#!/usr/bin/env python3
"""
Complete OpenFold++ Pipeline with Diffusion Refinement

This module integrates all OpenFold++ components:
- PLM embeddings (Phase A)
- Slim EvoFormer (Phase B) 
- Distilled weights (Phase C)
- SE(3) diffusion refiner (Phase D)
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import time
from typing import Dict, List, Tuple, Optional, Union
import logging
from dataclasses import dataclass
from pathlib import Path
import sys

# Add src to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from openfoldpp.models.esm_wrapper import create_esm_wrapper
from openfoldpp.modules.plm_projection import create_plm_projector
from openfoldpp.modules.slim_evoformer import create_slim_evoformer, SlimEvoFormerConfig
from openfoldpp.modules.diffusion_refiner import create_diffusion_refiner, DiffusionRefinerConfig


@dataclass
class OpenFoldPlusPlusConfig:
    """Complete configuration for OpenFold++."""
    
    # PLM settings (Phase A)
    plm_model: str = "esm2_t33_650M_UR50D"
    plm_quantize: bool = True
    plm_projection_type: str = "linear"
    
    # EvoFormer settings (Phase B)
    evoformer_blocks: int = 24
    evoformer_hidden_dim: int = 256
    use_gqa: bool = True
    use_swiglu: bool = True
    use_weight_sharing: bool = True
    use_flash_attention: bool = True
    
    # Diffusion refiner settings (Phase D)
    refiner_enabled: bool = True
    refiner_hidden_dim: int = 256
    refiner_iterations: int = 2
    refiner_timesteps: int = 50
    
    # Performance settings
    device: str = "cuda" if torch.cuda.is_available() else "cpu"
    mixed_precision: bool = True
    gradient_checkpointing: bool = True
    
    # Inference settings
    max_sequence_length: int = 1024
    target_latency_ms: float = 1000.0  # <1s added latency for refiner


class MockStructureModule(nn.Module):
    """
    Mock structure module for testing.
    
    In production, this would be the actual OpenFold structure module
    that converts single representations to 3D coordinates.
    """
    
    def __init__(self, hidden_dim: int = 384):
        super().__init__()
        
        self.hidden_dim = hidden_dim
        
        # Mock structure prediction layers
        self.coord_projection = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 3)  # x, y, z coordinates
        )
        
        # Mock confidence prediction
        self.confidence_projection = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid()
        )
    
    def forward(self, single_repr: torch.Tensor) -> Dict[str, torch.Tensor]:
        """
        Mock structure prediction.
        
        Args:
            single_repr: [batch, seq_len, hidden_dim] single representation
            
        Returns:
            Dictionary with coordinates and confidence
        """
        
        # Predict coordinates
        coordinates = self.coord_projection(single_repr)  # [batch, seq_len, 3]
        
        # Predict confidence
        confidence = self.confidence_projection(single_repr).squeeze(-1)  # [batch, seq_len]
        
        return {
            'coordinates': coordinates,
            'confidence': confidence
        }


class OpenFoldPlusPlus(nn.Module):
    """
    Complete OpenFold++ model with all optimizations.
    
    Pipeline:
    1. Sequence → PLM embeddings (ESM-2)
    2. PLM embeddings → MSA projection
    3. MSA + Pair → Slim EvoFormer
    4. Single repr → Structure module → Initial coordinates
    5. Initial coordinates → Diffusion refiner → Final coordinates
    """
    
    def __init__(self, config: OpenFoldPlusPlusConfig = None):
        super().__init__()
        
        self.config = config or OpenFoldPlusPlusConfig()
        
        # Initialize components
        self._init_plm_components()
        self._init_evoformer()
        self._init_structure_module()
        self._init_refiner()
        
        # Performance tracking
        self.register_buffer('inference_times', torch.zeros(5))  # Track component times
        
        logging.info("OpenFold++ pipeline initialized with all optimizations")
    
    def _init_plm_components(self):
        """Initialize PLM embedding and projection components."""
        
