text: # this code is a copy from huggingface
  # with some minor modifications
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import math
  import json
  import copy
  import logging
  import os
  import shutil
  import tempfile
  from functools import wraps
  from hashlib import sha256
  import sys
  from io import open
  
  import boto3
  import requests
  from botocore.exceptions import ClientError
  from tqdm import tqdm
  
  try:
      from urllib.parse import urlparse
  except ImportError:
      from urlparse import urlparse
  
  try:
      from pathlib import Path
      PYTORCH_PRETRAINED_BIGGAN_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BIGGAN_CACHE',
                                                     Path.home() / '.pytorch_pretrained_biggan'))
  except (AttributeError, ImportError):
      PYTORCH_PRETRAINED_BIGGAN_CACHE = os.getenv('PYTORCH_PRETRAINED_BIGGAN_CACHE',
                                                os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_biggan'))
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  PRETRAINED_MODEL_ARCHIVE_MAP = {
      'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin",
      'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin",
      'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin",
  }
  
  PRETRAINED_CONFIG_ARCHIVE_MAP = {
      'biggan-deep-128': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-config.json",
      'biggan-deep-256': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-config.json",
      'biggan-deep-512': "https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json",
  }
  
  WEIGHTS_NAME = 'pytorch_model.bin'
  CONFIG_NAME = 'config.json'
  
  def url_to_filename(url, etag=None):
      """
      Convert `url` into a hashed filename in a repeatable way.
      If `etag` is specified, append its hash to the url's, delimited
      by a period.
      """
      url_bytes = url.encode('utf-8')
      url_hash = sha256(url_bytes)
      filename = url_hash.hexdigest()
  
      if etag:
          etag_bytes = etag.encode('utf-8')
          etag_hash = sha256(etag_bytes)
          filename += '.' + etag_hash.hexdigest()
  
      return filename
  
  
  def filename_to_url(filename, cache_dir=None):
      """
      Return the url and etag (which may be ``None``) stored for `filename`.
      Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      cache_path = os.path.join(cache_dir, filename)
      if not os.path.exists(cache_path):
          raise EnvironmentError("file {} not found".format(cache_path))
  
      meta_path = cache_path + '.json'
      if not os.path.exists(meta_path):
          raise EnvironmentError("file {} not found".format(meta_path))
  
      with open(meta_path, encoding="utf-8") as meta_file:
          metadata = json.load(meta_file)
      url = metadata['url']
      etag = metadata['etag']
  
      return url, etag
  
  
  def cached_path(url_or_filename, cache_dir=None):
      """
      Given something that might be a URL (or might be a local path),
      determine which. If it's a URL, download the file and cache it, and
      return the path to the cached file. If it's already a local path,
      make sure the file exists and then return the path.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
      if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
          url_or_filename = str(url_or_filename)
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      parsed = urlparse(url_or_filename)
  
      if parsed.scheme in ('http', 'https', 's3'):
          # URL, so get it from the cache (downloading if necessary)
          return get_from_cache(url_or_filename, cache_dir)
      elif os.path.exists(url_or_filename):
          # File, and it exists.
          return url_or_filename
      elif parsed.scheme == '':
          # File, but it doesn't exist.
          raise EnvironmentError("file {} not found".format(url_or_filename))
      else:
          # Something unknown
          raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
  
  
  def split_s3_path(url):
      """Split a full s3 path into the bucket name and path."""
      parsed = urlparse(url)
      if not parsed.netloc or not parsed.path:
          raise ValueError("bad s3 path {}".format(url))
      bucket_name = parsed.netloc
      s3_path = parsed.path
      # Remove '/' at beginning of path.
      if s3_path.startswith("/"):
          s3_path = s3_path[1:]
      return bucket_name, s3_path
  
  
  def s3_request(func):
      """
      Wrapper function for s3 requests in order to create more helpful error
      messages.
      """
  
