text: # coding=utf-8
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import copy
  import logging
  import math
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import numpy as np
  
  from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv2d, LayerNorm
  from torch.nn.modules.utils import _pair
  from scipy import ndimage
  
  import models.configs as configs
  
  logger = logging.getLogger(__name__)
  
  ATTENTION_Q = "MultiHeadDotProductAttention_1/query"
  ATTENTION_K = "MultiHeadDotProductAttention_1/key"
  ATTENTION_V = "MultiHeadDotProductAttention_1/value"
  ATTENTION_OUT = "MultiHeadDotProductAttention_1/out"
  FC_0 = "MlpBlock_3/Dense_0"
  FC_1 = "MlpBlock_3/Dense_1"
  ATTENTION_NORM = "LayerNorm_0"
  MLP_NORM = "LayerNorm_2"
  
  def pjoin(*args):
      return "/".join(args)
  
  def np2th(weights, conv=False):
      """Possibly convert HWIO to OIHW."""
      if conv:
          weights = weights.transpose([3, 2, 0, 1])
      return torch.from_numpy(weights)
  
  def swish(x):
      return x * torch.sigmoid(x)
  
  ACT2FN = {"gelu": torch.nn.functional.gelu, "relu": torch.nn.functional.relu, "swish": swish}
  
  class LabelSmoothing(nn.Module):
      """
      NLL loss with label smoothing.
      """
      def __init__(self, smoothing=0.0):
          """
          Constructor for the LabelSmoothing module.
          :param smoothing: label smoothing factor
          """
          super(LabelSmoothing, self).__init__()
          self.confidence = 1.0 - smoothing
          self.smoothing = smoothing
  
      def forward(self, x, target):
          logprobs = torch.nn.functional.log_softmax(x, dim=-1)
  
          nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
          nll_loss = nll_loss.squeeze(1)
          smooth_loss = -logprobs.mean(dim=-1)
          loss = self.confidence * nll_loss + self.smoothing * smooth_loss
          return loss.mean()
  
  class Attention(nn.Module):
      def __init__(self, config):
          super(Attention, self).__init__()
          self.num_attention_heads = config.transformer["num_heads"]
          self.attention_head_size = int(config.hidden_size / self.num_attention_heads)
          self.all_head_size = self.num_attention_heads * self.attention_head_size
  
          self.query = Linear(config.hidden_size, self.all_head_size)
          self.key = Linear(config.hidden_size, self.all_head_size)
          self.value = Linear(config.hidden_size, self.all_head_size)
  
          self.out = Linear(config.hidden_size, config.hidden_size)
          self.attn_dropout = Dropout(config.transformer["attention_dropout_rate"])
          self.proj_dropout = Dropout(config.transformer["attention_dropout_rate"])
  
          self.softmax = Softmax(dim=-1)
  
      def transpose_for_scores(self, x):
          new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
          x = x.view(*new_x_shape)
          return x.permute(0, 2, 1, 3)
  
      def forward(self, hidden_states):
          mixed_query_layer = self.query(hidden_states)
          mixed_key_layer = self.key(hidden_states)
          mixed_value_layer = self.value(hidden_states)
  
          query_layer = self.transpose_for_scores(mixed_query_layer)
          key_layer = self.transpose_for_scores(mixed_key_layer)
          value_layer = self.transpose_for_scores(mixed_value_layer)
  
          attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
          attention_scores = attention_scores / math.sqrt(self.attention_head_size)
          attention_probs = self.softmax(attention_scores)
          weights = attention_probs
          attention_probs = self.attn_dropout(attention_probs)
  
          context_layer = torch.matmul(attention_probs, value_layer)
          context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
          new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
          context_layer = context_layer.view(*new_context_layer_shape)
          attention_output = self.out(context_layer)
          attention_output = self.proj_dropout(attention_output)
          return attention_output, weights
  
