# Copyright (c) Facebook, Inc. and its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Mostly copy-paste from timm library.
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import math
from functools import partial
import numpy as np

import torch
import torch.nn as nn

from src.utils import trunc_normal_


def drop_path(x, drop_prob: float = 0., training: bool = False):
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """
    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 MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=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 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, 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, attn


class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 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)

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


class PatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        num_patches = (img_size // patch_size) * (img_size // patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class ConvEmbed(nn.Module):
    """
    3x3 Convolution stems for ViT following ViTC models
    """

    def __init__(self, channels, strides, img_size=224, in_chans=3, batch_norm=True):
        super().__init__()
        # Build the stems
        stem = []
        channels = [in_chans] + channels
        for i in range(len(channels) - 2):
            stem += [nn.Conv2d(channels[i], channels[i+1], kernel_size=3,
                               stride=strides[i], padding=1, bias=(not batch_norm))]
            if batch_norm:
                stem += [nn.BatchNorm2d(channels[i+1])]
            stem += [nn.ReLU(inplace=True)]
        stem += [nn.Conv2d(channels[-2], channels[-1], kernel_size=1, stride=strides[-1])]
        self.stem = nn.Sequential(*stem)

        # Comptute the number of patches
        stride_prod = int(np.prod(strides))
        self.num_patches = (img_size[0] // stride_prod)**2

    def forward(self, x):
        p = self.stem(x)
        return p.flatten(2).transpose(1, 2)


class VisionTransformer(nn.Module):
    """ Vision Transformer """
    def __init__(self, img_size=[224], patch_size=16, in_chans=3, num_classes=0, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
                 drop_path_rate=0., norm_layer=nn.LayerNorm,
                 conv_stem=False, conv_stem_channels=None, conv_stem_strides=None, **kwargs):
        super().__init__()
        self.num_features = self.embed_dim = embed_dim

        if conv_stem:
            self.patch_embed = ConvEmbed(conv_stem_channels, conv_stem_strides,
                                         in_chans=in_chans, img_size=img_size)
        else:
            self.patch_embed = PatchEmbed(
                img_size=img_size[0], patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        self.blocks = nn.ModuleList([
            Block(
                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)

        # Classifier head
        self.fc = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
        self.pred = None

        trunc_normal_(self.pos_embed, std=.02)
        trunc_normal_(self.cls_token, 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)
        elif isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
            if isinstance(m, nn.Conv2d) and m.bias is not None:
                nn.init.constant_(m.bias, 0)

    def forward(self, x, return_before_head=False, patch_drop=0.):
        if not isinstance(x, list):
            x = [x]
        idx_crops = torch.cumsum(torch.unique_consecutive(
            torch.tensor([inp.shape[-1] for inp in x]),
            return_counts=True,
        )[1], 0)
        start_idx = 0
        for end_idx in idx_crops:
            _h = self.forward_features(torch.cat(x[start_idx:end_idx]), patch_drop)
            _z = self.forward_head(_h)
            if start_idx == 0:
                h, z = _h, _z
            else:
                h, z = torch.cat((h, _h)), torch.cat((z, _z))
            patch_drop = 0.
            start_idx = end_idx

        if return_before_head:
            return h, z
        return z

    def forward_head(self, x):
        if self.pred is not None:
            return self.pred(x)
        return x

    def forward_features(self, x, patch_drop):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
        x = x + pos_embed
        x = self.pos_drop(x)

        if patch_drop > 0:
            patch_keep = 1. - patch_drop
            T_H = int(np.floor((x.shape[1]-1)*patch_keep))
            perm = 1 + torch.randperm(x.shape[1]-1)[:T_H]  # keep class token
            idx = torch.cat([torch.zeros(1, dtype=perm.dtype, device=perm.device), perm])
            x = x[:, idx, :]

        for blk in self.blocks:
            x = blk(x)
        if self.norm is not None:
            x = self.norm(x)
        x = x[:, 0]
        if self.fc is not None:
            x = self.fc(x)
        return x

    def forward_blocks(self, x, num_blocks=1, patch_drop=0.):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
        x = x + pos_embed
        x = self.pos_drop(x)