      @wraps(func)
      def wrapper(url, *args, **kwargs):
          try:
              return func(url, *args, **kwargs)
          except ClientError as exc:
              if int(exc.response["Error"]["Code"]) == 404:
                  raise EnvironmentError("file {} not found".format(url))
              else:
                  raise
  
      return wrapper
  
  
  @s3_request
  def s3_etag(url):
      """Check ETag on S3 object."""
      s3_resource = boto3.resource("s3")
      bucket_name, s3_path = split_s3_path(url)
      s3_object = s3_resource.Object(bucket_name, s3_path)
      return s3_object.e_tag
  
  
  @s3_request
  def s3_get(url, temp_file):
      """Pull a file directly from S3."""
      s3_resource = boto3.resource("s3")
      bucket_name, s3_path = split_s3_path(url)
      s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
  
  
  def http_get(url, temp_file):
      req = requests.get(url, stream=True)
      content_length = req.headers.get('Content-Length')
      total = int(content_length) if content_length is not None else None
      progress = tqdm(unit="B", total=total)
      for chunk in req.iter_content(chunk_size=1024):
          if chunk: # filter out keep-alive new chunks
              progress.update(len(chunk))
              temp_file.write(chunk)
      progress.close()
  
  
  def get_from_cache(url, cache_dir=None):
      """
      Given a URL, look for the corresponding dataset in the local cache.
      If it's not there, download it. Then return the path to the cached file.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BIGGAN_CACHE
      if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      if not os.path.exists(cache_dir):
          os.makedirs(cache_dir)
  
      # Get eTag to add to filename, if it exists.
      try:
          filename = {'https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin':'07b5c0d1791fa2028f8aa458a36360f31e1549d44152c96579b3ad6b35055d34.c33e135ad91e13528d0b83edc3c53c7bf94f620bdf8bc2bf08be82ba6d602e62', 'https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-config.json':'a9937ab8377512e9f327212f707413ca6a17b09e3dbd9d2692c5bef9b901e695.7b1d5d2a369e67ee26e5d5c86a4dbdd49b097d4c9c44b887f92bee9fd679ed4a'}[url]
      except:
          from IPython import embed; embed()
      '''
      if url.startswith("s3://"):
          etag = s3_etag(url)
      else:
          response = requests.head(url, allow_redirects=True)
          if response.status_code != 200:
              raise IOError("HEAD request failed for url {} with status code {}"
                            .format(url, response.status_code))
          etag = response.headers.get("ETag")
  
      filename = url_to_filename(url, etag)
      '''
  
      # get cache path to put the file
      cache_path = os.path.join(cache_dir, filename)
  
      if not os.path.exists(cache_path):
          # Download to temporary file, then copy to cache dir once finished.
          # Otherwise you get corrupt cache entries if the download gets interrupted.
          with tempfile.NamedTemporaryFile() as temp_file:
              logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
  
              # GET file object
              if url.startswith("s3://"):
                  s3_get(url, temp_file)
              else:
                  http_get(url, temp_file)
  
              # we are copying the file before closing it, so flush to avoid truncation
              temp_file.flush()
              # shutil.copyfileobj() starts at the current position, so go to the start
              temp_file.seek(0)
  
              logger.info("copying %s to cache at %s", temp_file.name, cache_path)
              with open(cache_path, 'wb') as cache_file:
                  shutil.copyfileobj(temp_file, cache_file)
  
              logger.info("creating metadata file for %s", cache_path)
              meta = {'url': url, 'etag': etag}
              meta_path = cache_path + '.json'
              with open(meta_path, 'w', encoding="utf-8") as meta_file:
                  json.dump(meta, meta_file)
  
              logger.info("removing temp file %s", temp_file.name)
  
      return cache_path
  
  
  def read_set_from_file(filename):
      '''
      Extract a de-duped collection (set) of text from a file.
      Expected file format is one item per line.
      '''
      collection = set()
      with open(filename, 'r', encoding='utf-8') as file_:
          for line in file_:
              collection.add(line.rstrip())
      return collection
  
  
  def get_file_extension(path, dot=True, lower=True):
      ext = os.path.splitext(path)[1]
      ext = ext if dot else ext[1:]
      return ext.lower() if lower else ext
  