  class Mlp(nn.Module):
      def __init__(self, config):
          super(Mlp, self).__init__()
          self.fc1 = Linear(config.hidden_size, config.transformer["mlp_dim"])
          self.fc2 = Linear(config.transformer["mlp_dim"], config.hidden_size)
          self.act_fn = ACT2FN["gelu"]
          self.dropout = Dropout(config.transformer["dropout_rate"])
  
          self._init_weights()
  
      def _init_weights(self):
          nn.init.xavier_uniform_(self.fc1.weight)
          nn.init.xavier_uniform_(self.fc2.weight)
          nn.init.normal_(self.fc1.bias, std=1e-6)
          nn.init.normal_(self.fc2.bias, std=1e-6)
  
      def forward(self, x):
          x = self.fc1(x)
          x = self.act_fn(x)
          x = self.dropout(x)
          x = self.fc2(x)
          x = self.dropout(x)
          return x
  
  class Embeddings(nn.Module):
      """Construct the embeddings from patch, position embeddings.
      """
      def __init__(self, config, img_size, in_channels=3):
          super(Embeddings, self).__init__()
          self.hybrid = None
          img_size = _pair(img_size)
  
          patch_size = _pair(config.patches["size"])
          if config.split == 'non-overlap':
              n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
              self.patch_embeddings = Conv2d(in_channels=in_channels,
                                         out_channels=config.hidden_size,
                                         kernel_size=patch_size,
                                         stride=patch_size)
          elif config.split == 'overlap':
              n_patches = ((img_size[0] - patch_size[0]) // config.slide_step + 1) * ((img_size[1] - patch_size[1]) // config.slide_step + 1)
              self.patch_embeddings = Conv2d(in_channels=in_channels,
                                          out_channels=config.hidden_size,
                                          kernel_size=patch_size,
                                          stride=(config.slide_step, config.slide_step))
          self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))
          self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
  
          self.dropout = Dropout(config.transformer["dropout_rate"])
  
      def forward(self, x):
          B = x.shape[0]
          cls_tokens = self.cls_token.expand(B, -1, -1)
  
          if self.hybrid:
              x = self.hybrid_model(x)
          x = self.patch_embeddings(x)
          x = x.flatten(2)
          x = x.transpose(-1, -2)
          x = torch.cat((cls_tokens, x), dim=1)
  
          embeddings = x + self.position_embeddings
          embeddings = self.dropout(embeddings)
          return embeddings
  
  class Block(nn.Module):
      def __init__(self, config):
          super(Block, self).__init__()
          self.hidden_size = config.hidden_size
          self.attention_norm = LayerNorm(config.hidden_size, eps=1e-6)
          self.ffn_norm = LayerNorm(config.hidden_size, eps=1e-6)
          self.ffn = Mlp(config)
          self.attn = Attention(config)
  
      def forward(self, x):
          h = x
          x = self.attention_norm(x)
          x, weights = self.attn(x)
          x = x + h
  
          h = x
          x = self.ffn_norm(x)
          x = self.ffn(x)
          x = x + h
          return x, weights
  
      def load_from(self, weights, n_block):
          ROOT = f"Transformer/encoderblock_{n_block}"
          with torch.no_grad():
              query_weight = np2th(weights[pjoin(ROOT, ATTENTION_Q, "kernel")]).view(self.hidden_size, self.hidden_size).t()
              key_weight = np2th(weights[pjoin(ROOT, ATTENTION_K, "kernel")]).view(self.hidden_size, self.hidden_size).t()
              value_weight = np2th(weights[pjoin(ROOT, ATTENTION_V, "kernel")]).view(self.hidden_size, self.hidden_size).t()
              out_weight = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "kernel")]).view(self.hidden_size, self.hidden_size).t()
  
              query_bias = np2th(weights[pjoin(ROOT, ATTENTION_Q, "bias")]).view(-1)
              key_bias = np2th(weights[pjoin(ROOT, ATTENTION_K, "bias")]).view(-1)
              value_bias = np2th(weights[pjoin(ROOT, ATTENTION_V, "bias")]).view(-1)
              out_bias = np2th(weights[pjoin(ROOT, ATTENTION_OUT, "bias")]).view(-1)
  
