





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


_UpperCAmelCase		:       List[str]						    =	[
    {"""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  __lowerCamelCase     (			UpperCamelCase__=True				):







							'''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=snake_case_      )      )
class 			lowercase    (	snake_case_      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      None
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      None




				def    a							( self				,       snake_case				,       snake_case	):





											with TemporaryDirectory() as tmp_dir:
																		snake_case_          =   dataset_module_factory(snake_case				,       cache_dir=snake_case	)

																		snake_case_          =   import_main_class(dataset_module.module_path				,       dataset=snake_case	)

																		snake_case_          =   builder_cls(
																		    cache_dir=snake_case				,       config_name=snake_case				,       hash=dataset_module.hash				,       )

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





@pytest.mark.integration
def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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





@pytest.mark.integration
def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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







import requests

_UpperCAmelCase		:       Union[str, Any]						    =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   requests.get(_NEWS_API + bbc_news_api_key				).json()
							# each article in the list is a dict
							for i, article in enumerate(bbc_news_page['articles']       ,							1				):
														print(F'''{i}.) {article["title"]}'''				)


if __name__ == "__main__":
					fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




