text: """
  This file contains helper functions for building the model and for loading model parameters.
  These helper functions are built to mirror those in the official TensorFlow implementation.
  """
  
  import re
  import math
  import collections
  from functools import partial
  import torch
  from torch import nn
  from torch.nn import functional as F
  from torch.utils import model_zoo
  
  try:
      from torch.hub import load_state_dict_from_url
  except ImportError:
      from torch.utils.model_zoo import load_url as load_state_dict_from_url
  
  ########################################################################
  ############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ###############
  ########################################################################
  
  
  # Parameters for the entire model (stem, all blocks, and head)
  GlobalParams = collections.namedtuple('GlobalParams', [
      'dropout_rate',
      'num_classes', 'width_coefficient', 'depth_coefficient',
      'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size'])
  
  # Parameters for an individual model block
  BlockArgs = collections.namedtuple('BlockArgs', [
      'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
      'expand_ratio', 'id_skip', 'stride', 'se_ratio'])
  
  # Change namedtuple defaults
  GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
  BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)
  
  
  class SwishImplementation(torch.autograd.Function):
      @staticmethod
      def forward(ctx, i):
          result = i * torch.sigmoid(i)
          ctx.save_for_backward(i)
          return result
  
      @staticmethod
      def backward(ctx, grad_output):
          i = ctx.saved_variables[0]
          sigmoid_i = torch.sigmoid(i)
          return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
  
  
  class MemoryEfficientSwish(nn.Module):
      def forward(self, x):
          return SwishImplementation.apply(x)
  
  class Swish(nn.Module):
      def forward(self, x):
          return x * torch.sigmoid(x)
  
  
  def round_filters(filters, global_params):
      """ Calculate and round number of filters based on depth multiplier. """
      multiplier = global_params.width_coefficient
      if not multiplier:
          return filters
      divisor = global_params.depth_divisor
      min_depth = global_params.min_depth
      filters *= multiplier
      min_depth = min_depth or divisor
      new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
      if new_filters < 0.9 * filters:  # prevent rounding by more than 10%
          new_filters += divisor
      return int(new_filters)
  
  
  def round_repeats(repeats, global_params):
      """ Round number of filters based on depth multiplier. """
      multiplier = global_params.depth_coefficient
      if not multiplier:
          return repeats
      return int(math.ceil(multiplier * repeats))
  
  
  def drop_connect(inputs, p, training):
      """ Drop connect. """
      if not training: return inputs
      batch_size = inputs.shape[0]
      keep_prob = 1 - p
      random_tensor = keep_prob
      random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
      binary_tensor = torch.floor(random_tensor)
      output = inputs / keep_prob * binary_tensor
      return output
  
  
  def get_same_padding_conv2d(image_size=None):
      """ Chooses static padding if you have specified an image size, and dynamic padding otherwise.
          Static padding is necessary for ONNX exporting of models. """
      if image_size is None:
          return Conv2dDynamicSamePadding
      else:
          return partial(Conv2dStaticSamePadding, image_size=image_size)
  
  
  class Conv2dDynamicSamePadding(nn.Conv2d):
      """ 2D Convolutions like TensorFlow, for a dynamic image size """
      """ Weight Standardization """
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):
          super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)
          self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
  
      def forward(self, x):
          ih, iw = x.size()[-2:]
          kh, kw = self.weight.size()[-2:]
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])
          
          # Weight Standardization
          weight = self.weight
          weight_mean = weight.mean(dim=1, keepdims=True).mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
          weight = weight - weight_mean
          std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
          weight = weight / std.expand_as(weight)
          return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
  
  
  class Conv2dStaticSamePadding(nn.Conv2d):
      """ 2D Convolutions like TensorFlow, for a fixed image size"""
  
      def __init__(self, in_channels, out_channels, kernel_size, image_size=None, **kwargs):
          super().__init__(in_channels, out_channels, kernel_size, **kwargs)
          self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2
  
          # Calculate padding based on image size and save it
          assert image_size is not None
          ih, iw = image_size if type(image_size) == list else [image_size, image_size]
          kh, kw = self.weight.size()[-2:]
          sh, sw = self.stride
          oh, ow = math.ceil(ih / sh), math.ceil(iw / sw)
          pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)
          pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)
          if pad_h > 0 or pad_w > 0:
              self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))
          else:
              self.static_padding = Identity()
  
      def forward(self, x):
          x = self.static_padding(x)
          # Weight Standardization
          weight = self.weight
          weight_mean = weight.mean(dim=1, keepdims=True).mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
          weight = weight - weight_mean
          std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
          weight = weight / std.expand_as(weight)
          x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
          return x
  
  
  class Identity(nn.Module):
      def __init__(self, ):
          super(Identity, self).__init__()
  
      def forward(self, input):
          return input
  
  
  ########################################################################
  ############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ##############
  ########################################################################
  
  
  def efficientnet_params(model_name):
      """ Map EfficientNet model name to parameter coefficients. """
      params_dict = {
          # Coefficients:   width,depth,res,dropout
          'efficientnet-b0': (1.0, 1.0, 224, 0.2),
          'efficientnet-b1': (1.0, 1.1, 240, 0.2),
          'efficientnet-b2': (1.1, 1.2, 260, 0.3),
          'efficientnet-b3': (1.2, 1.4, 300, 0.3),
          'efficientnet-b4': (1.4, 1.8, 380, 0.4),
          'efficientnet-b5': (1.6, 2.2, 456, 0.4),
          'efficientnet-b6': (1.8, 2.6, 528, 0.5),
          'efficientnet-b7': (2.0, 3.1, 600, 0.5),
      }
      return params_dict[model_name]
  
  
  class BlockDecoder(object):
      """ Block Decoder for readability, straight from the official TensorFlow repository """
  