              self.attn.query.weight.copy_(query_weight)
              self.attn.key.weight.copy_(key_weight)
              self.attn.value.weight.copy_(value_weight)
              self.attn.out.weight.copy_(out_weight)
              self.attn.query.bias.copy_(query_bias)
              self.attn.key.bias.copy_(key_bias)
              self.attn.value.bias.copy_(value_bias)
              self.attn.out.bias.copy_(out_bias)
  
              mlp_weight_0 = np2th(weights[pjoin(ROOT, FC_0, "kernel")]).t()
              mlp_weight_1 = np2th(weights[pjoin(ROOT, FC_1, "kernel")]).t()
              mlp_bias_0 = np2th(weights[pjoin(ROOT, FC_0, "bias")]).t()
              mlp_bias_1 = np2th(weights[pjoin(ROOT, FC_1, "bias")]).t()
  
              self.ffn.fc1.weight.copy_(mlp_weight_0)
              self.ffn.fc2.weight.copy_(mlp_weight_1)
              self.ffn.fc1.bias.copy_(mlp_bias_0)
              self.ffn.fc2.bias.copy_(mlp_bias_1)
  
              self.attention_norm.weight.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "scale")]))
              self.attention_norm.bias.copy_(np2th(weights[pjoin(ROOT, ATTENTION_NORM, "bias")]))
              self.ffn_norm.weight.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "scale")]))
              self.ffn_norm.bias.copy_(np2th(weights[pjoin(ROOT, MLP_NORM, "bias")]))
  
  class Part_Attention(nn.Module):
      def __init__(self):
          super(Part_Attention, self).__init__()
  
      def forward(self, x):
          length = len(x)
          last_map = x[0]
          for i in range(1, length):
              last_map = torch.matmul(x[i], last_map)
          last_map = last_map[:,:,0,1:]
  
          _, max_inx = last_map.max(2)
          return _, max_inx
  
  class Encoder(nn.Module):
      def __init__(self, config):
          super(Encoder, self).__init__()
          self.layer = nn.ModuleList()
          for _ in range(config.transformer["num_layers"] - 1):
              layer = Block(config)
              self.layer.append(copy.deepcopy(layer))
          self.part_select = Part_Attention()
          self.part_layer = Block(config)
          self.part_norm = LayerNorm(config.hidden_size, eps=1e-6)
  
      def forward(self, hidden_states):
          attn_weights = []
          for layer in self.layer:
              hidden_states, weights = layer(hidden_states)
              attn_weights.append(weights)            
          part_num, part_inx = self.part_select(attn_weights)
          part_inx = part_inx + 1
          parts = []
          B, num = part_inx.shape
          for i in range(B):
              parts.append(hidden_states[i, part_inx[i,:]])
          parts = torch.stack(parts).squeeze(1)
          concat = torch.cat((hidden_states[:,0].unsqueeze(1), parts), dim=1)
          part_states, part_weights = self.part_layer(concat)
          part_encoded = self.part_norm(part_states)   
  
          return part_encoded
  
  class Transformer(nn.Module):
      def __init__(self, config, img_size):
          super(Transformer, self).__init__()
          self.embeddings = Embeddings(config, img_size=img_size)
          self.encoder = Encoder(config)
  
      def forward(self, input_ids):
          embedding_output = self.embeddings(input_ids)
          part_encoded = self.encoder(embedding_output)
          return part_encoded
  
  class VisionTransformer(nn.Module):
      def __init__(self, config, img_size=224, num_classes=21843, smoothing_value=0, zero_head=False):
          super(VisionTransformer, self).__init__()
          self.num_classes = num_classes
          self.smoothing_value = smoothing_value
          self.zero_head = zero_head
          self.classifier = config.classifier
          self.transformer = Transformer(config, img_size)
          self.part_head = Linear(config.hidden_size, num_classes)
  
      def forward(self, x, labels=None):
          part_tokens = self.transformer(x)
          part_logits = self.part_head(part_tokens[:, 0])
  
