'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     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_				(					snake_case_       : Union[str, Any]=True			)    ->      Union[str, Any]:


     '''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=UpperCamelCase__	)	)
class        __A      ( UpperCamelCase__	):
    a__       :						Tuple          =  None
    a__       :						List[Any]          =  None



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    Optional[int]   ,			__a    :    Optional[Any] ):
         with TemporaryDirectory() as tmp_dir:
              UpperCAmelCase_           =   dataset_module_factory(__a   ,			cache_dir=__a )

              UpperCAmelCase_           =   import_main_class(dataset_module.module_path   ,			dataset=__a )

              UpperCAmelCase_           =   builder_cls(
                  cache_dir=__a   ,			config_name=__a   ,			hash=dataset_module.hash   ,			)

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





@pytest.mark.integration
def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      List[Any]:


     '''simple docstring'''




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






@pytest.mark.integration
def 	lowerCAmelCase_				(					snake_case_       : List[Any]			)    ->      Dict:


     '''simple docstring'''




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










from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
 lowerCamelCase				:		int     =   input("Enter image url: ").strip()
 print(F"""Downloading image from {url} ...""")
 lowerCamelCase				:		Tuple     =   BeautifulSoup(requests.get(url).content, "html.parser")
 # The image URL is in the content field of the first meta tag with property og:image
 lowerCamelCase				:		int     =   soup.find("meta", {"property": "og:image"})["content"]
 lowerCamelCase				:		Dict     =   requests.get(image_url).content
 lowerCamelCase				:		Optional[int]     =   F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
 with open(file_name, "wb") as fp:
  fp.write(image_data)
 print(F"""Done. Image saved to disk as {file_name}.""")