      @staticmethod
      def _decode_block_string(block_string):
          """ Gets a block through a string notation of arguments. """
          assert isinstance(block_string, str)
  
          ops = block_string.split('_')
          options = {}
          for op in ops:
              splits = re.split(r'(\d.*)', op)
              if len(splits) >= 2:
                  key, value = splits[:2]
                  options[key] = value
  
          # Check stride
          assert (('s' in options and len(options['s']) == 1) or
                  (len(options['s']) == 2 and options['s'][0] == options['s'][1]))
  
          return BlockArgs(
              kernel_size=int(options['k']),
              num_repeat=int(options['r']),
              input_filters=int(options['i']),
              output_filters=int(options['o']),
              expand_ratio=int(options['e']),
              id_skip=('noskip' not in block_string),
              se_ratio=float(options['se']) if 'se' in options else None,
              stride=[int(options['s'][0])])
  
      @staticmethod
      def _encode_block_string(block):
          """Encodes a block to a string."""
          args = [
              'r%d' % block.num_repeat,
              'k%d' % block.kernel_size,
              's%d%d' % (block.strides[0], block.strides[1]),
              'e%s' % block.expand_ratio,
              'i%d' % block.input_filters,
              'o%d' % block.output_filters
          ]
          if 0 < block.se_ratio <= 1:
              args.append('se%s' % block.se_ratio)
          if block.id_skip is False:
              args.append('noskip')
          return '_'.join(args)
  
      @staticmethod
      def decode(string_list):
          """
          Decodes a list of string notations to specify blocks inside the network.
  
          :param string_list: a list of strings, each string is a notation of block
          :return: a list of BlockArgs namedtuples of block args
          """
          assert isinstance(string_list, list)
          blocks_args = []
          for block_string in string_list:
              blocks_args.append(BlockDecoder._decode_block_string(block_string))
          return blocks_args
  
      @staticmethod
      def encode(blocks_args):
          """
          Encodes a list of BlockArgs to a list of strings.
  
          :param blocks_args: a list of BlockArgs namedtuples of block args
          :return: a list of strings, each string is a notation of block
          """
          block_strings = []
          for block in blocks_args:
              block_strings.append(BlockDecoder._encode_block_string(block))
          return block_strings
  
  
  def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2,
                   drop_connect_rate=0.2, image_size=None, num_classes=1000):
      """ Creates a efficientnet model. """
  
      blocks_args = [
          'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
          'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
          'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
          'r1_k3_s11_e6_i192_o320_se0.25',
      ]
      blocks_args = BlockDecoder.decode(blocks_args)
  
      global_params = GlobalParams(
          dropout_rate=dropout_rate,
          drop_connect_rate=drop_connect_rate,
          # data_format='channels_last',  # removed, this is always true in PyTorch
          num_classes=num_classes,
          width_coefficient=width_coefficient,
          depth_coefficient=depth_coefficient,
          depth_divisor=8,
          min_depth=None,
          image_size=image_size,
      )
  
      return blocks_args, global_params
  
  
  def get_model_params(model_name, override_params, num_classes=1000):
      """ Get the block args and global params for a given model """
      if model_name.startswith('efficientnet'):
          w, d, s, p = efficientnet_params(model_name)
          # note: all models have drop connect rate = 0.2
          blocks_args, global_params = efficientnet(
              width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s, num_classes=num_classes)
      else:
          raise NotImplementedError('model name is not pre-defined: %s' % model_name)
      if override_params:
          # ValueError will be raised here if override_params has fields not included in global_params.
          global_params = global_params._replace(**override_params)
      return blocks_args, global_params
  
  
  url_map = {
      'efficientnet-b0': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b0-355c32eb.pth',
      'efficientnet-b1': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b1-f1951068.pth',
      'efficientnet-b2': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b2-8bb594d6.pth',
      'efficientnet-b3': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b3-5fb5a3c3.pth',
      'efficientnet-b4': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b4-6ed6700e.pth',
      'efficientnet-b5': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b5-b6417697.pth',
      'efficientnet-b6': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b6-c76e70fd.pth',
      'efficientnet-b7': 'http://storage.googleapis.com/public-models/efficientnet/efficientnet-b7-dcc49843.pth',
  }
  
  
  def load_pretrained_weights(model, model_name, load_fc=True):
      """ Loads pretrained weights, and downloads if loading for the first time. """
      state_dict = model_zoo.load_url(url_map[model_name])
      if load_fc:
          model.load_state_dict(state_dict)
      else:
          state_dict.pop('_fc.weight')
          state_dict.pop('_fc.bias')
          res = model.load_state_dict(state_dict, strict=False)
          assert set(res.missing_keys) == set(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights'
      print('Loaded pretrained weights for {}'.format(model_name))
  
  def GroupNorm2d(num_features):
      return nn.GroupNorm(num_groups=8, num_channels=num_features)