content: from collections import OrderedDict
  from typing import Tuple, Union
  
  import numpy as np
  import torch
  import torch.nn.functional as F
  from torch import nn
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1):
          super().__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.relu1 = nn.ReLU(inplace=True)
  
          self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(planes)
          self.relu2 = nn.ReLU(inplace=True)
  
          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.relu3 = 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.relu1(self.bn1(self.conv1(x)))
          out = self.relu2(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.relu3(out)
          return out
  
  
  class AttentionPool2d(nn.Module):
      def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
          super().__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.flatten(start_dim=2).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[:1], 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.squeeze(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().__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.relu1 = nn.ReLU(inplace=True)
          self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
          self.bn2 = nn.BatchNorm2d(width // 2)
          self.relu2 = nn.ReLU(inplace=True)
          self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
          self.bn3 = nn.BatchNorm2d(width)
          self.relu3 = nn.ReLU(inplace=True)
          self.avgpool = nn.AvgPool2d(2)
  
          # 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):
              x = self.relu1(self.bn1(self.conv1(x)))
              x = self.relu2(self.bn2(self.conv2(x)))
              x = self.relu3(self.bn3(self.conv3(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: torch.Tensor = None):
          super().__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):
          self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
          return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
  
      def forward(self, x: torch.Tensor):
          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: torch.Tensor = None):
          super().__init__()
          self.width = width
          self.layers = layers
          self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
  
      def forward(self, x: torch.Tensor):
          return self.resblocks(x)
  
  
  class VisionTransformer(nn.Module):
      def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
          super().__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)
  
          self.ln_post = LayerNorm(width)
          self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
  
      def forward(self, x: torch.Tensor):
          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 = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
          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
  
          x = self.ln_post(x[:, 0, :])
  
          if self.proj is not None:
              x = x @ self.proj
  
          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
                   ):
          super().__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 = VisionTransformer(
                  input_resolution=image_resolution,
                  patch_size=vision_patch_size,
                  width=vision_width,
                  layers=vision_layers,
                  heads=vision_heads,
                  output_dim=embed_dim
              )
  
          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([]) * np.log(1 / 0.07))
  
          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):
          # lazily create causal attention mask, with full attention between the vision tokens
          # pytorch uses additive attention mask; fill with -inf
          mask = torch.empty(self.context_length, self.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 self.visual(image.type(self.dtype))
  
      def encode_text(self, text):
          x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
  
          x = x + self.positional_embedding.type(self.dtype)
          x = x.permute(1, 0, 2)  # NLD -> LND
          x = self.transformer(x)
          x = x.permute(1, 0, 2)  # LND -> NLD
          x = self.ln_final(x).type(self.dtype)
  
          # x.shape = [batch_size, n_ctx, transformer.width]
          # take features from the eot embedding (eot_token is the highest number in each sequence)
          x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
  
          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 = logits_per_image.t()
  
          # shape = [global_batch_size, global_batch_size]
          return logits_per_image, logits_per_text
  
  
  def convert_weights(model: nn.Module):
      """Convert applicable model parameters to fp16"""
  
      def _convert_weights_to_fp16(l):
          if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
              l.weight.data = l.weight.data.half()
              if l.bias is not None:
                  l.bias.data = l.bias.data.half()
  
          if isinstance(l, 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(l, attr)
                  if tensor is not None:
                      tensor.data = tensor.data.half()
  
          for name in ["text_projection", "proj"]:
              if hasattr(l, name):
                  attr = getattr(l, name)
                  if attr is not None:
                      attr.data = attr.data.half()
  
      model.apply(_convert_weights_to_fp16)
  
  
  def build_model(state_dict: dict):
      vit = "visual.proj" in state_dict
  
      if vit:
          vision_width = state_dict["visual.conv1.weight"].shape[0]
          vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
          vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
          grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
          image_resolution = vision_patch_size * grid_size
      else:
          counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
          vision_layers = tuple(counts)
          vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
          output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
          vision_patch_size = None
          assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
          image_resolution = output_width * 32
  
      embed_dim = state_dict["text_projection"].shape[1]
      context_length = state_dict["positional_embedding"].shape[0]
      vocab_size = state_dict["token_embedding.weight"].shape[0]
      transformer_width = state_dict["ln_final.weight"].shape[0]
      transformer_heads = transformer_width // 64
      transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
  
      model = CLIP(
          embed_dim,
          image_resolution, vision_layers, vision_width, vision_patch_size,
          context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
      )
  
      for key in ["input_resolution", "context_length", "vocab_size"]:
          if key in state_dict:
              del state_dict[key]
  
      convert_weights(model)
      model.load_state_dict(state_dict)
      return model.eval()
  