
import torch
from torch import nn




# helpers

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.dim=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)
        q, k, v= map(lambda t:t.view(t.size(0),t.size(1),self.heads,-1).permute(0,2,1,3),qkv)#rearrange(t, 'b n (h d) -> b h n d', h=self.heads)
        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale

        attn = self.attend(dots)

        out = torch.matmul(attn, v)
        out = out.view(x.size(0),x.size(1),-1)#rearrange(out, 'b h n d -> b n (h d)')
        return self.to_out(out)


class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        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:
            x = attn(x) + x
            x = ff(x) + x
        return x


class ViT(nn.Module):
    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool='mean', channels=3,
                 dim_head=64, dropout=0., emb_dropout=0.):
        super().__init__()
        image_height, image_width = pair(image_size)
        patch_height, patch_width = pair(patch_size)
        self.patch_height = patch_height
        self.patch_width = patch_width

        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'

        num_patches = (image_height // patch_height) * (image_width // patch_width)
        patch_dim = channels * patch_height * patch_width
        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'

        self.to_patch_embedding = nn.Sequential(
            # Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),
            nn.Linear(patch_dim, dim),
        )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, 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.ln=nn.LayerNorm(dim)
        self.softmax=nn.Softmax(-1)
        self.relu = nn.ReLU()
        # self.to_latent = nn.Identity()

        # self.mlp_head = nn.Sequential(
        #     nn.LayerNorm(dim),
        #     nn.Linear(dim, num_classes)
        # )

    def forward(self, img):
        img = img.view(img.size(0), int((img.size(2)/self.patch_height)*(img.size(3)/self.patch_width)), -1)
        x = self.to_patch_embedding(img)
        b, n, _ = x.shape

        # cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
        # self.cls_token.repeat(b,n,self.cls_token.size(2))
        # x = torch.cat((self.cls_tokens, x), dim=1)
        x += self.pos_embedding
        x = self.dropout(x)

        x = self.transformer(x)
        x=self.relu(self.ln(x))#torch.Size([272, 49, 256])
        x=x.view(img.size(0),7,7,-1).permute(0,3,1,2)## Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=patch_height, p2=patch_width),

        # x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
        #
        # x = self.to_latent(x)#x没变化？
        # return self.mlp_head(x)
        return x
