Upload 1_hf_up_and_download.py with huggingface_hub

1_hf_up_and_download.py

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+import argparse
+import os
+from huggingface_hub import upload_file, hf_hub_download, create_repo
+import time
+import math
+from pathlib import Path
+import subprocess
+
+def split_large_file(file_path, chunk_size_mb=1000):
+    """Split a large file into smaller chunks."""
+    file_path = Path(file_path)
+    file_size = os.path.getsize(file_path) / (1024 * 1024)  # Size in MB
+    
+    if file_size <= chunk_size_mb:
+        print(f"File {file_path.name} is {file_size:.2f}MB, no need to split.")
+        return [file_path]
+    
+    # Create a directory for chunks if it doesn't exist
+    chunks_dir = file_path.parent / f"{file_path.stem}_chunks"
+    os.makedirs(chunks_dir, exist_ok=True)
+    
+    # Calculate number of chunks needed
+    num_chunks = math.ceil(file_size / chunk_size_mb)
+    print(f"Splitting {file_path.name} ({file_size:.2f}MB) into {num_chunks} chunks...")
+    
+    # Use split command for efficient splitting
+    chunk_prefix = chunks_dir / file_path.stem
+    subprocess.run([
+        "split", 
+        "-b", f"{chunk_size_mb}m", 
+        str(file_path), 
+        f"{chunk_prefix}_part_"
+    ])
+    
+    # Get all chunk files
+    chunk_files = sorted(chunks_dir.glob(f"{file_path.stem}_part_*"))
+    print(f"Created {len(chunk_files)} chunk files in {chunks_dir}")
+    return chunk_files
+
+def upload_files(api_token, repo_id):
+    # Create the repository first if it doesn't exist
+    try:
+        create_repo(
+            repo_id=repo_id,
+            token=api_token,
+            repo_type="dataset",
+            private=False  # Set to False for a public dataset
+        )
+        print(f"Created repository: {repo_id}")
+    except Exception as e:
+        print(f"Repository already exists or error occurred: {e}")
+    
+    # Add a delay to ensure repository creation is complete
+    time.sleep(5)
+
+    # Upload the script itself
+    try:
+        script_path = "1_hf_up_and_download.py"
+        print(f"Uploading script: {script_path}")
+        upload_file(
+            repo_id=repo_id,
+            path_or_fileobj=script_path,
+            path_in_repo=script_path,
+            token=api_token,
+            repo_type="dataset",
+        )
+        print(f"Uploaded {script_path} to {repo_id}/{script_path}")
+    except Exception as e:
+        print(f"Upload failed for script: {e}")
+
+    # Split the large file into chunks if needed
+    local_file = "pdfs.tar.gz"
+    chunk_files = split_large_file(local_file)
+    
+    # Upload each chunk
+    for i, chunk_file in enumerate(chunk_files):
+        try:
+            repo_file = chunk_file.name
+            print(f"Uploading chunk {i+1}/{len(chunk_files)}: {repo_file}")
+            
+            upload_file(
+                repo_id=repo_id,
+                path_or_fileobj=str(chunk_file),
+                path_in_repo=repo_file,
+                token=api_token,
+                repo_type="dataset",
+            )
+            print(f"Uploaded {chunk_file} to {repo_id}/{repo_file}")
+        except Exception as e:
+            print(f"Upload failed for {chunk_file}: {e}")
+
+def download_files(api_token, repo_id):
+    # Check if we have split files
+    try:
+        # List files in the repository
+        from huggingface_hub import list_repo_files
+        files = list_repo_files(repo_id=repo_id, repo_type="dataset", token=api_token)
+        
+        # Filter for our chunk files
+        chunk_files = [f for f in files if f.startswith("pdfs_part_") or "chunks" in f]
+        
+        if chunk_files:
+            print(f"Found {len(chunk_files)} chunk files. Downloading...")
+            os.makedirs("chunks", exist_ok=True)
+            
+            for file in chunk_files:
+                downloaded_path = hf_hub_download(
+                    repo_id=repo_id,
+                    filename=file,
+                    token=api_token,
+                    repo_type="dataset",
+                    local_dir="chunks",
+                    local_dir_use_symlinks=False
+                )
+                print(f"Downloaded {file} to {downloaded_path}")
+            
+            print("To combine chunks, use: cat chunks/pdfs_part_* > pdfs.tar.gz")
+            return
+    except Exception as e:
+        print(f"Error checking for chunk files: {e}")
+    
+    # Fall back to downloading the single file if no chunks found
+    try:
+        downloaded_path = hf_hub_download(
+            repo_id=repo_id,
+            filename="pdfs.tar.gz",
+            token=api_token,
+            repo_type="dataset",
+            local_dir=".",
+            local_dir_use_symlinks=False
+        )
+        print(f"Downloaded pdfs.tar.gz file to {downloaded_path}")
+    except Exception as e:
+        print(f"Download failed: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Upload or download files to/from a remote Hugging Face dataset."
+    )
+    parser.add_argument(
+        "operation",
+        choices=["upload", "download"],
+        help="Specify the operation: upload or download."
+    )
+    args = parser.parse_args()
+
+    # Try to get API token from environment variables or HF cache
+    API_TOKEN = os.environ.get("HUGGINGFACE_API_TOKEN")
+    if not API_TOKEN:
+        API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+        if not API_TOKEN:
+            try:
+                from huggingface_hub.constants import HF_TOKEN_PATH
+                if os.path.exists(HF_TOKEN_PATH):
+                    with open(HF_TOKEN_PATH, "r") as f:
+                        API_TOKEN = f.read().strip()
+            except ImportError:
+                pass
+            
+            if not API_TOKEN:
+                raise ValueError("No Hugging Face API token found. Please set HUGGINGFACE_API_TOKEN environment variable or login using `huggingface-cli login`")
+    
+    # Include your username in the repo_id
+    username = "liuganghuggingface"  # Replace with your actual Hugging Face username
+    repo_id = f"{username}/polymer_semantic_pdfs"
+
+    if args.operation == "upload":
+        upload_files(API_TOKEN, repo_id)
+    elif args.operation == "download":
+        download_files(API_TOKEN, repo_id)
+
+if __name__ == "__main__":
+    main()