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_Dbh9QhbZlU4dXhHTFhZQU0'

MD5 = 'f7ddfafed1033f68ec72b9267863af6c'

NUM_LINES = {
    'train': 650000,
    'test': 50000,
}

_PATH = 'yelp_review_full_csv.tar.gz'

DATASET_NAME = "YelpReviewFull"

_EXTRACTED_FILES = {
    'train': os.path.join('yelp_review_full_csv', 'train.csv'),
    'test': os.path.join('yelp_review_full_csv', 'test.csv'),
}


@_add_docstring_header(num_lines=NUM_LINES, num_classes=5)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def YelpReviewFull(root: str, 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])

    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:])))

@@ -901,23 +901,23 @@
 nt((
-'
+%22
 train
-', '
+%22, %22
 test
-'
+%22
 ))%0Ad
@@ -1209,24 +1209,35 @@
 %0A%0A    cache_
+compressed_
 dp = url_dp.
@@ -1388,24 +1388,35 @@
 )%0A    cache_
+compressed_
 dp = GDriveR
@@ -1431,11 +1431,68 @@
 che_
+compressed_dp)%0A    cache_compressed_dp = cache_compressed_
 dp
-)
 .end
@@ -1534,16 +1534,17 @@
 n=True)%0A
+%0A
     cach
@@ -1545,16 +1545,158 @@
   cache_
+decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=lambda x: os.path.join(root, _EXTRACTED_FILES%5Bsplit%5D))%0A    cache_decompressed_
 dp = Fil
@@ -1709,16 +1709,29 @@
 r(cache_
+decompressed_
 dp, mode
@@ -1740,101 +1740,73 @@
 b%22)%0A
-%0A
     
-extracted_files = cache_dp.read_from_tar()%0A%0A    filter_extracted_files = extracted_files
+cache_decompressed_dp = cache_decompressed_dp.read_from_tar()
 .fil
@@ -1856,42 +1856,181 @@
 0%5D)%0A
-%0A
     
-return filter_extracted_files
+cache_decompressed_dp = cache_decompressed_dp.end_caching(mode=%22wb%22, same_filepath_fn=True)%0A%0A    data_dp = FileOpener(cache_decompressed_dp, mode=%22b%22)%0A    return data_dp
 .par

add double caching for yelp full to speed up extracted reading. (#1529)