import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from einops.layers.torch import Rearrange, Reduce

class PatchEmbedding(nn.Module):
    def __init__(self, emb_size=40, patch_size=25, in_channels=1):
        super().__init__()
        self.projection = nn.Sequential(
            nn.Conv2d(in_channels, emb_size, kernel_size=(1, patch_size), stride=(1, patch_size)),
            Rearrange('b e (h) (w) -> b (h w) e'),
        )
        self.cls_token = nn.Parameter(torch.randn(1, 1, emb_size))
        self.positions = nn.Parameter(torch.randn(2000, emb_size)) 

    def forward(self, x):
        b, _, _, _ = x.shape
        x = self.projection(x)
        cls_tokens = self.cls_token.expand(b, -1, -1)
        x = torch.cat([cls_tokens, x], dim=1)
        x += self.positions[:x.shape[1], :]
        return x

class MultiHeadAttention(nn.Module):
    def __init__(self, emb_size, num_heads, dropout):
        super().__init__()
        self.emb_size = emb_size
        self.num_heads = num_heads
        self.keys = nn.Linear(emb_size, emb_size)
        self.queries = nn.Linear(emb_size, emb_size)
        self.values = nn.Linear(emb_size, emb_size)
        self.att_drop = nn.Dropout(dropout)
        self.projection = nn.Linear(emb_size, emb_size)

    def forward(self, x, mask=None):
        queries = rearrange(self.queries(x), 'b n (h d) -> b h n d', h=self.num_heads)
        keys = rearrange(self.keys(x), 'b n (h d) -> b h n d', h=self.num_heads)
        values = rearrange(self.values(x), 'b n (h d) -> b h n d', h=self.num_heads)
        energy = torch.einsum('bhqd, bhkd -> bhqk', queries, keys)  
        scaling = self.emb_size ** (1 / 2)
        att = F.softmax(energy / scaling, dim=-1)
        att = self.att_drop(att)
        out = torch.einsum('bhal, bhlv -> bhav ', att, values)
        out = rearrange(out, 'b h n d -> b n (h d)')
        out = self.projection(out)
        return out

class ResidualAdd(nn.Module):
    def __init__(self, fn):
        super().__init__()
        self.fn = fn
    def forward(self, x, **kwargs):
        res = x
        x = self.fn(x, **kwargs)
        x += res
        return x

class FeedForwardBlock(nn.Sequential):
    def __init__(self, emb_size, expansion, dropout):
        super().__init__(
            nn.Linear(emb_size, expansion * emb_size),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(expansion * emb_size, emb_size),
        )

class TransformerEncoderBlock(nn.Sequential):
    def __init__(self, emb_size, num_heads=4, drop_p=0.5, forward_expansion=4, forward_drop_p=0.5):
        super().__init__(
            ResidualAdd(nn.Sequential(
                nn.LayerNorm(emb_size),
                MultiHeadAttention(emb_size, num_heads, drop_p),
                nn.Dropout(drop_p)
            )),
            ResidualAdd(nn.Sequential(
                nn.LayerNorm(emb_size),
                FeedForwardBlock(emb_size, expansion=forward_expansion, dropout=forward_drop_p),
                nn.Dropout(drop_p)
            ))
        )

class TransformerEncoder(nn.Sequential):
    def __init__(self, depth, emb_size):
        super().__init__(*[TransformerEncoderBlock(emb_size) for _ in range(depth)])

class ClassificationHead(nn.Sequential):
    def __init__(self, emb_size, n_classes):
        super().__init__()
        self.clshead = nn.Sequential(
            Reduce('b n e -> b e', reduction='mean'),
            nn.LayerNorm(emb_size),
            nn.Linear(emb_size, n_classes)
        )
    def forward(self, x):
        return self.clshead(x)

class EEGConformer(nn.Module):
    def __init__(self, n_classes=3, channels=64, time_points=1000):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 40, (1, 25), (1, 1))
        # Ensure kernel size for dim 2 doesn't exceed channels
        k2 = channels if channels < 40 else 40 
        self.conv2 = nn.Conv2d(40, 40, (channels, 1), (1, 1))
        self.batchnorm1 = nn.BatchNorm2d(40)
        self.avgpool1 = nn.AvgPool2d((1, 75), (1, 15))
        self.flatten = nn.Flatten()
        
        # Calculate embedding dim linearly or use LazyLinear
        self.projection = nn.LazyLinear(40)
        
        self.transformer = TransformerEncoder(depth=4, emb_size=40)
        self.classification = ClassificationHead(emb_size=40, n_classes=n_classes)

    def forward(self, x):
        if x.dim() == 3:
            x = x.unsqueeze(1)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.batchnorm1(x)
        x = F.elu(x)
        x = self.avgpool1(x)
        x = F.dropout(x, 0.5)
        x = rearrange(x, 'b e h w -> b (h w) e')
        x = self.projection(x)
        x = self.transformer(x)
        x = self.classification(x)
        return x