from collections import OrderedDict
from functools import partial

import torch
import torch.nn as nn


def drop_path(x, drop_prob: float = 0.0, training: bool = False):

    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class PatchEmbed(nn.Module):

    def __init__(
        self, img_size=224, patch_size=16, in_c=3, embed_dim=768, norm_layer=None
    ):
        super().__init__()
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(
            in_c, embed_dim, kernel_size=patch_size, stride=patch_size
        )
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        assert (
            H == self.img_size[0] and W == self.img_size[1]
        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."

        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x


class Attention(nn.Module):
    def __init__(
        self,
        dim,
        num_heads=8,
        qkv_bias=False,
        qk_scale=None,
        attn_drop_ratio=0.0,
        proj_drop_ratio=0.0,
    ):
        super(Attention, self).__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_ratio)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop_ratio)

    def forward(self, x):

        B, N, C = x.shape

        qkv = (
            self.qkv(x)
            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
            .permute(2, 0, 3, 1, 4)
        )

        q, k, v = qkv[0], qkv[1], qkv[2]

        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, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class Mlp(nn.Module):

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(nn.Module):
    def __init__(
        self,
        dim,
        num_heads,
        mlp_ratio=4.0,
        qkv_bias=False,
        qk_scale=None,
        drop_ratio=0.0,
        attn_drop_ratio=0.0,
        drop_path_ratio=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
    ):
        super(Block, self).__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop_ratio=attn_drop_ratio,
            proj_drop_ratio=drop_ratio,
        )

        self.drop_path = (
            DropPath(drop_path_ratio) if drop_path_ratio > 0.0 else nn.Identity()
        )
        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,
            drop=drop_ratio,
        )

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


class VisionTransformer(nn.Module):
    def __init__(
        self,
        img_size=224,
        patch_size=16,
        in_c=3,
        num_classes=1000,
        embed_dim=768,
        depth=12,
        num_heads=12,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        representation_size=None,
        distilled=False,
        drop_ratio=0.0,
        attn_drop_ratio=0.0,
        drop_path_ratio=0.0,
        embed_layer=PatchEmbed,
        norm_layer=None,
        act_layer=None,
    ):

        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(
            img_size=img_size, patch_size=patch_size, in_c=in_c, embed_dim=embed_dim
        )
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = (
            nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        )
        self.pos_embed = nn.Parameter(
            torch.zeros(1, num_patches + self.num_tokens, embed_dim)
        )
        self.pos_drop = nn.Dropout(p=drop_ratio)

        dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]
        self.blocks = nn.Sequential(
            *[
                Block(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop_ratio=drop_ratio,
                    attn_drop_ratio=attn_drop_ratio,
                    drop_path_ratio=dpr[i],
                    norm_layer=norm_layer,
                    act_layer=act_layer,
                )
                for i in range(depth)
            ]
        )
        self.norm = norm_layer(embed_dim)

        if representation_size and not distilled:
            self.has_logits = True
            self.num_features = representation_size
            self.pre_logits = nn.Sequential(
                OrderedDict(
                    [
                        ("fc", nn.Linear(embed_dim, representation_size)),
                        ("act", nn.Tanh()),
                    ]
                )
            )
        else:
            self.has_logits = False
            self.pre_logits = nn.Identity()

        self.head = (
            nn.Linear(self.num_features, num_classes)
            if num_classes > 0
            else nn.Identity()
        )
        self.head_dist = None
        if distilled:
            self.head_dist = (
                nn.Linear(self.embed_dim, self.num_classes)
                if num_classes > 0
                else nn.Identity()
            )

        # Weight init
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        if self.dist_token is not None:
            nn.init.trunc_normal_(self.dist_token, std=0.02)

        nn.init.trunc_normal_(self.cls_token, std=0.02)
        self.apply(_init_vit_weights)

    def forward_features(self, x):

        x = self.patch_embed(x)

        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        if self.dist_token is None:
            x = torch.cat((cls_token, x), dim=1)
        else:
            x = torch.cat(
                (cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1
            )

        x = self.pos_drop(x + self.pos_embed)
        x = self.blocks(x)
        x = self.norm(x)
        if self.dist_token is None:
            return self.pre_logits(x[:, 0])
        else:
            return x[:, 0], x[:, 1]

    def forward(self, x):
        x = self.forward_features(x)
        if self.head_dist is not None:
            x, x_dist = self.head(x[0]), self.head_dist(x[1])
            if self.training and not torch.jit.is_scripting():

                return x, x_dist
            else:
                return (x + x_dist) / 2
        else:
            x = self.head(x)
        return x


def _init_vit_weights(m):

    if isinstance(m, nn.Linear):
        nn.init.trunc_normal_(m.weight, std=0.01)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out")
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.LayerNorm):
        nn.init.zeros_(m.bias)
        nn.init.ones_(m.weight)


def vit_base_patch16_224(num_classes: int = 1000):

    model = VisionTransformer(
        img_size=224,
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        representation_size=None,
        num_classes=num_classes,
    )
    return model


def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):

    model = VisionTransformer(
        img_size=224,
        patch_size=16,
        embed_dim=768,
        depth=12,
        num_heads=12,
        representation_size=768 if has_logits else None,
        num_classes=num_classes,
    )
    return model


def vit_base_patch32_224(num_classes: int = 1000):

    model = VisionTransformer(
        img_size=224,
        patch_size=32,
        embed_dim=768,
        depth=12,
        num_heads=12,
        representation_size=None,
        num_classes=num_classes,
    )
    return model


def vit_base_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):

    model = VisionTransformer(
        img_size=224,
        patch_size=32,
        embed_dim=768,
        depth=12,
        num_heads=12,
        representation_size=768 if has_logits else None,
        num_classes=num_classes,
    )
    return model


def vit_large_patch16_224(num_classes: int = 1000):

    model = VisionTransformer(
        img_size=224,
        patch_size=16,
        embed_dim=1024,
        depth=24,
        num_heads=16,
        representation_size=None,
        num_classes=num_classes,
    )
    return model


def vit_large_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):

    model = VisionTransformer(
        img_size=224,
        patch_size=16,
        embed_dim=1024,
        depth=24,
        num_heads=16,
        representation_size=1024 if has_logits else None,
        num_classes=num_classes,
    )
    return model


def vit_large_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):

    model = VisionTransformer(
        img_size=224,
        patch_size=32,
        embed_dim=1024,
        depth=24,
        num_heads=16,
        representation_size=1024 if has_logits else None,
        num_classes=num_classes,
    )
    return model


def vit_huge_patch14_224_in21k(num_classes: int = 21843, has_logits: bool = True):

    model = VisionTransformer(
        img_size=224,
        patch_size=14,
        embed_dim=1280,
        depth=32,
        num_heads=16,
        representation_size=1280 if has_logits else None,
        num_classes=num_classes,
    )
    return model