Version Control

class Dataset:
    """Dataset version control operations."""
    
    version: Version
    history: History
    current_branch: str
    
    def commit(self, message: str = "") -> str:
        """
        Commit current dataset changes.
        
        Parameters:
        - message: Commit message describing changes
        
        Returns:
        str: Commit ID/hash
        """
    
    def rollback(self, version_id: str) -> None:
        """
        Rollback dataset to specific version.
        
        Parameters:
        - version_id: Version ID to rollback to
        """
    
    def refresh(self) -> None:
        """Refresh dataset to latest version from storage."""

class Version:
    """Single version information."""
    
    id: str
    message: str
    timestamp: str
    client_timestamp: str
    
    def open(self) -> ReadOnlyDataset:
        """
        Open this version as read-only dataset.
        
        Returns:
        ReadOnlyDataset: Dataset at this version
        """
    
    def open_async(self) -> Future[ReadOnlyDataset]:
        """
        Open this version asynchronously.
        
        Returns:
        Future[ReadOnlyDataset]: Future resolving to dataset at this version
        """

class History:
    """Version history access."""
    
    def __getitem__(self, key: Union[int, str]) -> Version:
        """
        Access version by index or ID.
        
        Parameters:
        - key: Version index (int) or version ID (str)
        
        Returns:
        Version: Version object
        """
    
    def __iter__(self) -> Iterator[Version]:
        """Iterate over all versions in chronological order."""
    
    def __len__(self) -> int:
        """Get total number of versions."""

class Dataset:
    """Dataset branch operations."""
    
    branches: Branches
    
    def branch(self, name: str) -> Branch:
        """
        Create new branch from current state.
        
        Parameters:
        - name: Branch name
        
        Returns:
        Branch: New branch object
        """
    
    def merge(self, branch_name: str, message: str = "") -> None:
        """
        Merge branch into current branch.
        
        Parameters:
        - branch_name: Name of branch to merge
        - message: Merge commit message
        """

class Branch:
    """Dataset branch management."""
    
    id: str
    name: str
    timestamp: str
    base: str
    
    def open(self) -> Dataset:
        """
        Open this branch for modification.
        
        Returns:
        Dataset: Mutable dataset on this branch
        """
    
    def open_async(self) -> Future[Dataset]:
        """
        Open this branch asynchronously.
        
        Returns:
        Future[Dataset]: Future resolving to mutable dataset
        """
    
    def delete(self) -> None:
        """Delete this branch (cannot delete main branch)."""
    
    def rename(self, new_name: str) -> None:
        """
        Rename this branch.
        
        Parameters:
        - new_name: New branch name
        """

class BranchView:
    """Read-only branch information."""
    
    id: str
    name: str
    timestamp: str
    base: str
    
    def open(self) -> ReadOnlyDataset:
        """Open this branch as read-only dataset."""
    
    def open_async(self) -> Future[ReadOnlyDataset]:
        """Open this branch asynchronously."""

class Branches:
    """Collection of branches (mutable)."""
    
    def names(self) -> List[str]:
        """
        Get all branch names.
        
        Returns:
        List[str]: List of branch names
        """
    
    def __len__(self) -> int:
        """Get number of branches."""
    
    def __getitem__(self, name: str) -> Branch:
        """
        Get branch by name.
        
        Parameters:
        - name: Branch name
        
        Returns:
        Branch: Branch object
        """

class BranchesView:
    """Collection of branches (read-only)."""
    
    def names(self) -> List[str]:
        """Get all branch names."""
    
    def __len__(self) -> int:
        """Get number of branches."""
    
    def __getitem__(self, name: str) -> BranchView:
        """Get branch by name."""

class Dataset:
    """Dataset tag operations."""
    
    tags: Tags
    
    def tag(self, name: str, message: str = "") -> Tag:
        """
        Create tag at current version.
        
        Parameters:
        - name: Tag name
        - message: Tag message/description
        
        Returns:
        Tag: New tag object
        """

class DatasetView:
    """Query result tag operations."""
    
    def tag(self, name: str, message: str = "") -> TagView:
        """Create tag from query result view."""

class Tag:
    """Dataset tag management."""
    
    id: str
    name: str
    message: str
    version: str
    timestamp: str
    
    def open(self) -> ReadOnlyDataset:
        """
        Open dataset at this tag version.
        
