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, HttpReader, IterableWrapper

from torchtext.data.datasets_utils import (
    _wrap_split_argument,
    _add_docstring_header,
    _create_dataset_directory,
)
import os

URL = {
    'train': "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv",
    'test': "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv",
}

MD5 = {
    'train': "b1a00f826fdfbd249f79597b59e1dc12",
    'test': "d52ea96a97a2d943681189a97654912d",
}

NUM_LINES = {
    'train': 120000,
    'test': 7600,
}

DATASET_NAME = "AG_NEWS"


@_add_docstring_header(num_lines=NUM_LINES, num_classes=4)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def AG_NEWS(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[split]])
    cache_dp = url_dp.on_disk_cache(
        filepath_fn=lambda x: os.path.join(root, split + ".csv"),
        hash_dict={os.path.join(root, split + ".csv"): MD5[split]},
        hash_type="md5"
    )
    cache_dp = HttpReader(cache_dp)
    cache_dp = cache_dp.end_caching(mode="wb", same_filepath_fn=True)
    cache_dp = FileOpener(cache_dp, mode="r")
    return cache_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))

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, HttpReader, IterableWrapper

from torchtext.data.datasets_utils import (
    _wrap_split_argument,
    _add_docstring_header,
    _create_dataset_directory,
)
import os

URL = {
    'train': "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv",
    'test': "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv",
}

MD5 = {
    'train': "b1a00f826fdfbd249f79597b59e1dc12",
    'test': "d52ea96a97a2d943681189a97654912d",
}

NUM_LINES = {
    'train': 120000,
    'test': 7600,
}

DATASET_NAME = "AG_NEWS"


@_add_docstring_header(num_lines=NUM_LINES, num_classes=4)
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "test"))
def AG_NEWS(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[split]])
    cache_dp = url_dp.on_disk_cache(
        filepath_fn=lambda x: os.path.join(root, split + ".csv"),
        hash_dict={os.path.join(root, split + ".csv"): MD5[split]},
        hash_type="md5"
    )
    cache_dp = HttpReader(cache_dp)
    cache_dp = cache_dp.end_caching(mode="wb", same_filepath_fn=True)
    cache_dp = FileOpener(cache_dp, mode="r")
    return cache_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))
