add nemotron data processing script

data/climbmix_small/detokenize_climbmix.py

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+import os
+import glob
+import json
+import argparse
+import logging
+import tiktoken
+import tqdm
+import concurrent.futures
+import pandas as pd
+def process_file(input_file, output_folder):
+    """
+    Process a single Parquet file:
+      - Use GPT2 tokenizer to detokenize each row's tokens;
+      - Create a new DataFrame with token_count, and detokenized text;
+      - Write to a new .detokenized.parquet file;
+      - Return the filename and total token count for that file.
+    """
+    output_file = os.path.join(
+        output_folder,
+        os.path.basename(input_file)
+    )
+    os.makedirs(output_folder, exist_ok=True)
+    tokenizer = tiktoken.get_encoding("gpt2")
+    total_tokens_file = 0
+
+    try:
+        df = pd.read_parquet(input_file)
+        records = []
+
+        for _, row in df.iterrows():
+            tokens = row.get("tokens", [])
+            token_count = row.get("token_count", len(tokens))
+            total_tokens_file += token_count
+
+            try:
+                text = tokenizer.decode(tokens)
+            except Exception as e:
+                logging.error(f"Token decoding error in file {input_file}: {e}")
+                continue
+
+            record = {
+                # "token_count": token_count,
+                "text": text
+            }
+            records.append(record)
+
+        # Convert to DataFrame and save
+        # Use smaller row groups (1024 rows each) to match smollm format
+        new_df = pd.DataFrame(records)
+        new_df.to_parquet(output_file, index=False, row_group_size=1024)
+
+    except Exception as e:
+        logging.error(f"Error processing file {input_file}: {e}")
+
+    return input_file, total_tokens_file
+
+def process_folder_parallel(input_folder, output_folder, num_workers):
+    """
+    Find all .parquet files in the specified folder and process them in parallel:
+      - Start a process for each file;
+      - Display overall file processing progress using tqdm;
+      - Accumulate the token count from all files.
+    """
+    tokenized_files = glob.glob(os.path.join(input_folder, "*.parquet"))
+    if not tokenized_files:
+        logging.warning("No .parquet files found in the specified folder.")
+        return
+
+    total_tokens_all = 0
+
+    with concurrent.futures.ProcessPoolExecutor(max_workers=num_workers) as executor:
+        # Submit processing tasks for all files
+        futures = {executor.submit(process_file, file, output_folder): file for file in tokenized_files}
+        for future in tqdm.tqdm(concurrent.futures.as_completed(futures), total=len(futures), desc="Processing files"):
+            file, tokens_in_file = future.result()
+            logging.info(f"Processed file {file}, total tokens: {tokens_in_file}")
+            total_tokens_all += tokens_in_file
+
+    logging.info(f"Total tokens across all files: {total_tokens_all}")
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+    parser = argparse.ArgumentParser(
+        description="Parallel processing using openai/tiktoken to detokenize tokens in tokenized parquet files, tracking progress and token count"
+    )
+    parser.add_argument("--input_folder", type=str, help="Path to folder containing tokenized parquet files")
+    parser.add_argument("--output_folder", type=str, help="Path to output folder for detokenized parquet files")
+    parser.add_argument(
+        "--num_workers", type=int, default=os.cpu_count(), help="Number of parallel processing workers, defaults to CPU core count"
+    )
+    args = parser.parse_args()
+    process_folder_parallel(args.input_folder, args.output_folder, args.num_workers)

data/climbmix_small/download_climbmix.py

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+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="nvidia/ClimbMix",
+    repo_type="dataset",
+    allow_patterns=["climbmix_small/*.parquet"],
+    local_dir="./",
+    max_workers=16
+)

data/climbmix_small/process_climbmix.sh

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+# pip install -U "huggingface_hub[hf_transfer]"
+# export HF_HUB_ENABLE_HF_TRANSFER=1
+
+python download_climbmix.py
+python detokenize_climbmix.py --input_folder climbmix_small --output_folder ./
+rm -rf climbmix_small