          if labels is not None:
              if self.smoothing_value == 0:
                  loss_fct = CrossEntropyLoss()
              else:
                  loss_fct = LabelSmoothing(self.smoothing_value)
              part_loss = loss_fct(part_logits.view(-1, self.num_classes), labels.view(-1))
              contrast_loss = con_loss(part_tokens[:, 0], labels.view(-1))
              loss = part_loss + contrast_loss
              return loss, part_logits
          else:
              return part_logits
  
      def load_from(self, weights):
          with torch.no_grad():
              self.transformer.embeddings.patch_embeddings.weight.copy_(np2th(weights["embedding/kernel"], conv=True))
              self.transformer.embeddings.patch_embeddings.bias.copy_(np2th(weights["embedding/bias"]))
              self.transformer.embeddings.cls_token.copy_(np2th(weights["cls"]))
  
              posemb = np2th(weights["Transformer/posembed_input/pos_embedding"])
              posemb_new = self.transformer.embeddings.position_embeddings
              if posemb.size() == posemb_new.size():
                  self.transformer.embeddings.position_embeddings.copy_(posemb)
              else:
                  logger.info("load_pretrained: resized variant: %s to %s" % (posemb.size(), posemb_new.size()))
                  ntok_new = posemb_new.size(1)
  
                  if self.classifier == "token":
                      posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:]
                      ntok_new -= 1
                  else:
                      posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
  
                  gs_old = int(np.sqrt(len(posemb_grid)))
                  gs_new = int(np.sqrt(ntok_new))
                  print('load_pretrained: grid-size from %s to %s' % (gs_old, gs_new))
                  posemb_grid = posemb_grid.reshape(gs_old, gs_old, -1)
  
                  zoom = (gs_new / gs_old, gs_new / gs_old, 1)
                  posemb_grid = ndimage.zoom(posemb_grid, zoom, order=1)
                  posemb_grid = posemb_grid.reshape(1, gs_new * gs_new, -1)
                  posemb = np.concatenate([posemb_tok, posemb_grid], axis=1)
                  self.transformer.embeddings.position_embeddings.copy_(np2th(posemb))
  
              for bname, block in self.transformer.encoder.named_children():
                  if bname.startswith('part') == False:
                      for uname, unit in block.named_children():
                          unit.load_from(weights, n_block=uname)
  
              if self.transformer.embeddings.hybrid:
                  self.transformer.embeddings.hybrid_model.root.conv.weight.copy_(np2th(weights["conv_root/kernel"], conv=True))
                  gn_weight = np2th(weights["gn_root/scale"]).view(-1)
                  gn_bias = np2th(weights["gn_root/bias"]).view(-1)
                  self.transformer.embeddings.hybrid_model.root.gn.weight.copy_(gn_weight)
                  self.transformer.embeddings.hybrid_model.root.gn.bias.copy_(gn_bias)
  
                  for bname, block in self.transformer.embeddings.hybrid_model.body.named_children():
                      for uname, unit in block.named_children():
                          unit.load_from(weights, n_block=bname, n_unit=uname) 
  
  def con_loss(features, labels):
      B, _ = features.shape
      features = F.normalize(features)
      cos_matrix = features.mm(features.t())
      pos_label_matrix = torch.stack([labels == labels[i] for i in range(B)]).float()
      neg_label_matrix = 1 - pos_label_matrix
      pos_cos_matrix = 1 - cos_matrix
      neg_cos_matrix = cos_matrix - 0.4
      neg_cos_matrix[neg_cos_matrix < 0] = 0
      loss = (pos_cos_matrix * pos_label_matrix).sum() + (neg_cos_matrix * neg_label_matrix).sum()
      loss /= B
      return loss
  
  CONFIGS = {
      'ViT-B_16': configs.get_b16_config(),
      'ViT-B_32': configs.get_b32_config(),
      'ViT-L_16': configs.get_l16_config(),
      'ViT-L_32': configs.get_l32_config(),
      'ViT-H_14': configs.get_h14_config(),
      'testing': configs.get_testing(),
  }
  