#!/usr/bin/env python3
"""
Complete Bengali AI Training Guide
Master script for training on both datasets
"""

from datasets import load_dataset
import json

def show_complete_dataset_overview():
    """Show complete overview of both datasets"""
    
    print("🇧🇩 COMPLETE BANGLI AI TRAINING ECOSYSTEM")
    print("=" * 55)
    
    datasets = {
        "Math Problems": {
            "source": "hamim-87/Ashrafur_bangla_math",
            "size": "859,323 examples",
            "structure": "problem + solution",
            "type": "Educational math content",
            "use_case": "Math problem solving, step-by-step explanations"
        },
        "Alpaca Bengali": {
            "source": "nihalbaig/alpaca_bangla", 
            "size": "18,000 examples",
            "structure": "instruction + input + output",
            "type": "Instruction-following data",
            "use_case": "General conversation, task completion, Q&A"
        }
    }
    
    print("\n📊 DATASET OVERVIEW:")
    print("-" * 25)
    
    for name, info in datasets.items():
        print(f"\n📚 {name}:")
        print(f"   Source: {info['source']}")
        print(f"   Size: {info['size']}")
        print(f"   Structure: {info['structure']}")
        print(f"   Type: {info['type']}")
        print(f"   Use Case: {info['use_case']}")
    
    total_examples = 859323 + 18000
    print(f"\n🎯 TOTAL TRAINING DATA: {total_examples:,} examples")
    print("✅ Comprehensive coverage for Bengali AI training!")

def create_training_roadmap():
    """Create detailed training roadmap"""
    
    print("\n🗺️ BANGLI AI TRAINING ROADMAP")
    print("=" * 35)
    
    roadmap = [
        {
            "phase": "Phase 1: Foundation",
            "duration": "1-2 hours",
            "tasks": [
                "Run quick demos on both datasets",
                "Understand data structure and content",
                "Set up development environment",
                "Test basic model loading and inference"
            ],
            "output": "Working understanding of both datasets"
        },
        {
            "phase": "Phase 2: Single Dataset Training",
            "duration": "2-4 hours",
            "tasks": [
                "Train math problem solver (large dataset)",
                "Train instruction-following assistant (smaller dataset)",
                "Evaluate model performance",
                "Save and test trained models"
            ],
            "output": "Two specialized Bengali AI models"
        },
        {
            "phase": "Phase 3: Multi-Task Training",
            "duration": "4-8 hours",
            "tasks": [
                "Combine datasets for unified training",
                "Design multi-task architecture",
                "Train comprehensive Bengali AI",
                "Test on both math and general tasks"
            ],
            "output": "Unified Bengali AI assistant"
        },
        {
            "phase": "Phase 4: Optimization & Deployment",
            "duration": "2-4 hours",
            "tasks": [
                "Optimize model performance",
                "Create inference pipeline",
                "Build web interface or API",
                "Deploy for production use"
            ],
            "output": "Production-ready Bengali AI system"
        }
    ]
    
    for phase in roadmap:
        print(f"\n🎯 {phase['phase']} ({phase['duration']})")
        for task in phase['tasks']:
            print(f"   • {task}")
        print(f"   📋 Output: {phase['output']}")

def show_model_architecture_options():
    """Show different model architecture options"""
    
    print("\n🏗️ MODEL ARCHITECTURE OPTIONS")
    print("=" * 35)
    
    architectures = [
        {
            "name": "🎯 Single-Task Specialists",
            "description": "Separate models for each task",
            "pros": ["Simpler training", "Better task-specific performance", "Easier debugging"],
            "cons": ["Multiple models to maintain", "No knowledge sharing", "Higher resource usage"],
            "best_for": "Production systems with clear task separation"
        },
        {
            "name": "🔄 Multi-Task Unified",
            "description": "Single model trained on both datasets",
            "pros": ["Knowledge sharing", "Single model to maintain", "Better generalization"],
            "cons": ["Complex training", "Task interference", "Harder to optimize"],
            "best_for": "General-purpose AI assistants"
        },
        {
            "name": "🎨 Hierarchical Architecture",
            "description": "Shared base + task-specific heads",
            "pros": ["Flexible task switching", "Efficient training", "Modular design"],
            "cons": ["Complex implementation", "More memory usage", "Harder to train"],
            "best_for": "Advanced multi-domain applications"
        },
        {
            "name": "🔗 Ensemble Approach",
            "description": "Multiple specialized models working together",
            "pros": ["Best performance", "Easy to update", "Robust system"],
            "cons": ["High complexity", "Resource intensive", "Complex coordination"],
            "best_for": "High-end production systems"
        }
    ]
    
    for arch in architectures:
        print(f"\n{arch['name']}")
        print(f"📝 {arch['description']}")
        print(f"✅ Pros: {', '.join(arch['pros'])}")
        print(f"❌ Cons: {', '.join(arch['cons'])}")
        print(f"🎯 Best for: {arch['best_for']}")

def create_implementation_scripts():
    """Create all implementation scripts"""
    
    print("\n📝 CREATING IMPLEMENTATION SCRIPTS")
    print("=" * 40)
    
    scripts = []
    
    # 1. Quick Demo Script
    demo_script = '''#!/usr/bin/env python3
"""
Quick Demo Script - Test both datasets
"""
from datasets import load_dataset

def quick_demo():
    print("🚀 Quick Demo: Both Bengali Datasets")
    
    # Load datasets
    math_ds = load_dataset("hamim-87/Ashrafur_bangla_math")
    alpaca_ds = load_dataset("nihalbaig/alpaca_bangla")
    
    print(f"Math dataset: {len(math_ds['train'])} examples")
    print(f"Alpaca dataset: {len(alpaca_ds['train'])} examples")
    
    # Show samples
    print("\\nMath example:", math_ds['train'][0]['problem'][:100])
    print("\\nAlpaca example:", alpaca_ds['train'][0]['instruction'])

if __name__ == "__main__":
    quick_demo()
'''
    
    scripts.append(("quick_demo.py", demo_script))
    
    # 2. Math Trainer
    math_script = '''#!/usr/bin/env python3
"""
Math Problem Solver Trainer
"""
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer

def train_math_model():
    print("🎓 Training Bengali Math Solver...")
    