  class BigGANConfig(object):
      """ Configuration class to store the configuration of a `BigGAN`. 
          Defaults are for the 128x128 model.
          layers tuple are (up-sample in the layer ?, input channels, output channels)
      """
      def __init__(self,
                   output_dim=128,
                   z_dim=128,
                   class_embed_dim=128,
                   channel_width=128,
                   num_classes=1000,
                   layers=[(False, 16, 16),
                           (True, 16, 16),
                           (False, 16, 16),
                           (True, 16, 8),
                           (False, 8, 8),
                           (True, 8, 4),
                           (False, 4, 4),
                           (True, 4, 2),
                           (False, 2, 2),
                           (True, 2, 1)],
                   attention_layer_position=8,
                   eps=1e-4,
                   n_stats=51):
          """Constructs BigGANConfig. """
          self.output_dim = output_dim
          self.z_dim = z_dim
          self.class_embed_dim = class_embed_dim
          self.channel_width = channel_width
          self.num_classes = num_classes
          self.layers = layers
          self.attention_layer_position = attention_layer_position
          self.eps = eps
          self.n_stats = n_stats
  
      @classmethod
      def from_dict(cls, json_object):
          """Constructs a `BigGANConfig` from a Python dictionary of parameters."""
          config = BigGANConfig()
          for key, value in json_object.items():
              config.__dict__[key] = value
          return config
  
      @classmethod
      def from_json_file(cls, json_file):
          """Constructs a `BigGANConfig` from a json file of parameters."""
          with open(json_file, "r", encoding='utf-8') as reader:
              text = reader.read()
          return cls.from_dict(json.loads(text))
  
      def __repr__(self):
          return str(self.to_json_string())
  
      def to_dict(self):
          """Serializes this instance to a Python dictionary."""
          output = copy.deepcopy(self.__dict__)
          return output
  
      def to_json_string(self):
          """Serializes this instance to a JSON string."""
          return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
  def snconv2d(eps=1e-12, **kwargs):
      return nn.utils.spectral_norm(nn.Conv2d(**kwargs), eps=eps)
  
  def snlinear(eps=1e-12, **kwargs):
      return nn.utils.spectral_norm(nn.Linear(**kwargs), eps=eps)
  
  def sn_embedding(eps=1e-12, **kwargs):
      return nn.utils.spectral_norm(nn.Embedding(**kwargs), eps=eps)
  
  class SelfAttn(nn.Module):
      """ Self attention Layer"""
      def __init__(self, in_channels, eps=1e-12):
          super(SelfAttn, self).__init__()
          self.in_channels = in_channels
          self.snconv1x1_theta = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
                                          kernel_size=1, bias=False, eps=eps)
          self.snconv1x1_phi = snconv2d(in_channels=in_channels, out_channels=in_channels//8,
                                        kernel_size=1, bias=False, eps=eps)
          self.snconv1x1_g = snconv2d(in_channels=in_channels, out_channels=in_channels//2,
                                      kernel_size=1, bias=False, eps=eps)
          self.snconv1x1_o_conv = snconv2d(in_channels=in_channels//2, out_channels=in_channels,
                                           kernel_size=1, bias=False, eps=eps)
          self.maxpool = nn.MaxPool2d(2, stride=2, padding=0)
          self.softmax  = nn.Softmax(dim=-1)
          self.gamma = nn.Parameter(torch.zeros(1))
  
      def forward(self, x):
          _, ch, h, w = x.size()
          # Theta path
          theta = self.snconv1x1_theta(x)
          theta = theta.view(-1, ch//8, h*w)
          # Phi path
          phi = self.snconv1x1_phi(x)
          phi = self.maxpool(phi)
          phi = phi.view(-1, ch//8, h*w//4)
          # Attn map
          attn = torch.bmm(theta.permute(0, 2, 1), phi)
          attn = self.softmax(attn)
          # g path
          g = self.snconv1x1_g(x)
          g = self.maxpool(g)
          g = g.view(-1, ch//2, h*w//4)
          # Attn_g - o_conv
          attn_g = torch.bmm(g, attn.permute(0, 2, 1))
          attn_g = attn_g.view(-1, ch//2, h, w)
          attn_g = self.snconv1x1_o_conv(attn_g)
          # Out
          out = x + self.gamma*attn_g
          return out
  
