old_contents: from torchtext._internal.module_utils import is_module_available
  from typing import Union, Tuple
  if is_module_available("torchdata"):
      from torchdata.datapipes.iter import FileOpener, GDriveReader, IterableWrapper
  
  from torchtext.data.datasets_utils import (
      _wrap_split_argument,
      _add_docstring_header,
      _create_dataset_directory,
  )
  
  import os
  
  URL = 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM'
  
  MD5 = 'fe39f8b653cada45afd5792e0f0e8f9b'
  
  NUM_LINES = {
      'train': 3600000,
      'test': 400000,
  }
  
  _PATH = 'amazon_review_polarity_csv.tar.gz'
  
  _EXTRACTED_FILES = {
      'train': f'{os.sep}'.join(['amazon_review_polarity_csv', 'train.csv']),
      'test': f'{os.sep}'.join(['amazon_review_polarity_csv', 'test.csv']),
  }
  
  
  DATASET_NAME = "AmazonReviewPolarity"
  
  
  @_add_docstring_header(num_lines=NUM_LINES, num_classes=2)
  @_create_dataset_directory(dataset_name=DATASET_NAME)
  @_wrap_split_argument(("train", "test"))
  def AmazonReviewPolarity(root: str, split: Union[Tuple[str], str]):
      # TODO Remove this after removing conditional dependency
      if not is_module_available("torchdata"):
          raise ModuleNotFoundError("Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`")
  
      url_dp = IterableWrapper([URL])
      cache_dp = url_dp.on_disk_cache(
          filepath_fn=lambda x: os.path.join(root, _PATH), hash_dict={os.path.join(root, _PATH): MD5}, hash_type="md5"
      )
      cache_dp = GDriveReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True)
      cache_dp = FileOpener(cache_dp, mode="b")
      extracted_files = cache_dp.read_from_tar()
      filter_extracted_files = extracted_files.filter(lambda x: split in x[0])
      return filter_extracted_files.parse_csv().map(fn=lambda t: (int(t[0]), ' '.join(t[1:])))
  
new_contents: from torchtext._internal.module_utils import is_module_available
  from typing import Union, Tuple
  if is_module_available("torchdata"):
      from torchdata.datapipes.iter import FileOpener, GDriveReader, IterableWrapper
  
  from torchtext.data.datasets_utils import (
      _wrap_split_argument,
      _add_docstring_header,
      _create_dataset_directory,
  )
  
  import os
  
  URL = 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM'
  
  MD5 = 'fe39f8b653cada45afd5792e0f0e8f9b'
  
  NUM_LINES = {
      'train': 3600000,
      'test': 400000,
  }
  
  _PATH = 'amazon_review_polarity_csv.tar.gz'
  
  _EXTRACTED_FILES = {
      'train': f'{os.sep}'.join(['amazon_review_polarity_csv', 'train.csv']),
      'test': f'{os.sep}'.join(['amazon_review_polarity_csv', 'test.csv']),
  }
  
  
  DATASET_NAME = "AmazonReviewPolarity"
  
  
  @_add_docstring_header(num_lines=NUM_LINES, num_classes=2)
  @_create_dataset_directory(dataset_name=DATASET_NAME)
  @_wrap_split_argument(("train", "test"))
  def AmazonReviewPolarity(root: str, split: Union[Tuple[str], str]):
      # TODO Remove this after removing conditional dependency
      if not is_module_available("torchdata"):
          raise ModuleNotFoundError("Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`")
  
      url_dp = IterableWrapper([URL])
      cache_dp = url_dp.on_disk_cache(
          filepath_fn=lambda x: os.path.join(root, _PATH), hash_dict={os.path.join(root, _PATH): MD5}, hash_type="md5"
      )
      cache_dp = GDriveReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True)
      cache_dp = FileOpener(cache_dp, mode="b")
      extracted_files = cache_dp.read_from_tar()
      filter_extracted_files = extracted_files.filter(lambda x: _EXTRACTED_FILES[split] in x[0])
      return filter_extracted_files.parse_csv().map(fn=lambda t: (int(t[0]), ' '.join(t[1:])))
  
subject: Fix filter logic (#1505)