


import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowercase__				     =		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]






def __UpperCamelCase   (			__lowerCamelCase		: Tuple=True				)      ->     Tuple:







     '''simple docstring'''






     if with_config:
          return [
              {
                  "testcase_name": d["dataset"] + "/" + d["config_name"],
                  "dataset": d["dataset"],
                  "config_name": d["config_name"],
              }
              for d in DATASETS_ON_HF_GCP
          ]
     else:
          return [
              {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
          ]





@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=lowerCamelCase__ ) )
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				None
     UpperCAmelCase 					=				None






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    int:
          with TemporaryDirectory() as tmp_dir:
               _a						=		dataset_module_factory(__UpperCamelCase     ,							cache_dir=__UpperCamelCase		)

               _a						=		import_main_class(dataset_module.module_path     ,							dataset=__UpperCamelCase		)

               _a						=		builder_cls(
                   cache_dir=__UpperCamelCase     ,							config_name=__UpperCamelCase     ,							hash=dataset_module.hash     ,							)

               _a						=		"/".join(
                   [
                       HF_GCP_BASE_URL,
                       builder_instance._relative_data_dir(with_hash=__UpperCamelCase		).replace(os.sep     ,							"/"		),
                       config.DATASET_INFO_FILENAME,
                   ]		)
               _a						=		cached_path(__UpperCamelCase     ,							cache_dir=__UpperCamelCase		)
               self.assertTrue(os.path.exists(__UpperCamelCase		)		)







@pytest.mark.integration
def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]				)      ->     List[Any]:







     '''simple docstring'''






     _a						=		tmp_path_factory.mktemp("test_hf_gcp"				) / "test_wikipedia_simple"
     _a						=		dataset_module_factory("wikipedia"      ,  cache_dir=__lowerCamelCase				)
     _a						=		import_main_class(dataset_module.module_path				)
     _a						=		builder_cls(
         cache_dir=__lowerCamelCase      ,  config_name="20220301.frr"      ,  hash=dataset_module.hash      ,  )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     _a						=		None
     builder_instance.download_and_prepare()
     _a						=		builder_instance.as_dataset()
     assert ds






@pytest.mark.integration
def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]				)      ->     Optional[Any]:







     '''simple docstring'''






     _a						=		dataset_module_factory("wikipedia"      ,  cache_dir=__lowerCamelCase				)
     _a						=		import_main_class(dataset_module.module_path      ,  dataset=__lowerCamelCase				)
     _a						=		builder_cls(
         cache_dir=__lowerCamelCase      ,  config_name="20220301.frr"      ,  hash=dataset_module.hash      ,  )
     _a						=		builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(__lowerCamelCase      ,  __lowerCamelCase				)
     assert "train" in ds
     assert isinstance(ds["train"]      ,  __lowerCamelCase				)
     assert next(iter(ds["train"]				)				)





'''simple docstring'''

from __future__ import annotations

import requests






def __UpperCamelCase   (			__lowerCamelCase		: str				)      ->     dict:







     '''simple docstring'''






     _a						=		F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
     return requests.get(__lowerCamelCase				).json()






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     list[dict]:







     '''simple docstring'''






     _a						=		"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _a						=		requests.get(__lowerCamelCase				).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase				) for story_id in story_ids]






def __UpperCamelCase   (			__lowerCamelCase		: int = 10				)      ->     str:







     '''simple docstring'''






     _a						=		hackernews_top_stories(__lowerCamelCase				)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase				) for story in stories				)


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())

