A powerful sparse-activation language model with web search capabilities,
tool use, and mixture-of-experts architecture.

Key Features:
- 26B total parameters, 18B active (sparse activation)
- 48-layer transformer with grouped-query attention
- 8 experts per layer with top-2 routing
- 32K context length
- Web search integration
- Multi-language support
- Tool use capabilities

Quick Start:
-----------
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Kirim-ai/Kirim-V2")
tokenizer = AutoTokenizer.from_pretrained("Kirim-ai/Kirim-V2")

prompt = "Explain quantum computing:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(outputs[0])

For more examples, see: examples.py
"""

__version__ = "2.0.0"
__author__ = "Kirim-ai"
__license__ = "Apache-2.0"

# Model information
MODEL_NAME = "Kirim-V2"
MODEL_SIZE = "26B"
ACTIVE_SIZE = "18B"
ARCHITECTURE = "Sparse Transformer + Mixture of Experts"

# Model configuration
MODEL_CONFIG = {
    "model_type": "kirim",
    "architecture": "KirimForCausalLM",
    "total_parameters": 26_000_000_000,
    "active_parameters": 18_000_000_000,
    "hidden_size": 6144,
    "intermediate_size": 16384,
    "num_hidden_layers": 48,
    "num_attention_heads": 48,
    "num_key_value_heads": 8,
    "num_experts": 8,
    "experts_per_token": 2,
    "vocab_size": 128256,
    "max_position_embeddings": 32768,
    "rope_theta": 500000.0,
}

# Capabilities
CAPABILITIES = [
    "text_generation",
    "web_search",
    "tool_use",
    "code_generation",
    "multilingual",
    "long_context",
    "reasoning",
    "question_answering",
    "summarization",
    "translation",
]

# Supported languages
SUPPORTED_LANGUAGES = [
    "en",  # English
    "es",  # Spanish
    "fr",  # French
    "de",  # German
    "it",  # Italian
    "pt",  # Portuguese
    "zh",  # Chinese
    "ja",  # Japanese
    "ko",  # Korean
    "ru",  # Russian
]

# Import utilities
try:
    from .utils import ModelUtils, calculate_model_stats
    __all__ = ["ModelUtils", "calculate_model_stats"]
except ImportError:
    # If running standalone, utils might not be in package
    try:
        from utils import ModelUtils, calculate_model_stats
        __all__ = ["ModelUtils", "calculate_model_stats"]
    except ImportError:
        __all__ = []

# Add configuration utilities
def get_model_info():
    """
    Get comprehensive model information.
    
    Returns:
        Dictionary with model details
    """
    return {
        "name": MODEL_NAME,
        "version": __version__,
        "size": MODEL_SIZE,
        "active_size": ACTIVE_SIZE,
        "architecture": ARCHITECTURE,
        "config": MODEL_CONFIG,
        "capabilities": CAPABILITIES,
        "languages": SUPPORTED_LANGUAGES,
        "license": __license__,
    }


def print_model_card():
    """Print a formatted model card with key information."""
    info = get_model_info()
    
    print("\n" + "="*70)
    print(f"{info['name']} - Model Card".center(70))
    print("="*70)
    print(f"\nVersion:        {info['version']}")
    print(f"Parameters:     {info['size']} total, {info['active_size']} active")
    print(f"Architecture:   {info['architecture']}")
    print(f"License:        {info['license']}")
    
    print(f"\nCapabilities:   {', '.join(info['capabilities'][:5])}")
    print(f"                {', '.join(info['capabilities'][5:])}")
    
    print(f"\nLanguages:      {', '.join(info['languages'][:5])}")
    print(f"                {', '.join(info['languages'][5:])}")
    
    print("\nKey Features:")
    print("  • Sparse activation (69% parameters active)")
    print("  • Mixture of Experts (8 experts, top-2 routing)")
    print("  • Extended context (32,768 tokens)")
    print("  • Web search integration")
    print("  • Tool use capabilities")
    print("  • Multi-language support")
    
    print("\nHardware Requirements:")
    print("  Minimum:     24GB VRAM (with INT8 quantization)")
    print("  Recommended: 48GB+ VRAM (BF16 precision)")
    print("  Optimal:     80GB VRAM (full batch processing)")
    
    print("\nQuick Start:")
    print("  from transformers import AutoModelForCausalLM, AutoTokenizer")
    print('  model = AutoModelForCausalLM.from_pretrained("Kirim-ai/Kirim-V2")')
    print('  tokenizer = AutoTokenizer.from_pretrained("Kirim-ai/Kirim-V2")')
    
    print("\nDocumentation:")
    print("  README:      See README.md for detailed documentation")
    print("  Quickstart:  See QUICKSTART.md for usage examples")
    print("  Examples:    See examples.py for code samples")
    print("  Web Search:  See web/README.md for search integration")
    
    print("\n" + "="*70 + "\n")


def check_requirements():
    """
    Check if required packages are installed.
    
    Returns:
        Dictionary with package availability
    """
    requirements = {}
    
    packages = [
        "torch",
        "transformers",
        "accelerate",
        "sentencepiece",
        "safetensors",
        "bitsandbytes",
    ]
    
    for package in packages:
        try:
            __import__(package)
            requirements[package] = "✓ Installed"
        except ImportError:
            requirements[package] = "✗ Not installed"
    
    return requirements


def print_requirements():
    """Print package requirements and their installation status."""
    reqs = check_requirements()
    
    print("\n" + "="*70)
    print("Package Requirements".center(70))
    print("="*70 + "\n")
    
    for package, status in reqs.items():
        print(f"  {package:<20} {status}")
    
    missing = [pkg for pkg, status in reqs.items() if "Not installed" in status]
    
    if missing:
        print("\n" + "⚠"*35)
        print("\nMissing packages detected!")
        print("Install with: pip install " + " ".join(missing))
        print("\nOr install all requirements:")
        print("  pip install -r requirements.txt")
    else:
        print("\n✓ All requirements are installed!")
    
    print("\n" + "="*70 + "\n")


# Convenience functions
def get_default_generation_config():
    """
    Get default generation configuration.
    
    Returns:
        Dictionary with generation parameters
    """
    return {
        "max_new_tokens": 512,
        "temperature": 0.7,
        "top_p": 0.9,
        "top_k": 50,
        "repetition_penalty": 1.1,
        "do_sample": True,
        "pad_token_id": None,
    }


def get_recommended_device_map():
    """
    Get recommended device map based on available hardware.
    
    Returns:
        Device map string
    """
    try:
        import torch
        if torch.cuda.is_available():
            device_count = torch.cuda.device_count()
            if device_count > 1:
                return "balanced"  # Multi-GPU
            else:
                return "auto"  # Single GPU
        else:
            return "cpu"
    except ImportError:
        return "auto"


# Export public API
__all__ = [
    # Version info
    "__version__",
    "__author__",
    "__license__",
    
    # Model info
    "MODEL_NAME",
    "MODEL_SIZE",
    "ACTIVE_SIZE",
    "ARCHITECTURE",
    "MODEL_CONFIG",
    "CAPABILITIES",
    "SUPPORTED_LANGUAGES",
    
    # Functions
    "get_model_info",
    "print_model_card",
    "check_requirements",
    "print_requirements",
    "get_default_generation_config",
    "get_recommended_device_map",
]

# Print welcome message on import (optional)
if __name__ != "__main__":
    import sys
    if hasattr(sys, 'ps1'):  # Interactive mode
        print(f"\n🚀 Kirim-V2 v{__version__} loaded")
        print(f"   Use print_model_card() for details\n")