  class BigGANBatchNorm(nn.Module):
      """ This is a batch norm module that can handle conditional input and can be provided with pre-computed
          activation means and variances for various truncation parameters.
          We cannot just rely on torch.batch_norm since it cannot handle
          batched weights (pytorch 1.0.1). We computate batch_norm our-self without updating running means and variances.
          If you want to train this model you should add running means and variance computation logic.
      """
      def __init__(self, num_features, condition_vector_dim=None, n_stats=51, eps=1e-4, conditional=True):
          super(BigGANBatchNorm, self).__init__()
          self.num_features = num_features
          self.eps = eps
          self.conditional = conditional
  
          # We use pre-computed statistics for n_stats values of truncation between 0 and 1
          self.register_buffer('running_means', torch.zeros(n_stats, num_features))
          self.register_buffer('running_vars', torch.ones(n_stats, num_features))
          self.step_size = 1.0 / (n_stats - 1)
  
          if conditional:
              assert condition_vector_dim is not None
              self.scale = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
              self.offset = snlinear(in_features=condition_vector_dim, out_features=num_features, bias=False, eps=eps)
          else:
              self.weight = torch.nn.Parameter(torch.Tensor(num_features))
              self.bias = torch.nn.Parameter(torch.Tensor(num_features))
  
      def forward(self, x, truncation, condition_vector=None):
          # Retreive pre-computed statistics associated to this truncation
          coef, start_idx = math.modf(truncation / self.step_size)
          start_idx = int(start_idx)
          if coef != 0.0:  # Interpolate
              running_mean = self.running_means[start_idx] * coef + self.running_means[start_idx + 1] * (1 - coef)
              running_var = self.running_vars[start_idx] * coef + self.running_vars[start_idx + 1] * (1 - coef)
          else:
              running_mean = self.running_means[start_idx]
              running_var = self.running_vars[start_idx]
  
          if self.conditional:
              running_mean = running_mean.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
              running_var = running_var.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
  
              weight = 1 + self.scale(condition_vector).unsqueeze(-1).unsqueeze(-1)
              bias = self.offset(condition_vector).unsqueeze(-1).unsqueeze(-1)
  
              out = (x - running_mean) / torch.sqrt(running_var + self.eps) * weight + bias
          else:
              out = F.batch_norm(x, running_mean, running_var, self.weight, self.bias,
                                 training=False, momentum=0.0, eps=self.eps)
  
          return out
  
  class GenBlock(nn.Module):
      def __init__(self, in_size, out_size, condition_vector_dim, reduction_factor=4, up_sample=False,
                   n_stats=51, eps=1e-12):
          super(GenBlock, self).__init__()
          self.up_sample = up_sample
          self.drop_channels = (in_size != out_size)
          middle_size = in_size // reduction_factor
  
          self.bn_0 = BigGANBatchNorm(in_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
          self.conv_0 = snconv2d(in_channels=in_size, out_channels=middle_size, kernel_size=1, eps=eps)
  
          self.bn_1 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
          self.conv_1 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
  
          self.bn_2 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
          self.conv_2 = snconv2d(in_channels=middle_size, out_channels=middle_size, kernel_size=3, padding=1, eps=eps)
  
          self.bn_3 = BigGANBatchNorm(middle_size, condition_vector_dim, n_stats=n_stats, eps=eps, conditional=True)
          self.conv_3 = snconv2d(in_channels=middle_size, out_channels=out_size, kernel_size=1, eps=eps)
  
          self.relu = nn.ReLU()
  
      def forward(self, x, cond_vector, truncation):
          x0 = x
  
          x = self.bn_0(x, truncation, cond_vector)
          x = self.relu(x)
          x = self.conv_0(x)
  
          x = self.bn_1(x, truncation, cond_vector)
          x = self.relu(x)
          if self.up_sample:
              x = F.interpolate(x, scale_factor=2, mode='nearest')
          x = self.conv_1(x)
  
