import torch
import torch.nn as nn
import esm
import numpy as np
import torch.nn.functional as F
import pdb

vhse8_values = {
    'A': [0.15, -1.11, -1.35, -0.92, 0.02, -0.91, 0.36, -0.48],
    'R': [-1.47, 1.45, 1.24, 1.27, 1.55, 1.47, 1.30, 0.83],
    'N': [-0.99, 0.00, 0.69, -0.37, -0.55, 0.85, 0.73, -0.80],
    'D': [-1.15, 0.67, -0.41, -0.01, -2.68, 1.31, 0.03, 0.56],
    'C': [0.18, -1.67, -0.21, 0.00, 1.20, -1.61, -0.19, -0.41],
    'Q': [-0.96, 0.12, 0.18, 0.16, 0.09, 0.42, -0.20, -0.41],
    'E': [-1.18, 0.40, 0.10, 0.36, -2.16, -0.17, 0.91, 0.36],
    'G': [-0.20, -1.53, -2.63, 2.28, -0.53, -1.18, -1.34, 1.10],
    'H': [-0.43, -0.25, 0.37, 0.19, 0.51, 1.28, 0.93, 0.65],
    'I': [1.27, 0.14, 0.30, -1.80, 0.30, -1.61, -0.16, -0.13],
    'L': [1.36, 0.07, 0.26, -0.80, 0.22, -1.37, 0.08, -0.62],
    'K': [-1.17, 0.70, 0.80, 1.64, 0.67, 1.63, 0.13, -0.01],
    'M': [1.01, -0.53, 0.43, 0.00, 0.23, 0.10, -0.86, -0.68],
    'F': [1.52, 0.61, 0.95, -0.16, 0.25, 0.28, -1.33, -0.65],
    'P': [0.22, -0.17, -0.50, -0.05, 0.01, -1.34, 0.19, 3.56],
    'S': [-0.67, -0.86, -1.07, -0.41, -0.32, 0.27, -0.64, 0.11],
    'T': [-0.34, -0.51, -0.55, -1.06, 0.01, -0.01, -0.79, 0.39],
    'W': [1.50, 2.06, 1.79, 0.75, 0.75, 0.13, -1.06, -0.85],
    'Y': [0.61, 1.60, 1.17, 0.73, 0.53, 0.25, -0.96, -0.52],
    'V': [0.76, -0.92, 0.17, -1.91, 0.22, -1.40, -0.24, -0.03],
}

aa_to_idx = {'A': 5, 'R': 10, 'N': 17, 'D': 13, 'C': 23, 'Q': 16, 'E': 9, 'G': 6, 'H': 21, 'I': 12, 'L': 4, 'K': 15, 'M': 20, 'F': 18, 'P': 14, 'S': 8, 'T': 11, 'W': 22, 'Y': 19, 'V': 7}

vhse8_tensor = torch.zeros(33, 8)
for aa, values in vhse8_values.items():
    aa_index = aa_to_idx[aa]
    vhse8_tensor[aa_index] = torch.tensor(values)

vhse8_tensor.requires_grad = False

class muPPIt(torch.nn.Module):
    def __init__(self, d_node, d_k, d_v, n_heads, lr):
        super(muPPIt, self).__init__()

        self.esm, self.alphabet = esm.pretrained.esm2_t33_650M_UR50D()
        for param in self.esm.parameters():
            param.requires_grad = False

        self.q = nn.Linear(d_node, n_heads * d_k)
        self.k = nn.Linear(d_node, n_heads * d_k)
        self.v = nn.Linear(d_node, n_heads * d_v)
        self.layer_norm = torch.nn.LayerNorm(n_heads * d_v)

        self.map = torch.nn.Sequential(
            torch.nn.Linear(n_heads * d_v, (n_heads * d_v) // 2), 
            torch.nn.SiLU(),
            torch.nn.Linear((n_heads * d_v) // 2, (n_heads * d_v) // 4),
            torch.nn.SiLU(),
            torch.nn.Linear((n_heads * d_v) // 4, 2)
        )

        self.learning_rate = lr
        self.n_heads = n_heads
        self.d_k = d_k
        self.d_v = d_v
        self.d_node = d_node

    def forward(self, binder_tokens, wt_embed, mut_embed):
        device = binder_tokens.device
        global vhse8_tensor
        vhse8_tensor = vhse8_tensor.to(device)

        with torch.no_grad():
            binder_pad_mask = (binder_tokens != self.alphabet.padding_idx).int()
            binder_embed = self.esm(binder_tokens, repr_layers=[33], return_contacts=False)["representations"][33] * binder_pad_mask.unsqueeze(-1)
            binder_vhse8 = vhse8_tensor[binder_tokens]
            binder_embed = torch.concat([binder_embed, binder_vhse8], dim=-1)

        binder_wt_reciprocal = self.cross_attention(binder_embed, wt_embed)
        binder_mut_reciprocal = self.cross_attention(binder_embed, mut_embed)

        binder_wt_reciprocal = self.layer_norm(binder_wt_reciprocal).mean(dim=1)
        binder_mut_reciprocal = self.layer_norm(binder_mut_reciprocal).mean(dim=1)

        difference = binder_wt_reciprocal - binder_mut_reciprocal # (B, d_node)

        logits = self.map(difference) # (B, 2)

        return logits

    def cross_attention(self, embed_1, embed_2):
        B, L1, _ = embed_1.shape  
        _, L2, _ = embed_2.shape  

        Q = self.q(embed_1).view(B, L1, self.n_heads, self.d_k)  # (B, L1, n_heads, d_k)
        K = self.k(embed_2).view(B, L2, self.n_heads, self.d_k)  # (B, L2, n_heads, d_k)
        V = self.v(embed_2).view(B, L2, self.n_heads, self.d_v)  # (B, L2, n_heads, d_v)

        Q = Q.transpose(1, 2)  # (B, n_heads, L1, d_k)
        K = K.transpose(1, 2)  # (B, n_heads, L2, d_k)
        V = V.transpose(1, 2)  # (B, n_heads, L2, d_v)

        attention_scores = torch.matmul(Q, K.transpose(-2, -1)) / (self.d_k ** 0.5)  # (B, n_heads, L1, L2)
        attention_weights = F.softmax(attention_scores, dim=-1)  # (B, n_heads, L1, L2)

        output = torch.matmul(attention_weights, V)  # (B, n_heads, L1, d_v)

        output = output.transpose(1, 2).contiguous().view(B, L1, self.n_heads * self.d_v)  # (B, L1, n_heads * d_v)

        return output

    def get_log_probs(self, binder_tokens, wt_embed, mut_embed):
        logits = self.forward(binder_tokens, wt_embed, mut_embed)
        log_prob = torch.log(torch.softmax(logits, dim=-1)) # (B, 2)

        return log_prob