        # ESM-2 wrapper (Phase A)
        self.esm_wrapper = create_esm_wrapper(
            model_name=self.config.plm_model,
            device=self.config.device,
            quantize=self.config.plm_quantize
        )
        
        # PLM projector (Phase A)
        self.plm_projector = create_plm_projector(
            projection_type=self.config.plm_projection_type
        )
    
    def _init_evoformer(self):
        """Initialize slim EvoFormer (Phase B)."""
        
        evoformer_config = SlimEvoFormerConfig(
            no_blocks=self.config.evoformer_blocks,
            c_m=self.config.evoformer_hidden_dim,
            use_gqa=self.config.use_gqa,
            use_swiglu=self.config.use_swiglu,
            use_weight_sharing=self.config.use_weight_sharing,
            use_flash_attention=self.config.use_flash_attention
        )
        
        self.evoformer = create_slim_evoformer(evoformer_config)
    
    def _init_structure_module(self):
        """Initialize structure prediction module."""
        
        # Mock structure module for testing
        self.structure_module = MockStructureModule(
            hidden_dim=self.config.evoformer_hidden_dim
        )
    
    def _init_refiner(self):
        """Initialize diffusion refiner (Phase D)."""
        
        if self.config.refiner_enabled:
            refiner_config = DiffusionRefinerConfig(
                hidden_dim=self.config.refiner_hidden_dim,
                num_iterations=self.config.refiner_iterations,
                num_timesteps=self.config.refiner_timesteps
            )
            
            self.refiner = create_diffusion_refiner(refiner_config)
        else:
            self.refiner = None
    
    def extract_plm_embeddings(self, sequences: List[str]) -> torch.Tensor:
        """Extract PLM embeddings from sequences."""
        
        start_time = time.time()
        
        # Extract ESM-2 embeddings
        embeddings = self.esm_wrapper.extract_embeddings_for_openfold(sequences)
        
        # Project to MSA space
        msa_embeddings = self.plm_projector(embeddings)
        
        self.inference_times[0] = time.time() - start_time
        
        return msa_embeddings
    
    def run_evoformer(
        self, 
        msa_embeddings: torch.Tensor,
        pair_embeddings: Optional[torch.Tensor] = None
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Run slim EvoFormer."""
        
        start_time = time.time()
        
        batch_size, seq_len = msa_embeddings.shape[:2]
        
        # Create pair embeddings if not provided
        if pair_embeddings is None:
            pair_embeddings = torch.randn(
                batch_size, seq_len, seq_len, 128,
                device=msa_embeddings.device
            )
        
        # Run EvoFormer
        msa_out, pair_out, single_out = self.evoformer(msa_embeddings, pair_embeddings)
        
        self.inference_times[1] = time.time() - start_time
        
        return msa_out, pair_out, single_out
    
    def predict_structure(self, single_repr: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Predict initial structure from single representation."""
        
        start_time = time.time()
        
        # Structure prediction
        structure_output = self.structure_module(single_repr)
        
        self.inference_times[2] = time.time() - start_time
        
        return structure_output
    
    def refine_structure(
        self, 
        coordinates: torch.Tensor,
        single_repr: torch.Tensor,
        mask: Optional[torch.Tensor] = None
    ) -> torch.Tensor:
        """Refine structure using diffusion refiner."""
        
        if not self.config.refiner_enabled or self.refiner is None:
            return coordinates
        
        start_time = time.time()
        
        # Diffusion refinement
        refined_coordinates = self.refiner(coordinates, single_repr, mask)
        
        refiner_time = time.time() - start_time
        self.inference_times[3] = refiner_time
        
        # Check latency constraint
        if refiner_time * 1000 > self.config.target_latency_ms:
            logging.warning(f"Refiner latency {refiner_time*1000:.1f}ms exceeds target {self.config.target_latency_ms:.1f}ms")
        
        return refined_coordinates
    
    def forward(
        self,
        sequences: List[str],
        return_intermediate: bool = False
    ) -> Dict[str, torch.Tensor]:
        """
        Complete forward pass through OpenFold++.
        
        Args:
            sequences: List of protein sequences
            return_intermediate: Whether to return intermediate results
            
        Returns:
            Dictionary with final coordinates and optional intermediates
        """
        
        total_start_time = time.time()
        
        # 1. Extract PLM embeddings (Phase A)
        msa_embeddings = self.extract_plm_embeddings(sequences)
        
        # 2. Run EvoFormer (Phase B)
        msa_out, pair_out, single_out = self.run_evoformer(msa_embeddings)
        
        # 3. Predict initial structure
        structure_output = self.predict_structure(single_out)
        initial_coordinates = structure_output['coordinates']
        confidence = structure_output['confidence']
        
        # 4. Refine structure (Phase D)
        final_coordinates = self.refine_structure(
            initial_coordinates, single_out
        )
        
