code: """
  Utilities for working with the local dataset cache.
  This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
  Copyright by the AllenNLP authors.
  """
  from __future__ import absolute_import, division, print_function, unicode_literals
  
  import fnmatch
  import json
  import logging
  import os
  import shutil
  import sys
  import tempfile
  from functools import wraps
  from hashlib import sha256
  from io import open
  
  import boto3
  import numpy as np
  import requests
  from botocore.exceptions import ClientError
  from dotmap import DotMap
  from tqdm import tqdm
  
  try:
      from torch.hub import _get_torch_home
  
      torch_cache_home = _get_torch_home()
  except ImportError:
      torch_cache_home = os.path.expanduser(
          os.getenv(
              "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch")
          )
      )
  default_cache_path = os.path.join(torch_cache_home, "farm")
  
  try:
      from urllib.parse import urlparse
  except ImportError:
      from urlparse import urlparse
  
  try:
      from pathlib import Path
  
      FARM_CACHE = Path(os.getenv("FARM_CACHE", default_cache_path))
  except (AttributeError, ImportError):
      FARM_CACHE = os.getenv("FARM_CACHE", default_cache_path)
  
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  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 = FARM_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 = FARM_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, proxies=None):
      req = requests.get(url, stream=True, proxies=proxies)
      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 = FARM_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.
      if url.startswith("s3://"):
          etag = s3_etag(url)
      else:
          try:
              response = requests.head(url, allow_redirects=True)
              if response.status_code != 200:
                  etag = None
              else:
                  etag = response.headers.get("ETag")
          except EnvironmentError:
              etag = None
  
      if sys.version_info[0] == 2 and etag is not None:
          etag = etag.decode("utf-8")
      filename = url_to_filename(url, etag)
  
      # get cache path to put the file
      cache_path = os.path.join(cache_dir, filename)
  
      # If we don't have a connection (etag is None) and can't identify the file
      # try to get the last downloaded one
      if not os.path.exists(cache_path) and etag is None:
          matching_files = fnmatch.filter(os.listdir(cache_dir), filename + ".*")
          matching_files = list(filter(lambda s: not s.endswith(".json"), matching_files))
          if matching_files:
              cache_path = os.path.join(cache_dir, matching_files[-1])
  
      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") as meta_file:
                  output_string = json.dumps(meta)
                  if sys.version_info[0] == 2 and isinstance(output_string, str):
                      output_string = unicode(
                          output_string, "utf-8"
                      )  # The beauty of python 2
                  meta_file.write(output_string)
  
              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
  
  
  def read_config(path, flattend=False):
      if path:
          with open(path) as json_data_file:
              conf_args = json.load(json_data_file)
      else:
          raise ValueError("No config provided for classifier")
  
      def getArgValue(arg):
          if "value" not in arg:
              logger.error(
                  "Only depth 2 config files supported. Failed to convert: %s" % str(arg)
              )
          return arg["value"] if (arg["value"] is not None) else arg["default"]
  
      # flatten last part of config, take either value or default as value
      for gk, gv in conf_args.items():
          for k, v in gv.items():
              if isinstance(getArgValue(v), dict):
                  logger.error("Config is too deeply nested, at %s" % str(v))
              conf_args[gk][k] = getArgValue(v)
  
      # DotMap for making nested dictionary accessible through dot notation
      flat_args = dict(
          conf_args["general"],
          **conf_args["task"],
          **conf_args["parameter"],
          **conf_args["logging"],
      )
      if flattend:
          args = DotMap(flat_args, _dynamic=False)
      else:
          args = DotMap(conf_args, _dynamic=False)
  
      return args
  
  
  def unnestConfig(config, flattened=False):
      """
      This function creates a list of config files for evaluating parameters with different values. If a config parameter
      is of type list this list is iterated over and a config object without lists is returned. Can handle lists inside any
      number of parameters.
  
      Can handle shallow or nested (one level) configs
      """
      nestedKeys = []
      nestedVals = []
      if flattened:
          for k, v in config.items():
              if isinstance(v, list):
                  if k != "layer_dims":  # exclude layer dims, since it is already a list
                      nestedKeys.append(k)
                      nestedVals.append(v)
      else:
          for gk, gv in config.items():
              if(gk != "task"):
                  for k, v in gv.items():
                      if isinstance(v, list):
                          if isinstance(v, list):
                              if (
                                  k != "layer_dims"
                              ):  # exclude layer dims, since it is already a list
                                  nestedKeys.append([gk, k])
                                  nestedVals.append(v)
                          elif isinstance(v, dict):
                              logger.error("Config too deep!")
  
      if len(nestedKeys) == 0:
          unnestedConfig = [config]
      else:
          if flattened:
              logger.info("Nested config at parameters: %s" % (", ".join(nestedKeys)))
          else:
              logger.info(
                  "Nested config at parameters: %s"
                  % (", ".join(".".join(x) for x in nestedKeys))
              )
          unnestedConfig = []
          mesh = np.meshgrid(
              *nestedVals
          )  # get all combinations, each dimension corresponds to one parameter type
          # flatten mesh into shape: [num_parameters, num_combinations] so we can iterate in 2d over any paramter combinations
          mesh = [x.flatten() for x in mesh]
  
          # loop over all combinations
          for i in range(len(mesh[0])):
              tempconfig = config.copy()
              for j, k in enumerate(nestedKeys):
                  if isinstance(k, str):
                      tempconfig[k] = mesh[j][
                          i
                      ]  # get ith val of correct param value and overwrite original config
                  elif len(k) == 2:
                      tempconfig[k[0]][k[1]] = mesh[j][i]  # set nested dictionary keys
                  else:
                      logger.error("Config too deep!")
              unnestedConfig.append(tempconfig)
  
      return unnestedConfig
  
apis: torch.hub._get_torch_home numpy.meshgrid