import torch
import torch.nn as nn
import math
from timm.models.layers import Mlp, trunc_normal_
from medlat.modules.in_and_out import PatchEmbed, ToPixel
from medlat.modules.pos_embed import to_ntuple

import einops
import torch.utils.checkpoint

if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
    ATTENTION_MODE = 'flash'
else:
    try:
        import xformers
        import xformers.ops
        ATTENTION_MODE = 'xformers'
    except:
        ATTENTION_MODE = 'math'
print(f'attention mode is {ATTENTION_MODE}')


__all__ = ['UViT', 'U_ViT_Small', 'U_ViT_Small_Deep', 'U_ViT_Mid', 'U_ViT_Large', 'U_ViT_Huge']


def timestep_embedding(timesteps, dim, max_period=10000):
    """
    Create sinusoidal timestep embeddings.

    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    half = dim // 2
    freqs = torch.exp(
        -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
    ).to(device=timesteps.device)
    args = timesteps[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    return embedding


def patchify(imgs, patch_size):
    x = einops.rearrange(imgs, 'B C (h p1) (w p2) -> B (h w) (p1 p2 C)', p1=patch_size, p2=patch_size)
    return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x):
        B, L, C = x.shape

        qkv = self.qkv(x)
        if ATTENTION_MODE == 'flash':
            qkv = einops.rearrange(qkv, 'B L (K H D) -> K B H L D', K=3, H=self.num_heads).float()
            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
            x = einops.rearrange(x, 'B H L D -> B L (H D)')
        elif ATTENTION_MODE == 'xformers':
            qkv = einops.rearrange(qkv, 'B L (K H D) -> K B L H D', K=3, H=self.num_heads)
            q, k, v = qkv[0], qkv[1], qkv[2]  # B L H D
            x = xformers.ops.memory_efficient_attention(q, k, v)
            x = einops.rearrange(x, 'B L H D -> B L (H D)', H=self.num_heads)
        elif ATTENTION_MODE == 'math':
            qkv = einops.rearrange(qkv, 'B L (K H D) -> K B H L D', K=3, H=self.num_heads)
            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
            attn = (q @ k.transpose(-2, -1)) * self.scale
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            x = (attn @ v).transpose(1, 2).reshape(B, L, C)
        else:
            raise NotImplemented

        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None,
                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, skip=False, use_checkpoint=False):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer)
        self.skip_linear = nn.Linear(2 * dim, dim) if skip else None
        self.use_checkpoint = use_checkpoint

    def forward(self, x, skip=None):
        if self.use_checkpoint:
            return torch.utils.checkpoint.checkpoint(self._forward, x, skip)
        else:
            return self._forward(x, skip)

    def _forward(self, x, skip=None):
        if self.skip_linear is not None:
            x = self.skip_linear(torch.cat([x, skip], dim=-1))
        x = x + self.attn(self.norm1(x))
        x = x + self.mlp(self.norm2(x))
        return x


class UViT(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.,
                 qkv_bias=False, qk_scale=None, norm_layer=nn.LayerNorm, mlp_time_embed=False, num_classes=-1,
                 use_checkpoint=False, conv=True, skip=True, dims=2):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_classes = num_classes
        self.in_chans = in_chans
        self.dims = dims

        img_size   = to_ntuple(img_size,   dims)
        patch_size = to_ntuple(patch_size, dims)
        self.patch_embed = PatchEmbed(to_embed='conv', img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.time_embed = nn.Sequential(
            nn.Linear(embed_dim, 4 * embed_dim),
            nn.SiLU(),
            nn.Linear(4 * embed_dim, embed_dim),
        ) if mlp_time_embed else nn.Identity()

        if self.num_classes > 0:
            self.label_emb = nn.Embedding(self.num_classes, embed_dim)
            self.extras = 2
        else:
            self.extras = 1

        self.pos_embed = nn.Parameter(torch.zeros(1, self.extras + num_patches, embed_dim))

        self.in_blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                norm_layer=norm_layer, use_checkpoint=use_checkpoint)
            for _ in range(depth // 2)])

        self.mid_block = Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                norm_layer=norm_layer, use_checkpoint=use_checkpoint)

        self.out_blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                norm_layer=norm_layer, skip=skip, use_checkpoint=use_checkpoint)
            for _ in range(depth // 2)])

        self.norm = norm_layer(embed_dim)
        self.decoder_pred = ToPixel('conv' if conv else 'linear', img_size=img_size, out_channels=in_chans, in_dim=embed_dim, patch_size=patch_size)

        trunc_normal_(self.pos_embed, std=.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed'}

    def forward(self, x, timesteps, y=None):
        x = self.patch_embed(x)
        B, L, D = x.shape

        time_token = self.time_embed(timestep_embedding(timesteps, self.embed_dim))
        time_token = time_token.unsqueeze(dim=1)
        x = torch.cat((time_token, x), dim=1)
        if y is not None:
            label_emb = self.label_emb(y)
            label_emb = label_emb.unsqueeze(dim=1)
            x = torch.cat((label_emb, x), dim=1)
        x = x + self.pos_embed

        skips = []
        for blk in self.in_blocks:
            x = blk(x)
            skips.append(x)

        x = self.mid_block(x)

        for blk in self.out_blocks:
            x = blk(x, skips.pop())

        x = self.norm(x)
        x = x[:, self.extras:, :]
        x = self.decoder_pred(x)
        return x
    


def U_ViT_Small(**kwargs):
    return UViT(depth=13, embed_dim=512, mlp_ratio=4, num_heads=8, **kwargs)

def U_ViT_Small_Deep(**kwargs):
    return UViT(depth=17, embed_dim=512, mlp_ratio=4, num_heads=8, **kwargs)

def U_ViT_Mid(**kwargs):
    return UViT(depth=17, embed_dim=768, mlp_ratio=4, num_heads=12, **kwargs)

def U_ViT_Large(**kwargs):
    return UViT(depth=21, embed_dim=1024, mlp_ratio=4, num_heads=16, **kwargs)

def U_ViT_Huge(**kwargs):
    return UViT(depth=29, embed_dim=1152, mlp_ratio=4, num_heads=16, **kwargs)


