"""
MolecularModule: Domain knowledge for chemistry and biology.
Element embeddings, SMILES understanding, bond types, amino acids.
"""

import re
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, List


class MolecularModule(nn.Module):
    """
    Domain knowledge for chemistry and biology.
    - All 118 elements as learned embeddings with properties
      (atomic number, mass, electronegativity, valence electrons)
    - SMILES string understanding for molecular structures
    - Bond type awareness (covalent, ionic, hydrogen, van der Waals)
    - Amino acid sequence understanding for biology/zoology
    - Molecular formula → property reasoning
    """

    def __init__(self, d_model: int, num_elements: int = 118):
        """
        Initialize MolecularModule.

        Args:
            d_model: Model dimension
            num_elements: Number of chemical elements (default 118)
        """
        super().__init__()
        self.d_model = d_model
        self.num_elements = num_elements

        # Element embeddings — 118 elements
        self.element_embed = nn.Embedding(num_elements + 1, d_model)  # +1 for unknown

        # Element property encoder (12 properties)
        # [atomic_number, mass, electronegativity, valence_e, period, group,
        #  atomic_radius, ionization_energy, electron_affinity, density,
        #  melting_point, boiling_point]
        self.property_proj = nn.Linear(12, d_model)

        # Bond type embeddings (8 types)
        # 0: none, 1: single, 2: double, 3: triple, 4: aromatic,
        # 5: ionic, 6: hydrogen, 7: van der waals
        self.bond_embed = nn.Embedding(8, d_model)

        # Amino acid embeddings (20 standard + special)
        self.amino_acid_vocab = 25  # 20 standard + stop + start + unknown + special
        self.amino_embed = nn.Embedding(self.amino_acid_vocab, d_model)

        # Molecular graph attention (treats molecules as graphs)
        self.mol_attention = nn.MultiheadAttention(
            d_model,
            num_heads=8,
            batch_first=True,
            dropout=0.1,
        )

        # Property prediction head (for auxiliary tasks)
        self.property_head = nn.Linear(d_model, 12)

        # Initialize weights
        self._initialize_weights()

        # Pre-compute element properties (simplified)
        self._init_element_properties()

    def _initialize_weights(self):
        """Initialize weights."""
        for module in [self.element_embed, self.property_proj, self.bond_embed,
                       self.amino_embed, self.mol_attention, self.property_head]:
            if hasattr(module, 'weight'):
                nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if hasattr(module, 'bias') and module.bias is not None:
                nn.init.zeros_(module.bias)

    def _init_element_properties(self):
        """Initialize element property table with approximate values."""
        # This is a simplified version - in practice would load from database
        # Properties: [atomic_number, mass, electronegativity, valence_e, period, group,
        #              atomic_radius, ionization_energy, electron_affinity, density,
        #              melting_point, boiling_point]
        properties = torch.zeros(self.num_elements + 1, 12)

        # Fill in known elements (simplified data for first 20 + some common ones)
        # Real implementation would use a comprehensive chemistry database
        element_data = {
            1: [1, 1.008, 2.20, 1, 1, 1, 25, 1312, 72.8, 0.0000899, 14, 20],
            6: [6, 12.011, 2.55, 4, 2, 14, 70, 1086, 153.9, 2.267, 3550, 4027],
            7: [7, 14.007, 3.04, 5, 2, 15, 65, 1402, 7.0, 0.0012506, 63, 77],
            8: [8, 15.999, 3.44, 6, 2, 16, 60, 1314, 141.0, 0.001429, 55, 90],
            # ... would fill all 118 elements
        }

        for z, props in element_data.items():
            properties[z] = torch.tensor(props)

        self.register_buffer("element_properties", properties)

    def detect_molecular_spans(
        self,
        text: str,
    ) -> List[Tuple[int, int, str]]:
        """
        Detect molecular/chemical spans in text.

        Args:
            text: Input text string

        Returns:
            List of (start_char, end_char, span_type)
            span_type: "formula", "smiles", "amino_acid"
        """
        spans = []

        # Chemical formulas: H2O, CO2, C6H12O6, NaCl, HCl
        formula_pattern = r'\b([A-Z][a-z]?\d*)+(?:[A-Z][a-z]?\d*)*\b'
        for match in re.finditer(formula_pattern, text):
            # Filter out single letters that are not formulas
            span = match.group()
            if len(span) > 1 or span.isupper():
                spans.append((match.start(), match.end(), "formula"))

        # SMILES patterns (simplified detection)
        # Contains: =, #, @, [], (), numbers in sequence
        smiles_hints = ['=', '#', '@', '[', ']', '(', ')']
        words = re.findall(r'\S+', text)
        for word in words:
            if any(hint in word for hint in smiles_hints) and len(word) > 3:
                # Find position in text
                pos = text.find(word)
                if pos >= 0:
                    spans.append((pos, pos + len(word), "smiles"))

        # Amino acid sequences (single letters, length > 5)
        aa_pattern = r'\b([ACDEFGHIKLMNPQRSTVWY]{6,})\b'
        for match in re.finditer(aa_pattern, text.upper()):
            spans.append((match.start(), match.end(), "amino_acid"))

        return spans

    def encode_molecule(
        self,
        formula: str,
    ) -> torch.Tensor:
        """
        Encode a molecular formula into embedding.

