_argument(
+        "--no-cache-sync",
+        action="store_true",
+        help="Skip forcing cache synchronization (faster but may use cached data)",
+    )
 
     args = parser.parse_args()
-    convert_dataset(**vars(args))
+
+    # Convert args to match function signature
+    convert_args = {
+        "source_repo_id": args.source_repo_id,
+        "dest_repo_id": args.dest_repo_id,
+        "episodes": args.episodes,
+        "branch": args.branch,
+        "num_workers": args.num_workers,
+        "force_cache_sync": not args.no_cache_sync,
+    }
+
+    convert_dataset(**convert_args)