old_contents: import os
  from typing import Union, Tuple
  
  from torchtext._internal.module_utils import is_module_available
  from torchtext.data.datasets_utils import (
      _wrap_split_argument,
      _add_docstring_header,
      _create_dataset_directory,
  )
  
  if is_module_available("torchdata"):
      from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
  
  URL = {
      "train": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt",
      "test": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt",
      "valid": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt",
  }
  
  MD5 = {
      "train": "f26c4b92c5fdc7b3f8c7cdcb991d8420",
      "valid": "aa0affc06ff7c36e977d7cd49e3839bf",
      "test": "8b80168b89c18661a38ef683c0dc3721",
  }
  
  NUM_LINES = {
      "train": 42068,
      "valid": 3370,
      "test": 3761,
  }
  
  DATASET_NAME = "PennTreebank"
  
  
  @_add_docstring_header(num_lines=NUM_LINES)
  @_create_dataset_directory(dataset_name=DATASET_NAME)
  @_wrap_split_argument(("train", "valid", "test"))
  def PennTreebank(root, split: Union[Tuple[str], str]):
      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[split]])
      cache_dp = url_dp.on_disk_cache(
          filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
          hash_dict={os.path.join(root, os.path.basename(URL[split])): MD5[split]},
          hash_type="md5",
      )
      cache_dp = HttpReader(cache_dp).end_caching(mode="w", same_filepath_fn=True)
      data_dp = FileOpener(cache_dp, mode="r")
      # remove single leading and trailing space from the dataset
      return data_dp.readlines(return_path=False).map(lambda t: t.strip())
  
new_contents: import os
  from typing import Union, Tuple
  
  from torchtext._internal.module_utils import is_module_available
  from torchtext.data.datasets_utils import (
      _wrap_split_argument,
      _add_docstring_header,
      _create_dataset_directory,
  )
  
  if is_module_available("torchdata"):
      from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
  
  URL = {
      "train": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt",
      "test": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt",
      "valid": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.valid.txt",
  }
  
  MD5 = {
      "train": "f26c4b92c5fdc7b3f8c7cdcb991d8420",
      "valid": "aa0affc06ff7c36e977d7cd49e3839bf",
      "test": "8b80168b89c18661a38ef683c0dc3721",
  }
  
  NUM_LINES = {
      "train": 42068,
      "valid": 3370,
      "test": 3761,
  }
  
  DATASET_NAME = "PennTreebank"
  
  
  @_add_docstring_header(num_lines=NUM_LINES)
  @_create_dataset_directory(dataset_name=DATASET_NAME)
  @_wrap_split_argument(("train", "valid", "test"))
  def PennTreebank(root, split: Union[Tuple[str], str]):
      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[split]])
      cache_dp = url_dp.on_disk_cache(
          filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
          hash_dict={os.path.join(root, os.path.basename(URL[split])): MD5[split]},
          hash_type="md5",
      )
      cache_dp = HttpReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True)
      data_dp = FileOpener(cache_dp, mode="r")
      # remove single leading and trailing space from the dataset
      return data_dp.readlines(return_path=False).map(lambda t: t.strip())
  
subject: fix penn dataset (#1552)