content: """
  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.
  """
  
  import os
  import logging
  import shutil
  import tempfile
  import json
  from urllib.parse import urlparse
  from pathlib import Path
  from typing import Optional, Tuple, Union, IO, Callable, Set
  from hashlib import sha256
  from functools import wraps
  
  from tqdm import tqdm
  
  import boto3
  from botocore.exceptions import ClientError
  import requests
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
                                                 Path.home() / '.pytorch_pretrained_bert'))
  
  
  def url_to_filename(url: str, etag: str = None) -> str:
      """
      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: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
      """
      Return the url and etag (which may be ``None``) stored for `filename`.
      Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
      """
      if cache_dir is None:
          cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
      if 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 FileNotFoundError("file {} not found".format(cache_path))
  
      meta_path = cache_path + '.json'
      if not os.path.exists(meta_path):
          raise FileNotFoundError("file {} not found".format(meta_path))
  
      with open(meta_path) as meta_file:
          metadata = json.load(meta_file)
      url = metadata['url']
      etag = metadata['etag']
  
      return url, etag
  
  
  def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
      """
      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_BERT_CACHE
      if isinstance(url_or_filename, Path):
          url_or_filename = str(url_or_filename)
      if 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 FileNotFoundError("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: str) -> Tuple[str, str]:
      """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: Callable):
      """
      Wrapper function for s3 requests in order to create more helpful error
      messages.
      """
  
      @wraps(func)
      def wrapper(url: str, *args, **kwargs):
          try:
              return func(url, *args, **kwargs)
          except ClientError as exc:
              if int(exc.response["Error"]["Code"]) == 404:
                  raise FileNotFoundError("file {} not found".format(url))
              else:
                  raise
  
      return wrapper
  
  
  @s3_request
  def s3_etag(url: str) -> Optional[str]:
      """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: str, temp_file: IO) -> None:
      """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: str, temp_file: IO) -> None:
      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: str, cache_dir: Union[str, Path] = None) -> str:
      """
      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_BERT_CACHE
      if isinstance(cache_dir, Path):
          cache_dir = str(cache_dir)
  
      os.makedirs(cache_dir, exist_ok=True)
  
      # Get eTag to add to filename, if it exists.
      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') 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: str) -> Set[str]:
      '''
      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: str, dot=True, lower: bool = True):
      ext = os.path.splitext(path)[1]
      ext = ext if dot else ext[1:]
      return ext.lower() if lower else ext
  