# HuggingFace Dataset Integration Architecture

This document provides an architectural overview of how the HuggingFace dataset application integrates with CaDaR.

## System Architecture

```mermaid
graph TB
    subgraph "HuggingFace Hub"
        HF1[Datasets]
        HF2[Models]
        HF3[Spaces]
    end
    
    subgraph "Application Layer"
        CLI[CLI Interface]
        API[Python API]
        CONFIG[Configuration]
    end
    
    subgraph "Processing Layer"
        LOADER[DatasetLoader]
        PROCESSOR[CaDaRProcessor]
        CONFIGPROC[ConfigurableProcessor]
    end
    
    subgraph "CaDaR Core"
        CADAR[CaDaR Library]
        ICR[ICR Pipeline]
        TRANSL[Transliteration]
    end
    
    subgraph "Output Layer"
        HFOUT[HuggingFace Format]
        LOCAL[Local Files]
        STREAM[Streaming]
    end
    
    HF1 --> LOADER
    CLI --> LOADER
    API --> LOADER
    CONFIG --> CONFIGPROC
    
    LOADER --> PROCESSOR
    PROCESSOR --> CADAR
    CADAR --> ICR
    ICR --> TRANSL
    
    PROCESSOR --> HFOUT
    PROCESSOR --> LOCAL
    PROCESSOR --> STREAM
```

## Component Architecture

### 1. Dataset Loader Component

The DatasetLoader component is responsible for:

- Fetching datasets from HuggingFace Hub
- Loading datasets from local files
- Handling various data formats (JSON, CSV, Parquet)
- Applying filters and column mappings
- Managing dataset caching

```mermaid
graph LR
    subgraph "Dataset Loader"
        HFSRC[HF Hub Source] --> FETCH[Fetch Data]
        LOCALSRC[Local Source] --> LOAD[Load Data]
        FETCH --> FILTER[Apply Filters]
        LOAD --> FILTER
        FILTER --> MAP[Column Mapping]
        MAP --> CACHE[Cache Management]
    end
```

### 2. CaDaR Processor Component

The CaDaR Processor component integrates with CaDaR's core transliteration pipeline:

```mermaid
graph TB
    subgraph "CaDaR Processor"
        INPUT[Dataset Input] --> BATCH[Batch Processing]
        BATCH --> SCRIPT[Script Detection]
        SCRIPT --> NORM[Normalization]
        NORM --> TOKEN[Tokenization]
        TOKEN --> ICR[ICR Generation]
        ICR --> OUT[Output Generation]
    end
    
    subgraph "CaDaR Core Pipeline"
        SCRIPT --> DETECT[Detect Script]
        NORM --> CLEAN[Clean & Normalize]
        TOKEN --> SEGMENT[Word Segmentation]
        ICR --> CANON[Canonical Representation]
        OUT --> VALID[Validation & Fixes]
    end
```

### 3. Configuration System

The configuration system enables reproducible processing pipelines:

```mermaid
graph LR
    subgraph "Configuration System"
        YAML[YAML Config] --> PARSE[Parser]
        CLIARGS[CLI Args] --> MERGE[Config Merger]
        ENV[Environment] --> MERGE
        PARSE --> MERGE
        MERGE --> VALIDATE[Validator]
        VALIDATE --> CONFIG[Runtime Config]
    end
```

## Data Flow

### Dataset Processing Pipeline

```mermaid
sequenceDiagram
    participant User
    participant CLI/API
    participant Loader
    participant Processor
    participant CaDaR
    participant Storage
    
    User->>CLI/API: Process dataset request
    CLI/API->>Loader: Load dataset
    Loader->>Loader: Fetch from HF Hub or local
    Loader->>Processor: Pass dataset
    Processor->>CaDaR: Apply transliteration
    CaDaR->>Processor: Return processed text
    Processor->>Storage: Save processed dataset
    Storage->>User: Return results
```

### Batch Processing Flow

```mermaid
sequenceDiagram
    participant Processor
    participant BatchManager
    participant CaDaR
    participant OutputHandler
    
    loop For each batch
        Processor->>BatchManager: Get next batch
        BatchManager->>CaDaR: Process batch items
        CaDaR->>CaDaR: Apply transliteration pipeline
        CaDaR->>BatchManager: Return processed batch
        BatchManager->>OutputHandler: Save batch
    end
    
    OutputHandler->>Processor: Signal completion
```

