# The implementation is  adopated from the CLIP4Clip implementation,
# made publicly available under Apache License, Version 2.0 at https://github.com/ArrowLuo/CLIP4Clip

import hashlib
import os
import urllib
import warnings
from collections import OrderedDict
from typing import Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from torch import nn
from tqdm import tqdm

_MODELS = {}
_PT_NAME = {'ViT-B/16': 'ViT-B-16.pt'}


def available_models():
    """Returns the names of available CLIP models"""
    return list(_MODELS.keys())


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super(Bottleneck, self).__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(
                OrderedDict([('-1', nn.AvgPool2d(stride)),
                             ('0',
                              nn.Conv2d(
                                  inplanes,
                                  planes * self.expansion,
                                  1,
                                  stride=1,
                                  bias=False)),
                             ('1', nn.BatchNorm2d(planes * self.expansion))]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu(self.bn1(self.conv1(x)))
        out = self.relu(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)
        return out


class AttentionPool2d(nn.Module):

    def __init__(self,
                 spacial_dim: int,
                 embed_dim: int,
                 num_heads: int,
                 output_dim: int = None):
        super(AttentionPool2d, self).__init__()
        self.positional_embedding = nn.Parameter(
            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.reshape(x.shape[0], x.shape[1],
                      x.shape[2] * x.shape[3]).permute(2, 0,
                                                       1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x,
            key=x,
            value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat(
                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False)

        return x[0]


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self,
                 layers,
                 output_dim,
                 heads,
                 input_resolution=224,
                 width=64):
        super(ModifiedResNet, self).__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(
            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.conv2 = nn.Conv2d(
            width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.conv3 = nn.Conv2d(
            width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.avgpool = nn.AvgPool2d(2)
        self.relu = nn.ReLU(inplace=True)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim,
                                        heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):

        def stem(x):
            for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2),
                             (self.conv3, self.bn3)]:
                x = self.relu(bn(conv(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):

    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):

    def __init__(self, d_model: int, n_head: int, attn_mask=None):
        super(ResidualAttentionBlock, self).__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(
            OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)),
                         ('gelu', QuickGELU()),
                         ('c_proj', nn.Linear(d_model * 4, d_model))]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        attn_mask_ = self.attn_mask
        if self.attn_mask is not None and hasattr(self.attn_mask, '__call__'):
            attn_mask_ = self.attn_mask(x.size(0))  # LND

        attn_mask_ = attn_mask_.to(
            dtype=x.dtype, device=x.device) if attn_mask_ is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask_)[0]

    def forward(self, x):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):

    def __init__(self,
                 width: int,
                 layers: int,
                 heads: int,
                 attn_mask=None,
                 use_gc=0):
        super(Transformer, self).__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[
            ResidualAttentionBlock(width, heads, attn_mask)
            for _ in range(layers)
        ])

        self.use_gc = use_gc

    def forward(self, x: torch.Tensor):
        if self.use_gc > 0:
            for blk in self.resblocks:
                x = checkpoint.checkpoint(blk, x)
            return x
        else:
            return self.resblocks(x)


class VisualTransformer(nn.Module):

    def __init__(self,
                 input_resolution: int,
                 patch_size: int,
                 width: int,
                 layers: int,
                 heads: int,
                 output_dim: int,
                 linear_patch: str = '2d',
                 use_gc: int = 0):
        super(VisualTransformer, self).__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim

        self.conv1 = nn.Conv2d(
            in_channels=3,
            out_channels=width,
            kernel_size=patch_size,
            stride=patch_size,
            bias=False)

        scale = width**-0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn(
            (input_resolution // patch_size)**2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads, use_gc=use_gc)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

