"""
Main script for running OpenAI 20B model using Virtual GPU infrastructure
"""
import os
import json
from typing import Dict, List, Optional, Union, Any
from pathlib import Path
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig

from virtual_gpu_driver.src.driver_api import VirtualGPUDriver
from virtual_gpu_driver.src.hal.hal import HardwareAbstractionLayer
from virtual_gpu_driver.src.memory.memory_manager import MemoryManager
from virtual_gpu_driver.src.memory_pool import MemoryPool

from helium.pipeline.unified_controller import UnifiedPipelineController
from helium.core.probability import ProbabilityCalculator
from helium.core.pipeline import Pipeline
from helium.tokenizer import HeliumTokenizer
import array

# Initialize HuggingFace token from environment
HF_TOKEN = os.getenv("HF_TOKEN")


class VGPUTensor:
    """VGPU tensor class"""
    def __init__(self, data, shape=None, dtype='float32'):
        self.driver = VirtualGPUDriver()
        self.shape = shape or self._infer_shape(data)
        self.dtype = dtype
        
        # Get memory from pool
        self.memory_pool = MemoryPool()
        self.addr = self.memory_pool.allocate(
            self._calculate_size(self.shape, dtype)
        )
        
        # Transfer data
        if isinstance(data, (list, array.array)):
            self.hal.write_memory(self.addr, array.array(self._get_typecode(dtype), data))
            
    def _infer_shape(self, data):
        if isinstance(data, list):
            shape = [len(data)]
            if isinstance(data[0], list):
                shape.extend(self._infer_shape(data[0]))
            return tuple(shape)
        return (1,)
        
    def _calculate_size(self, shape, dtype):
        total_elements = 1
        for dim in shape:
            total_elements *= dim
        return total_elements * self._get_dtype_size(dtype)
        
    def _get_dtype_size(self, dtype):
        sizes = {
            'float32': 4,
            'float64': 8,
            'int32': 4,
            'int64': 8
        }
        return sizes.get(dtype, 4)
        
    def _get_typecode(self, dtype):
        typecodes = {
            'float32': 'f',
            'float64': 'd',
            'int32': 'l',
            'int64': 'q'
        }
        return typecodes.get(dtype, 'f')

class VGPUModule:
    """Base class for VGPU neural network modules"""
    def __init__(self):
        self.driver = VirtualGPUDriver()
        self.parameters = {}
        
    def register_parameter(self, name: str, tensor: VGPUTensor):
        self.parameters[name] = tensor

class VGPUTransformerBlock(VGPUModule):
    """Transformer block implementation using VGPU"""
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.attention = HeliumMultiHeadAttention(config)
        self.mlp = HeliumMLP(config)
        self.ln_1 = HeliumLayerNorm(config.hidden_size)
        self.ln_2 = HeliumLayerNorm(config.hidden_size)
        
    def forward(self, hidden_states, attention_mask=None):
        attn_output = self.attention(self.ln_1(hidden_states), attention_mask)
        hidden_states = hidden_states + attn_output
        mlp_output = self.mlp(self.ln_2(hidden_states))
        return hidden_states + mlp_output

class HeliumGPT(HeliumModule):
    """OpenAI GPT model implementation using Helium"""
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.transformer_blocks = [
            HeliumTransformerBlock(config)
            for _ in range(config.num_hidden_layers)
        ]
        self.ln_f = HeliumLayerNorm(config.hidden_size)
        
    def forward(self, input_ids, attention_mask=None):
        hidden_states = self.get_embeddings(input_ids)
        
        for block in self.transformer_blocks:
            hidden_states = block(hidden_states, attention_mask)
            
        hidden_states = self.ln_f(hidden_states)
        logits = self.get_logits(hidden_states)
        
        return logits
        
    def get_embeddings(self, input_ids):
        # Convert input_ids to embeddings using embedding table
        pass
        
    def get_logits(self, hidden_states):
        # Convert final hidden states to logits
        pass

def load_openai_20b():
    """Load OpenAI 20B model and convert to Helium format"""
    # Initialize Helium infrastructure
    controller = UnifiedPipelineController()
    
    # Model ID for OpenAI's open source 20B model
    model_id = "openai/gpt-oss-20b"
    
    # Load model config
    config = AutoConfig.from_pretrained(model_id)
    
    # Create VGPU model
    model = VGPUGPT(config)
    
    # Load weights and convert to VGPU format
    torch_model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto"
    )
    
    # Convert weights to Helium tensors
    for name, param in torch_model.named_parameters():
        vgpu_tensor = VGPUTensor(param.detach().numpy())
        model.register_parameter(name, vgpu_tensor)
        
    return model, config

def generate_text(
    model: HeliumGPT,
    tokenizer: HeliumTokenizer,
    prompt: str,
    max_length: int = 100,
    temperature: float = 0.7,
    top_k: int = 50,
    top_p: float = 0.9
    ) -> str:
    """
    Generate text using Helium infrastructure
    
    Args:
        model: Helium model
        tokenizer: Helium tokenizer
        prompt: Input prompt
        max_length: Maximum generation length
        temperature: Sampling temperature
        top_k: Top-k sampling parameter
        top_p: Nucleus sampling parameter
        
    Returns:
        Generated text
    """
    # Initialize components
    controller = UnifiedPipelineController()
    prob_calc = ProbabilityCalculator()
    
    # Encode prompt
    input_ids = tokenizer.encode(prompt)
    
    # Create attention mask
    attention_mask = [1] * len(input_ids)
    
    # Generate tokens
    for _ in range(max_length):
        # Forward pass
        logits = model.forward(input_ids, attention_mask)
        
        # Get next token
        next_token_logits = logits[:, -1, :]
        
        # Apply temperature and sampling
        probs = prob_calc.compute_probabilities(next_token_logits, temperature)
        next_token = prob_calc.sample_from_probs(probs, top_k=top_k, top_p=top_p)
        
        # Append to sequence
        input_ids.append(next_token)
        attention_mask.append(1)
        
        # Check for end of sequence
        if next_token == tokenizer.special_tokens["[SEP]"]:
            break
            
    # Decode and return text
    return tokenizer.decode(input_ids)

if __name__ == "__main__":
    # Load model
    print("Loading OpenAI 20B model...")
    model, config = load_openai_20b()
    
    # Initialize tokenizer
    tokenizer = HeliumTokenizer()
    tokenizer.load_vocabulary("path/to/vocab.json")
    
    # Example generation
    prompt = "Once upon a time"
    print(f"\nPrompt: {prompt}")
    
    generated_text = generate_text(
        model,
        tokenizer,
        prompt,
        max_length=100,
        temperature=0.7,
        top_k=50,
        top_p=0.9
    )
    
    print(f"\nGenerated text:\n{generated_text}")