        Returns:
        ReadOnlyDataset: Dataset at tag version
        """
    
    def open_async(self) -> Future[ReadOnlyDataset]:
        """
        Open dataset at this tag asynchronously.
        
        Returns:
        Future[ReadOnlyDataset]: Future resolving to dataset
        """
    
    def delete(self) -> None:
        """Delete this tag."""
    
    def rename(self, new_name: str) -> None:
        """
        Rename this tag.
        
        Parameters:
        - new_name: New tag name
        """

class TagView:
    """Read-only tag information."""
    
    id: str
    name: str
    message: str
    version: str
    timestamp: str
    
    def open(self) -> ReadOnlyDataset:
        """Open dataset at this tag version."""
    
    def open_async(self) -> Future[ReadOnlyDataset]:
        """Open dataset at this tag asynchronously."""

class Tags:
    """Collection of tags (mutable)."""
    
    def names(self) -> List[str]:
        """
        Get all tag names.
        
        Returns:
        List[str]: List of tag names
        """
    
    def __len__(self) -> int:
        """Get number of tags."""
    
    def __getitem__(self, name: str) -> Tag:
        """
        Get tag by name.
        
        Parameters:
        - name: Tag name
        
        Returns:
        Tag: Tag object
        """

class TagsView:
    """Collection of tags (read-only)."""
    
    def names(self) -> List[str]:
        """Get all tag names."""
    
    def __len__(self) -> int:
        """Get number of tags."""
    
    def __getitem__(self, name: str) -> TagView:
        """Get tag by name."""

class Dataset:
    """Remote synchronization operations."""
    
    def push(self) -> None:
        """Push local changes to remote storage."""
    
    def pull(self) -> None:
        """Pull remote changes to local dataset."""

class ReadOnlyDataset:
    """Remote operations for read-only datasets."""
    
    def push(self) -> None:
        """Push dataset state to remote (metadata only)."""
    
    def refresh(self) -> None:
        """Refresh dataset from remote storage."""

import deeplake

# Open dataset and make changes
dataset = deeplake.open("./my_dataset")

# Add some data
dataset.append({"images": "new_image.jpg", "labels": "cat"})

# Commit changes
commit_id = dataset.commit("Added new cat image")
print(f"Committed changes: {commit_id}")

# View commit history
for version in dataset.history:
    print(f"Version {version.id}: {version.message} ({version.timestamp})")

# Get current version info
current_version = dataset.version
print(f"Current version: {current_version.id}")
print(f"Commit message: {current_version.message}")

# Create new branch for experiments
experiment_branch = dataset.branch("feature_experiment")
print(f"Created branch: {experiment_branch.name}")

# List all branches
print("Available branches:")
for branch_name in dataset.branches.names():
    branch = dataset.branches[branch_name]
    print(f"  {branch.name} (created: {branch.timestamp})")

# Switch to experiment branch
experiment_dataset = experiment_branch.open()

# Make experimental changes
experiment_dataset.add_column("confidence", deeplake.types.Float32())
experiment_dataset.append({
    "images": "experiment_image.jpg", 
    "labels": "experimental_label",
    "confidence": 0.95
})

# Commit experimental changes
experiment_dataset.commit("Added confidence scores for experimentation")

# Switch back to main branch
main_dataset = dataset.branches["main"].open()

# Merge experimental branch into main
main_dataset.merge("feature_experiment", "Merged confidence score feature")

# Clean up: delete experimental branch
experiment_branch.delete()

# Create tags for important milestones
v1_tag = dataset.tag("v1.0", "Initial production dataset")
print(f"Created tag: {v1_tag.name}")

# Add more data and create another tag
dataset.extend([
    {"images": f"batch_image_{i}.jpg", "labels": f"label_{i}"} 
    for i in range(100)
])
dataset.commit("Added batch of 100 images")

v1_1_tag = dataset.tag("v1.1", "Added training batch")

# List all tags
print("Available tags:")
for tag_name in dataset.tags.names():
    tag = dataset.tags[tag_name]
    print(f"  {tag.name}: {tag.message} (version: {tag.version})")

# Open dataset at specific tag
v1_dataset = v1_tag.open()
print(f"Dataset at v1.0 has {len(v1_dataset)} rows")

# Compare with current version
print(f"Current dataset has {len(dataset)} rows")

# Examine version history
print(f"Dataset has {len(dataset.history)} versions")

# Get specific version
latest_version = dataset.history[-1]  # Most recent
first_version = dataset.history[0]    # First version

print(f"Latest: {latest_version.message}")
print(f"First: {first_version.message}")