        if patch_drop > 0:
            patch_keep = 1. - patch_drop
            T_H = int(np.floor((x.shape[1]-1)*patch_keep))
            perm = 1 + torch.randperm(x.shape[1]-1)[:T_H]  # keep class token
            idx = torch.cat([torch.zeros(1, dtype=perm.dtype, device=perm.device), perm])
            x = x[:, idx, :]

        cls_x = []
        for i in range(len(self.blocks)):
            x = self.blocks[i](x)
            if (len(self.blocks) - i) <= num_blocks:
                cls_x.append(x[:, 0])

        return torch.cat(cls_x, dim=-1)

    def interpolate_pos_encoding(self, x, pos_embed):
        npatch = x.shape[1] - 1
        N = pos_embed.shape[1] - 1
        if npatch == N:
            return pos_embed
        class_emb = pos_embed[:, 0]
        pos_embed = pos_embed[:, 1:]
        dim = x.shape[-1]
        pos_embed = nn.functional.interpolate(
            pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
            scale_factor=math.sqrt(npatch / N),
            mode='bicubic',
        )
        pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)

    def forward_selfattention(self, x):
        B, nc, w, h = x.shape
        N = self.pos_embed.shape[1] - 1
        x = self.patch_embed(x)

        # interpolate patch embeddings
        dim = x.shape[-1]
        w0 = w // self.patch_embed.patch_size
        h0 = h // self.patch_embed.patch_size
        class_pos_embed = self.pos_embed[:, 0]
        patch_pos_embed = self.pos_embed[:, 1:]
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
            mode='bicubic',
        )
        # sometimes there is a floating point error in the interpolation and so
        # we need to pad the patch positional encoding.
        if w0 != patch_pos_embed.shape[-2]:
            helper = torch.zeros(h0)[None, None, None, :].repeat(1, dim, w0 - patch_pos_embed.shape[-2], 1).to(x.device)
            patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-2)
        if h0 != patch_pos_embed.shape[-1]:
            helper = torch.zeros(w0)[None, None, :, None].repeat(1, dim, 1, h0 - patch_pos_embed.shape[-1]).to(x.device)
            patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-1)

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        pos_embed = torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        x = x + pos_embed
        x = self.pos_drop(x)

        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x)
            else:
                return blk(x, return_attention=True)

    def forward_return_n_last_blocks(self, x, n=1, return_patch_avgpool=False):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)
        pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
        x = x + pos_embed
        x = self.pos_drop(x)

        # we will return the [CLS] tokens from the `n` last blocks
        output = []
        for i, blk in enumerate(self.blocks):
            x = blk(x)
            if len(self.blocks) - i <= n:
                output.append(self.norm(x)[:, 0])
        if return_patch_avgpool:
            x = self.norm(x)
            # In addition to the [CLS] tokens from the `n` last blocks, we also return
            # the patch tokens from the last block. This is useful for linear eval.
            output.append(torch.mean(x[:, 1:], dim=1))
        return torch.cat(output, dim=-1)


def deit_tiny(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_small(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_small_p8(patch_size=8, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_small_p7(patch_size=7, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def vitc_4gf(patch_size=16, **kwargs):
    channels = [48, 96, 192, 384, 384]
    strides = [2, 2, 2, 2, 1]
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=384, depth=11, num_heads=6, mlp_ratio=3,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
        conv_stem=True, conv_stem_channels=channels, conv_stem_strides=strides,
        **kwargs)
    return model


def deit_small_convstem(patch_size=16, **kwargs):
    channels = [48, 96, 192, 384, 384]
    strides = [2, 2, 2, 2, 1]
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=384, depth=11, num_heads=6, mlp_ratio=6,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
        conv_stem=True, conv_stem_channels=channels, conv_stem_strides=strides,
        **kwargs)
    return model


def deit_base_p8(patch_size=8, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_base_p7(patch_size=7, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_base_p4(patch_size=4, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_base(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_large_p7(patch_size=7, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_large_p8(patch_size=8, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_large(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_huge(patch_size=16, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_huge_p8(patch_size=8, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_huge_p7(patch_size=7, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


def deit_huge_p10(patch_size=10, **kwargs):
    model = VisionTransformer(
        patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
        qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model