        # For 3D
        assert linear_patch in ['2d', '3d']
        self.linear_patch = linear_patch
        if self.linear_patch == '3d':
            self.conv2 = nn.Conv3d(
                in_channels=3,
                out_channels=width,
                kernel_size=(3, patch_size, patch_size),
                stride=(1, patch_size, patch_size),
                padding=(1, 0, 0),
                bias=False)

    def forward(self, x: torch.Tensor, video_frame=-1):

        if self.linear_patch == '3d':
            assert video_frame != -1
            x_3d = x.reshape(-1, video_frame, x.shape[-3], x.shape[-2],
                             x.shape[-1])
            x_3d = x_3d.permute(0, 2, 1, 3, 4)
            x_3d = self.conv2(x_3d)  # shape = [*, width, frame, grid, grid]
            x_3d = x_3d.permute(0, 2, 1, 3,
                                4)  # shape = [*, frame, width, grid, grid]
            x = x_3d.reshape(
                -1, x_3d.shape[-3], x_3d.shape[-2],
                x_3d.shape[-1]).contiguous()  # shape = [*, width, grid, grid]
        else:
            x = self.conv1(x)  # shape = [*, width, grid, grid]

        x = x.reshape(x.shape[0], x.shape[1],
                      -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]

        _x = self.class_embedding.to(x.dtype) + torch.zeros(
            x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device)
        x = torch.cat([_x, x], dim=1)
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        return x


class CLIP(nn.Module):

    def __init__(
            self,
            embed_dim: int,
            # vision
            image_resolution: int,
            vision_layers: Union[Tuple[int, int, int, int], int],
            vision_width: int,
            vision_patch_size: int,
            # text
            context_length: int,
            vocab_size: int,
            transformer_width: int,
            transformer_heads: int,
            transformer_layers: int,
            # vision linear of patch
            linear_patch: str = '2d',
            use_gc: int = 0):
        super(CLIP, self).__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width)
        else:
            vision_heads = vision_width // 64
            self.visual = VisualTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim,
                linear_patch=linear_patch,
                use_gc=use_gc)

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask)

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(
            torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(
            torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features**-0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [
                    self.visual.layer1, self.visual.layer2, self.visual.layer3,
                    self.visual.layer4
            ]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith('bn3.weight'):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width**-0.5) * (
            (2 * self.transformer.layers)**-0.5)
        attn_std = self.transformer.width**-0.5
        fc_std = (2 * self.transformer.width)**-0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(
                self.text_projection, std=self.transformer.width**-0.5)

    def build_attention_mask(self, context_length):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.zeros(context_length, context_length)
        mask.fill_(float('-inf'))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image, return_hidden=False):
        hidden = self.visual(image.type(self.dtype))
        hidden = self.visual.ln_post(hidden) @ self.visual.proj

        x = hidden[:, 0, :]

        if return_hidden:
            return x, hidden

        return x

    def encode_text(self, text, return_hidden=False, prompt=None):
        x = self.token_embedding(text).type(
            self.dtype)  # [batch_size, n_ctx, d_model]
        if prompt:
            x = prompt(x)

        pos_emd = self.positional_embedding[:x.size(1), :].type(self.dtype)
        x = x + pos_emd
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        hidden = self.ln_final(x).type(self.dtype) @ self.text_projection

        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = hidden[torch.arange(hidden.shape[0]), text.argmax(dim=-1)]

        if return_hidden:
            return x, hidden

        return x

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(
            dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(
            dim=-1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logit_scale * text_features @ image_features.t()

        return logits_per_image, logits_per_text


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(lay):
        # l = lay
        if isinstance(lay, (nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.Linear)):
            lay.weight.data = lay.weight.data.half()
            if lay.bias is not None:
                lay.bias.data = lay.bias.data.half()

        if isinstance(lay, nn.MultiheadAttention):
            for attr in [
                    *[f'{s}_proj_weight' for s in ['in', 'q', 'k', 'v']],
                    'in_proj_bias', 'bias_k', 'bias_v'
            ]:
                tensor = getattr(lay, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ['text_projection', 'proj']:
            if hasattr(lay, name):
                attr = getattr(lay, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)