from transformers import PreTrainedModel
import torch
import torch.nn as nn
from torch.nn import functional as F
from .configuration_medts import MedTSConfig


class FeedFoward(nn.Module):
    """ a simple linear layer followed by a non-linearity """
    def __init__(self, n_embd, dropout):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)


class Head(nn.Module):
    """ one head of self-attention """
    def __init__(self, head_size, n_embd, block_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
    def forward(self, x):
        # input of size (batch, time-step, channels)
        # output of size (batch, time-step, head size)
        B,T,C = x.shape
        k = self.key(x)   # (B,T,hs)
        q = self.query(x) # (B,T,hs)
        # compute attention scores ("affinities")
        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
        wei = F.softmax(wei, dim=-1) # (B, T, T)
        # perform the weighted aggregation of the values
        v = self.value(x) # (B,T,hs)
        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
        return out

class MultiHeadAttention(nn.Module):
    """ multiple heads of self-attention in parallel """
    def __init__(self, num_heads, head_size, n_embd, dropout, block_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size, n_embd, block_size) for _ in range(num_heads)])
        self.proj = nn.Linear(head_size * num_heads, n_embd)
        self.dropout = nn.Dropout(dropout)
    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class Block(nn.Module):
    """ Transformer block: communication followed by computation """
    def __init__(self, n_embd, n_head, dropout, block_size):
        # n_embd: embedding dimension, n_head: the number of heads we'd like
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size, n_embd, dropout, block_size)
        self.ffwd = FeedFoward(n_embd, dropout)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x


class PatientsTimeSeriesModel(nn.Module):
    def __init__(self, vocab_size, n_embd, block_size, device, n_layer, n_head, dropout):
        '''
        args:
        - vocab_size: int, the number of unique tokens in the vocabulary, i.e. the number of unique tests results
        - n_embd: int, the dimension of the embedding, i.e. the number of tests results (same as vocab_size)
        - block_size: int, the length of the context
        '''
        super().__init__()
        # each token directly reads off the logits for the next token from a lookup table
        self.position_embedding_table = nn.Embedding(block_size, vocab_size)
        # self.sa =Head(n_embd, n_embd, block_size)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head, dropout, block_size) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd) # final layer norm

        self.lm_prefix = nn.Linear(vocab_size, n_embd) # linear layer to project the tokens to the embedding dimension
        self.lm_head = nn.Linear(n_embd, vocab_size) # linear layer to project the embeddings to the vocabulary size
        self.device = device
        self.apply(self._init_weights)

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, tok_emb, targets=None):
        # tok_emb and targets are both (B,T,C) tensors
        # where B is the batch size, T is the number of time steps and C is the number of tests results
        B, T, C = tok_emb.shape
        pos_emb = self.position_embedding_table(torch.arange(T, device=self.device)) # (T,Vocab_size)
        x = tok_emb + pos_emb # (B,T,Vocab_size)
        x = self.lm_prefix(x) # (B,T,C)
        x = self.blocks(x) # (B,T,C)
        x = self.ln_f(x) # (B,T,C)
        logits = self.lm_head(x) # (B,T,vocab_size)
        if targets is None:
            return {"logits": logits}
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T, C)
            # TODO: Add padding mask to the loss computation
            # loss = F.mse_loss(logits, targets)
            loss = self.mse_loss(logits, targets, reduction="mean")
        return {"logits": logits, "loss": loss}

    def mse_loss(self, out, target, reduction):
        mask = (target == 0)
        loss = (out[~mask]-target[~mask])**2
        if reduction == "mean":
            return loss.mean()
        elif reduction == "None":
            return loss


class MedTSModel(PreTrainedModel):
    config_class = MedTSConfig

    def __init__(self, config):
        super().__init__(config)
        
        self.model = PatientsTimeSeriesModel(
            vocab_size=config.vocab_size,
            n_embd=config.n_embd,
            block_size=config.block_size,
            device= 'mps' if torch.backends.mps.is_available() else 'cuda' if torch.cuda.is_available() else 'cpu',
            n_layer=config.n_layer,
            n_head=config.n_head,
            dropout=config.dropout
        )

    def forward(self, tensor, targets=None):
        return self.model(tensor, targets)