"""
Adapted from https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit.py
"""

import torch
from einops import rearrange, repeat
from torch import nn


def pair(t):
    return t if isinstance(t, tuple) else (t, t)


# classes

class PreNorm(nn.Module):
    def __init__(self, dim, fn):
        super().__init__()
        self.norm = nn.LayerNorm(dim)
        self.fn = fn

    def forward(self, x, **kwargs):
        return self.fn(self.norm(x), **kwargs)


class FeedForward(nn.Module):
    def __init__(self, dim, hidden_dim, dropout=0.):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim, hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, dim),
            nn.Dropout(dropout)
        )

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


class Attention(nn.Module):
    def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        project_out = not (heads == 1 and dim_head == dim)

        self.heads = heads
        self.scale = dim_head ** -0.5

        self.attend = nn.Softmax(dim=-1)
        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, dim),
            nn.Dropout(dropout)
        ) if project_out else nn.Identity()

    def forward(self, x):
        qkv = self.to_qkv(x).chunk(3, dim=-1)
        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), qkv)

        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out), attn


class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        self.attn_wts = None
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout=dropout))
            ]))

    def forward(self, x):
        for attn, ff in self.layers:
            attn_out, self.attn_wts = attn(x)
            # x = attn(x) + x
            x = attn_out + x
            x = ff(x) + x
        return x


class AST(nn.Module):
    def __init__(self, *, num_inputs, dim, depth, heads, mlp_dim, pool='cls', channels=3,
                 dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()

        self.num_patches = num_inputs
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        self.pos_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, dim))
        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)

        self.pool = pool
        self.to_latent = nn.Identity()

    def forward(self, inputs):
        x = inputs
        b, n, _ = x.shape

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
        x = torch.cat((cls_tokens, x), dim=1)
        x += self.pos_embedding[:, :(n + 1)]
        x = self.dropout(x)

        x = self.transformer(x)

        x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]

        return self.to_latent(x)

    def get_attn_wts(self):
        return self.transformer.attn_wts