# Open dataset at specific version
historical_dataset = first_version.open()
print(f"First version had {len(historical_dataset)} rows")

# Rollback to previous version if needed
if len(dataset.history) > 1:
    previous_version = dataset.history[-2]
    dataset.rollback(previous_version.id)
    print(f"Rolled back to: {previous_version.message}")

# Push local changes to remote storage
dataset.push()
print("Pushed local changes to remote")

# Pull remote changes (in another location/process)
remote_dataset = deeplake.open("s3://my-bucket/shared_dataset")
remote_dataset.pull()
print("Pulled latest changes from remote")

# Refresh to get latest version without pulling changes
remote_dataset.refresh()
print("Refreshed dataset metadata from remote")

# Typical collaborative workflow

# Developer A: Create feature branch
dataset_a = deeplake.open("s3://shared-bucket/project_dataset")
feature_branch = dataset_a.branch("add_validation_data")
feature_dataset = feature_branch.open()

# Add validation data
validation_data = [
    {"images": f"val_image_{i}.jpg", "labels": f"val_label_{i}"} 
    for i in range(500)
]
feature_dataset.extend(validation_data)
feature_dataset.commit("Added validation dataset")

# Push branch to remote
feature_dataset.push()

# Developer B: Pull and review changes
dataset_b = deeplake.open("s3://shared-bucket/project_dataset")
dataset_b.pull()

# Review feature branch
feature_branch_b = dataset_b.branches["add_validation_data"]
feature_data_b = feature_branch_b.open()
print(f"Feature branch has {len(feature_data_b)} total rows")

# Merge into main after review
main_dataset = dataset_b.branches["main"].open()
main_dataset.merge("add_validation_data", "Merged validation data from feature branch")
main_dataset.push()

# Tag the release
release_tag = main_dataset.tag("v2.0", "Added validation dataset - ready for training")

# Complex branching scenario
dataset = deeplake.open("./complex_dataset")

# Create multiple feature branches
data_cleaning_branch = dataset.branch("data_cleaning")
augmentation_branch = dataset.branch("data_augmentation")
labeling_branch = dataset.branch("relabeling")

# Work on data cleaning
cleaning_dataset = data_cleaning_branch.open()
# ... perform data cleaning operations
cleaning_dataset.commit("Cleaned corrupted entries")

# Work on augmentation
aug_dataset = augmentation_branch.open()
# ... add augmented data
aug_dataset.commit("Added augmented training examples")

# Merge branches sequentially
main_dataset = dataset.branches["main"].open()

# Merge data cleaning first
main_dataset.merge("data_cleaning", "Merged data cleaning improvements")

# Merge augmentation
main_dataset.merge("data_augmentation", "Merged data augmentation")

# Create milestone tag
milestone_tag = main_dataset.tag("preprocessing_complete", "Completed data preprocessing pipeline")

# Clean up feature branches
data_cleaning_branch.delete()
augmentation_branch.delete()

print(f"Completed preprocessing. Dataset now has {len(main_dataset)} rows")

# Track ML experiments with versions
dataset = deeplake.open("./experiment_dataset")

# Create experiment tracking
experiment_results = []

for experiment_id in range(5):
    # Create experiment branch
    exp_branch = dataset.branch(f"experiment_{experiment_id}")
    exp_dataset = exp_branch.open()
    
    # Apply different preprocessing
    # ... experiment-specific data modifications
    
    exp_dataset.commit(f"Applied preprocessing for experiment {experiment_id}")
    
    # Tag experiment version
    exp_tag = exp_dataset.tag(f"exp_{experiment_id}_data", f"Data for experiment {experiment_id}")
    
    # Record experiment info
    experiment_results.append({
        "experiment_id": experiment_id,
        "branch": exp_branch.name,
        "tag": exp_tag.name,
        "data_version": exp_dataset.version.id,
        "num_samples": len(exp_dataset)
    })
    
    # Clean up branch after tagging
    exp_branch.delete()

# Review all experiments
print("Experiment Summary:")
for result in experiment_results:
    print(f"Experiment {result['experiment_id']}: {result['num_samples']} samples, tagged as {result['tag']}")