        # Total time
        total_time = time.time() - total_start_time
        self.inference_times[4] = total_time
        
        # Prepare output
        output = {
            'coordinates': final_coordinates,
            'confidence': confidence,
            'inference_time': total_time
        }
        
        if return_intermediate:
            output.update({
                'initial_coordinates': initial_coordinates,
                'msa_embeddings': msa_embeddings,
                'single_repr': single_out,
                'pair_repr': pair_out,
                'component_times': {
                    'plm_extraction': self.inference_times[0].item(),
                    'evoformer': self.inference_times[1].item(),
                    'structure_prediction': self.inference_times[2].item(),
                    'refinement': self.inference_times[3].item(),
                    'total': self.inference_times[4].item()
                }
            })
        
        return output
    
    def get_performance_stats(self) -> Dict[str, float]:
        """Get performance statistics."""
        
        return {
            'plm_time_ms': self.inference_times[0].item() * 1000,
            'evoformer_time_ms': self.inference_times[1].item() * 1000,
            'structure_time_ms': self.inference_times[2].item() * 1000,
            'refinement_time_ms': self.inference_times[3].item() * 1000,
            'total_time_ms': self.inference_times[4].item() * 1000,
            'refinement_overhead_percent': (self.inference_times[3] / self.inference_times[4] * 100).item(),
            'meets_latency_target': self.inference_times[3].item() * 1000 <= self.config.target_latency_ms
        }
    
    def count_parameters(self) -> Dict[str, int]:
        """Count parameters in each component."""
        
        def count_params(module):
            return sum(p.numel() for p in module.parameters() if p.requires_grad)
        
        return {
            'plm_projector': count_params(self.plm_projector),
            'evoformer': count_params(self.evoformer),
            'structure_module': count_params(self.structure_module),
            'refiner': count_params(self.refiner) if self.refiner else 0,
            'total': count_params(self)
        }


# Factory function
def create_openfold_plus_plus(config: OpenFoldPlusPlusConfig = None) -> OpenFoldPlusPlus:
    """
    Factory function to create complete OpenFold++ model.
    
    Args:
        config: Optional configuration
        
    Returns:
        OpenFoldPlusPlus model
    """
    return OpenFoldPlusPlus(config)


# Example usage and testing
if __name__ == "__main__":
    # Test complete pipeline
    config = OpenFoldPlusPlusConfig(
        refiner_enabled=True,
        refiner_timesteps=10  # Reduced for testing
    )
    
    model = create_openfold_plus_plus(config)
    model.eval()
    
    # Test sequences
    test_sequences = [
        "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLAGG",
        "MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHV"
    ]
    
    print("🚀 Testing OpenFold++ Complete Pipeline")
    print("=" * 50)
    
    # Run inference
    with torch.no_grad():
        results = model(test_sequences, return_intermediate=True)
    
    # Print results
    print(f"✅ Pipeline test successful!")
    print(f"   Input sequences: {len(test_sequences)}")
    print(f"   Output coordinates: {results['coordinates'].shape}")
    print(f"   Confidence scores: {results['confidence'].shape}")
    print(f"   Total inference time: {results['inference_time']:.3f}s")
    
    # Performance stats
    perf_stats = model.get_performance_stats()
    print(f"\n📊 Performance Breakdown:")
    print(f"   PLM extraction: {perf_stats['plm_time_ms']:.1f}ms")
    print(f"   EvoFormer: {perf_stats['evoformer_time_ms']:.1f}ms")
    print(f"   Structure prediction: {perf_stats['structure_time_ms']:.1f}ms")
    print(f"   Refinement: {perf_stats['refinement_time_ms']:.1f}ms")
    print(f"   Refinement overhead: {perf_stats['refinement_overhead_percent']:.1f}%")
    print(f"   Meets latency target: {'✅' if perf_stats['meets_latency_target'] else '❌'}")
    
    # Parameter counts
    param_counts = model.count_parameters()
    print(f"\n📦 Parameter Counts:")
    for component, count in param_counts.items():
        print(f"   {component}: {count:,}")
    
    print(f"\n🎯 Phase D Integration Complete!")
    print(f"   Refiner adds {perf_stats['refinement_time_ms']:.1f}ms latency")
    print(f"   Target: <{config.target_latency_ms:.0f}ms")
    print(f"   Result: {'✅ PASS' if perf_stats['meets_latency_target'] else '❌ FAIL'}")