          x = self.bn_2(x, truncation, cond_vector)
          x = self.relu(x)
          x = self.conv_2(x)
  
          x = self.bn_3(x, truncation, cond_vector)
          x = self.relu(x)
          x = self.conv_3(x)
  
          if self.drop_channels:
              new_channels = x0.shape[1] // 2
              x0 = x0[:, :new_channels, ...]
          if self.up_sample:
              x0 = F.interpolate(x0, scale_factor=2, mode='nearest')
  
          out = x + x0
          return out
  
  class Generator(nn.Module):
      def __init__(self, config):
          super(Generator, self).__init__()
          self.config = config
          ch = config.channel_width
          condition_vector_dim = config.z_dim * 2
  
          self.gen_z = snlinear(in_features=condition_vector_dim,
                                out_features=4 * 4 * 16 * ch, eps=config.eps)
  
          layers = []
          for i, layer in enumerate(config.layers):
              if i == config.attention_layer_position:
                  layers.append(SelfAttn(ch*layer[1], eps=config.eps))
              layers.append(GenBlock(ch*layer[1],
                                     ch*layer[2],
                                     condition_vector_dim,
                                     up_sample=layer[0],
                                     n_stats=config.n_stats,
                                     eps=config.eps))
          self.layers = nn.ModuleList(layers)
  
          self.bn = BigGANBatchNorm(ch, n_stats=config.n_stats, eps=config.eps, conditional=False)
          self.relu = nn.ReLU()
          self.conv_to_rgb = snconv2d(in_channels=ch, out_channels=ch, kernel_size=3, padding=1, eps=config.eps)
          self.tanh = nn.Tanh()
  
      def forward(self, cond_vector, truncation):
          z = self.gen_z(cond_vector[0].unsqueeze(0))
  
          # We use this conversion step to be able to use TF weights:
          # TF convention on shape is [batch, height, width, channels]
          # PT convention on shape is [batch, channels, height, width]
          z = z.view(-1, 4, 4, 16 * self.config.channel_width)
          z = z.permute(0, 3, 1, 2).contiguous()
  
          next_available_latent_index = 1
          for layer in self.layers:
              if isinstance(layer, GenBlock):
                  z = layer(z, cond_vector[next_available_latent_index].unsqueeze(0), truncation)
                  next_available_latent_index += 1
              else:
                  z = layer(z)
  
          z = self.bn(z, truncation)
          z = self.relu(z)
          z = self.conv_to_rgb(z)
          z = z[:, :3, ...]
          z = self.tanh(z)
          return z
  
  class BigGAN(nn.Module):
      """BigGAN Generator."""
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
          if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
              model_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
              config_file = PRETRAINED_CONFIG_ARCHIVE_MAP[pretrained_model_name_or_path]
          else:
              model_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
              config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
  
          try:
              resolved_model_file = cached_path(model_file, cache_dir=cache_dir)
              resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
          except EnvironmentError:
              logger.error("Wrong model name, should be a valid path to a folder containing "
                           "a {} file and a {} file or a model name in {}".format(
                           WEIGHTS_NAME, CONFIG_NAME, PRETRAINED_MODEL_ARCHIVE_MAP.keys()))
              raise
  
          logger.info("loading model {} from cache at {}".format(pretrained_model_name_or_path, resolved_model_file))
  
          # Load config
          config = BigGANConfig.from_json_file(resolved_config_file)
          logger.info("Model config {}".format(config))
  
          # Instantiate model.
          model = cls(config, *inputs, **kwargs)
          state_dict = torch.load(resolved_model_file, map_location='cpu' if not torch.cuda.is_available() else None)
          model.load_state_dict(state_dict, strict=False)
          return model
  
      def __init__(self, config):
          super(BigGAN, self).__init__()
          self.config = config
          self.embeddings = nn.Linear(config.num_classes, config.z_dim, bias=False)
          self.generator = Generator(config)
  
      def forward(self, z, class_label, truncation):
          assert 0 < truncation <= 1
  
          embed = self.embeddings(class_label)
          cond_vector = torch.cat((z, embed), dim=1)
  
          z = self.generator(cond_vector, truncation)
          return z
  