from torchtext.data.datasets_utils import (
    _RawTextIterableDataset,
    _wrap_split_argument,
    _add_docstring_header,
    _download_extract_validate,
    _create_dataset_directory,
    _create_data_from_csv,
)
import os
import logging

URL = 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbUkVqNEszd0pHaFE'

MD5 = '0c1700ba70b73f964dd8de569d3fd03e'

NUM_LINES = {
    'train': 450000,
    'test': 60000,
}

_PATH = 'sogou_news_csv.tar.gz'

_EXTRACTED_FILES = {
    'train': f'{os.sep}'.join(['sogou_news_csv', 'train.csv']),
    'test': f'{os.sep}'.join(['sogou_news_csv', 'test.csv']),
}

_EXTRACTED_FILES_MD5 = {
    'train': "f36156164e6eac2feda0e30ad857eef0",
    'test': "59e493c41cee050329446d8c45615b38"
}

DATASET_NAME = "SogouNews"


@_add_docstring_header(num_lines=NUM_LINES, num_classes=5)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(('train', 'test'))
def SogouNews(root, split):
    path = _download_extract_validate(root, URL, MD5, os.path.join(root, _PATH), os.path.join(root, _EXTRACTED_FILES[split]),
                                      _EXTRACTED_FILES_MD5[split], hash_type="md5")
    logging.info('Creating {} data'.format(split))
    return _RawTextIterableDataset(DATASET_NAME, NUM_LINES[split],
                                   _create_data_from_csv(path))

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_Dbh9QhbUkVqNEszd0pHaFE'

MD5 = '0c1700ba70b73f964dd8de569d3fd03e'

NUM_LINES = {
    'train': 450000,
    'test': 60000,
}

_PATH = 'sogou_news_csv.tar.gz'

_EXTRACTED_FILES = {
    'train': f'{os.sep}'.join(['sogou_news_csv', 'train.csv']),
    'test': f'{os.sep}'.join(['sogou_news_csv', 'test.csv']),
}

_EXTRACTED_FILES_MD5 = {
    'train': "f36156164e6eac2feda0e30ad857eef0",
    'test': "59e493c41cee050329446d8c45615b38"
}

DATASET_NAME = "SogouNews"


@_add_docstring_header(num_lines=NUM_LINES, num_classes=5)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def SogouNews(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:])))

migrate SogouNews to datapipes. (#1503)