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# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# Download the dataset
export DATASET_DIR="$TMP_DIR/datasets/cosmos_nemo_assets"
mkdir -p "$DATASET_DIR"
hf download nvidia/Cosmos-NeMo-Assets \
  --repo-type dataset \
  --local-dir "$DATASET_DIR" \
  --include "*.mp4*"
mv "$DATASET_DIR/nemo_diffusion_example_data" "$DATASET_DIR/videos"

# Create prompts for the dataset
python -m scripts.create_prompts_for_nemo_assets \
    --dataset_path "$DATASET_DIR" \
    --prompt "A video of sks teal robot."

# Train the model
torchrun $TORCHRUN_ARGS scripts/train.py \
  --config=cosmos_predict2/_src/predict2/configs/video2world/config.py \
  -- \
  experiment=predict2_video2world_training_2b_cosmos_nemo_assets \
  dataloader_train.dataset.dataset_dir="$DATASET_DIR" \
  dataloader_train.sampler.dataset.dataset_dir="$DATASET_DIR" \
  $TRAIN_ARGS

# Get path to the latest checkpoint
CHECKPOINTS_DIR="${IMAGINAIRE_OUTPUT_ROOT}/cosmos_predict_v2p5/video2world/2b_cosmos_nemo_assets/checkpoints"
CHECKPOINT_ITER="$(cat "$CHECKPOINTS_DIR/latest_checkpoint.txt")"
CHECKPOINT_DIR="$CHECKPOINTS_DIR/$CHECKPOINT_ITER"

# Convert DCP checkpoint to PyTorch format
python scripts/convert_distcp_to_pt.py "$CHECKPOINT_DIR/model" "$CHECKPOINT_DIR"

# Run inference
torchrun $TORCHRUN_ARGS examples/inference.py \
  -i "$INPUT_DIR/assets/video2world_cosmos_nemo_assets/nemo_image2world.json" \
  -o "$OUTPUT_DIR" \
  --checkpoint-path "$CHECKPOINT_DIR/model_ema_bf16.pt" \
  --experiment predict2_video2world_training_2b_cosmos_nemo_assets \
  $INFERENCE_ARGS