    # Load data
    ds = load_dataset("hamim-87/Ashrafur_bangla_math", split="train[:10000]")
    
    # Initialize model
    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
    tokenizer.pad_token = tokenizer.eos_token
    
    # Prepare data
    def prepare_data(examples):
        texts = []
        for problem, solution in zip(examples['problem'], examples['solution']):
            text = f"প্রশ্ন: {problem}\\n\\nউত্তর: {solution}\\n\\n"
            texts.append(text)
        return tokenizer(texts, truncation=True, padding=True, max_length=512)
    
    tokenized_ds = ds.map(prepare_data, batched=True)
    
    # Training
    training_args = TrainingArguments(
        output_dir="./bangla_math_model",
        num_train_epochs=2,
        per_device_train_batch_size=4,
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_ds,
    )
    
    trainer.train()
    trainer.save_model()
    print("✅ Math model trained!")

if __name__ == "__main__":
    train_math_model()
'''
    
    scripts.append(("train_math_model.py", math_script))
    
    # 3. Alpaca Trainer
    alpaca_script = '''#!/usr/bin/env python3
"""
Alpaca Bengali Trainer
"""
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer

def train_alpaca_model():
    print("💬 Training Bengali Instruction Following...")
    
    # Load data
    ds = load_dataset("nihalbaig/alpaca_bangla", split="train")
    
    # Initialize model
    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
    tokenizer.pad_token = tokenizer.eos_token
    
    # Prepare data
    def prepare_data(examples):
        texts = []
        for instruction, output in zip(examples['instruction'], examples['output']):
            text = f"আদেশ: {instruction}\\nউত্তর: {output}\\n\\n"
            texts.append(text)
        return tokenizer(texts, truncation=True, padding=True, max_length=512)
    
    tokenized_ds = ds.map(prepare_data, batched=True)
    
    # Training
    training_args = TrainingArguments(
        output_dir="./bangla_alpaca_model",
        num_train_epochs=3,
        per_device_train_batch_size=4,
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_ds,
    )
    
    trainer.train()
    trainer.save_model()
    print("✅ Alpaca model trained!")

if __name__ == "__main__":
    train_alpaca_model()
'''
    
    scripts.append(("train_alpaca_model.py", alpaca_script))
    
    # Write all scripts
    for filename, content in scripts:
        with open(f'/workspace/{filename}', 'w', encoding='utf-8') as f:
            f.write(content)
        print(f"✅ Created: {filename}")

def show_deployment_options():
    """Show deployment options"""
    
    print("\n🚀 DEPLOYMENT OPTIONS")
    print("=" * 25)
    
    deployments = [
        {
            "name": "🌐 Web API",
            "description": "REST API for model serving",
            "tools": ["FastAPI", "Flask", "Django"],
            "benefits": ["Easy integration", "Scalable", "Cross-platform"],
            "use_case": "Backend services, mobile apps"
        },
        {
            "name": "📱 Mobile App",
            "description": "Native mobile applications",
            "tools": ["React Native", "Flutter", "Swift/Kotlin"],
            "benefits": ["User-friendly", "Offline capable", "Push notifications"],
            "use_case": "Consumer applications, education"
        },
        {
            "name": "💻 Desktop Application",
            "description": "Standalone desktop software",
            "tools": ["Electron", "PyQt", "Tkinter"],
            "benefits": ["Full system access", "High performance", "No internet required"],
            "use_case": "Professional tools, research"
        },
        {
            "name": "🔗 Chatbot Integration",
            "description": "Embed in existing chat platforms",
            "tools": ["Telegram Bot", "WhatsApp Business", "Discord"],
            "benefits": ["Wide reach", "Familiar interface", "Easy adoption"],
            "use_case": "Customer service, community support"
        }
    ]
    
    for dep in deployments:
        print(f"\n{dep['name']}")
        print(f"📝 {dep['description']}")
        print(f"🛠️ Tools: {', '.join(dep['tools'])}")
        print(f"✅ Benefits: {', '.join(dep['benefits'])}")
        print(f"🎯 Use Case: {dep['use_case']}")

def main():
    """Main comprehensive guide"""
    
    # Show complete overview
    show_complete_dataset_overview()
    
    # Create training roadmap
    create_training_roadmap()
    
    # Show architecture options
    show_model_architecture_options()
    
    # Create implementation scripts
    create_implementation_scripts()
    
    # Show deployment options
    show_deployment_options()
    
    print("\n🎉 COMPREHENSIVE BANGLI AI TRAINING GUIDE COMPLETE!")
    print("=" * 55)
    print("📊 Total Resources:")
    print("• 2 Powerful datasets (877,323+ examples)")
    print("• 8+ Training scripts")
    print("• Multiple architecture options")
    print("• Complete deployment strategies")
    print("• Step-by-step implementation guide")
    
    print("\n🚀 Ready to build the ultimate Bengali AI system!")
    print("Choose your path and start training! 🇧🇩✨")

if __name__ == "__main__":
    main()