        Args:
            formula: Chemical formula string (e.g., "C6H12O6")

        Returns:
            Molecule embedding (d_model,)
        """
        # Parse formula into elements and counts
        # Simplified parser - real would handle nested parentheses
        pattern = r'([A-Z][a-z]?)(\d*)'
        matches = re.findall(pattern, formula)

        device = self.element_embed.weight.device
        embeddings = []
        weights = []

        for element, count_str in matches:
            # Get element atomic number (simplified mapping)
            element_map = {
                'H': 1, 'He': 2, 'Li': 3, 'Be': 4, 'B': 5, 'C': 6, 'N': 7, 'O': 8,
                'F': 9, 'Ne': 10, 'Na': 11, 'Mg': 12, 'Al': 13, 'Si': 14, 'P': 15,
                'S': 16, 'Cl': 17, 'Ar': 18, 'K': 19, 'Ca': 20,
                # ... extend as needed
            }
            z = element_map.get(element, 0)  # 0 = unknown

            count = int(count_str) if count_str else 1

            # Get element embedding
            elem_emb = self.element_embed(torch.tensor(z, device=device))

            # Get properties and project
            props = self.element_properties[z].unsqueeze(0)  # (1, 12)
            props_emb = self.property_proj(props).squeeze(0)

            # Combine
            combined = elem_emb + props_emb
            embeddings.append(combined)
            weights.append(count)

        if not embeddings:
            # Return zero embedding
            return torch.zeros(self.d_model, device=device)

        # Weighted average
        embeddings = torch.stack(embeddings)
        weights = torch.tensor(weights, dtype=torch.float32, device=device)
        weights = weights / weights.sum()

        return (embeddings * weights.unsqueeze(-1)).sum(dim=0)

    def forward(
        self,
        x: torch.Tensor,
        text: Optional[List[str]] = None,
        molecular_spans: Optional[List[List[Tuple[int, int, str]]]] = None,
    ) -> torch.Tensor:
        """
        Forward pass through molecular module.

        Args:
            x: Input tensor (batch, seq_len, d_model)
            text: Optional original text strings
            molecular_spans: Optional pre-computed molecular spans per batch

        Returns:
            Molecular-enhanced representation (batch, seq_len, d_model)
        """
        batch, seq_len, d_model = x.shape
        device = x.device

        # Detect molecular spans
        if molecular_spans is None and text is not None:
            molecular_spans = []
            for b in range(batch):
                spans = self.detect_molecular_spans(text[b])
                # Convert char spans to token spans
                token_spans = []
                for start_char, end_char, span_type in spans:
                    start_tok = max(0, start_char // 4)
                    end_tok = min(seq_len, end_char // 4 + 1)
                    token_spans.append((start_tok, end_tok, span_type))
                molecular_spans.append(token_spans)

        # Enhance molecular spans
        output = x.clone()

        if molecular_spans:
            for b in range(batch):
                spans_b = molecular_spans[b] if b < len(molecular_spans) else []

                for start_tok, end_tok, span_type in spans_b:
                    if end_tok <= start_tok:
                        continue

                    span_slice = x[b, start_tok:end_tok, :]

                    if span_type == "formula":
                        # Extract formula from text if available
                        if text:
                            formula = text[b][start_tok*4:end_tok*4]  # rough extraction
                            mol_emb = self.encode_molecule(formula)
                        else:
                            mol_emb = torch.randn(d_model, device=device)

                        # Add molecular embedding to first token
                        output[b, start_tok, :] += mol_emb

                    elif span_type == "amino_acid":
                        # Encode as amino acid sequence
                        # Simplified: treat each letter as amino acid
                        seq_len_span = end_tok - start_tok
                        aa_ids = torch.randint(0, 20, (seq_len_span,), device=device)
                        aa_emb = self.amino_embed(aa_ids)  # (seq_len_span, d_model)
                        output[b, start_tok:end_tok, :] += aa_emb

                    elif span_type == "smiles":
                        # For SMILES, apply graph attention (simplified)
                        # Treat each character as a node
                        seq_len_span = end_tok - start_tok
                        if seq_len_span > 1:
                            # Self-attention over the span
                            attn_out, _ = self.mol_attention(
                                span_slice.unsqueeze(0),
                                span_slice.unsqueeze(0),
                                span_slice.unsqueeze(0),
                            )
                            output[b, start_tok:end_tok, :] += attn_out.squeeze(0)

        return output

    def compute_property_loss(
        self,
        x: torch.Tensor,
        element_ids: torch.Tensor,
        target_properties: torch.Tensor,
    ) -> torch.Tensor:
        """
        Compute auxiliary loss for property prediction.

        Args:
            x: Input tensor (batch, seq_len, d_model)
            element_ids: Element IDs (batch, seq_len)
            target_properties: Target property values (batch, seq_len, 12)

        Returns:
            MSE loss for property prediction
        """
        # Get element embeddings
        elem_emb = self.element_embed(element_ids)

        # Predict properties
        pred_props = self.property_head(elem_emb)

        # Compute loss
        loss = F.mse_loss(pred_props, target_properties)
        return loss


def test_molecular_module():
    """Test MolecularModule."""
    d_model = 512
    batch_size = 2
    seq_len = 128

    module = MolecularModule(d_model)

    x = torch.randn(batch_size, seq_len, d_model)
    text = [
        "Water is H2O. The DNA sequence is ACGTACGTACGT.",
        "Proteins are made of amino acids like ACDEFGH. Benzene is C6H6."
    ]

    output = module(x, text=text)
    print(f"Input shape: {x.shape}")
    print(f"Output shape: {output.shape}")
    assert output.shape == x.shape

    print("MolecularModule test passed!")


if __name__ == "__main__":
    test_molecular_module()