## Integration Points

### 1. HuggingFace Datasets Library Integration

The application builds on the HuggingFace `datasets` library:

```python
# Core integration points
from datasets import load_dataset, Dataset, IterableDataset
from datasets.utils.file_utils import DownloadManager

# Integration features
- Dataset loading and streaming
- Caching mechanisms
- Format conversion
- Memory management
```

### 2. CaDaR Library Integration

The application uses CaDaR's core functionality:

```python
# Core integration points
from cadar import CaDaR, transliterate, standardize
from cadar.types import Script, Dialect

# Integration features
- Bidirectional transliteration
- Script standardization
- Dialect-specific processing
- Error handling
```

## Performance Considerations

### 1. Memory Management

```mermaid
graph LR
    subgraph "Memory Management"
        STREAMING[Streaming Mode] --> LOW[Low Memory Usage]
        BATCHING[Batch Processing] --> CONTROLLED[Controlled Usage]
        CACHING[Smart Caching] --> EFFICIENT[Efficient Access]
        GC[Garbage Collection] --> CLEANUP[Regular Cleanup]
    end
```

### 2. Parallelization Strategy

```mermaid
graph TB
    subgraph "Parallelization"
        DATASPLIT[Dataset Splitting] --> PROCPOOL[Process Pool]
        PROCPOOL --> MAPREDUCE[Map-Reduce Pattern]
        MAPREDUCE --> RESULTMERGE[Result Merging]
    end
```

### 3. I/O Optimization

```mermaid
graph LR
    subgraph "I/O Optimization"
        PREFETCH[Prefetching] --> PIPELINE[Pipeline Processing]
        BUFFERING[Buffered I/O] --> THROUGHPUT[Higher Throughput]
        COMPRESSION[Compression] --> STORAGE[Reduced Storage]
        PARQUET[Parquet Format] --> PERFORMANCE[Better Performance]
    end
```

## Error Handling

```mermaid
graph TB
    subgraph "Error Handling"
        INPUTERR[Input Errors] --> VALIDATE[Validation]
        PROCESSERR[Processing Errors] --> RETRY[Retry Logic]
        CADARERR[CADaR Errors] --> FALLBACK[Fallback Options]
        IOERR[I/O Errors] --> RECOVERY[Recovery Procedures]
        
        VALIDATE --> LOG[Error Logging]
        RETRY --> LOG
        FALLBACK --> LOG
        RECOVERY --> LOG
    end
```

## Extensibility

### 1. Plugin Architecture

The application supports a plugin architecture for extending functionality:

```python
# Plugin interface
class DatasetPlugin:
    def pre_load(self, config):
        """Called before dataset loading."""
        pass
    
    def post_load(self, dataset, config):
        """Called after dataset loading."""
        pass
    
    def pre_process(self, dataset, config):
        """Called before processing."""
        pass
    
    def post_process(self, dataset, config):
        """Called after processing."""
        pass
```

### 2. Custom Processing Functions

Users can define custom processing functions:

```python
def custom_processing_function(example, config):
    """Custom function for processing examples."""
    # Apply custom logic
    example["custom_field"] = process_text(example["text"])
    return example

processor = CaDaRProcessor()
processed = processor.process_dataset(
    dataset,
    text_column="text",
    target_script="latin",
    preprocess_callback=custom_processing_function
)
```

## Security Considerations

```mermaid
graph TB
    subgraph "Security"
        DOWNLOAD[Secure Downloads] --> VERIFY[Signature Verification]
        ACCESS[Access Control] --> AUTH[Authentication]
        PRIVACY[Privacy Protection] --> ANON[Anonymization]
        SANITIZE[Input Sanitization] --> VALIDATE[Validation]
    end
```

## Monitoring and Logging

```mermaid
graph LR
    subgraph "Monitoring"
        METRICS[Metrics Collection] --> DASHBOARD[Dashboard]
        LOGGING[Structured Logging] --> SEARCH[Log Search]
        PERFORMANCE[Performance Monitoring] --> ALERTS[Alerts]
        QUALITY[Quality Metrics] --> REPORTS[Reports]
    end
```
