






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


__a:     Union[str, Any]   					=					[
    {'''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       _SCREAMING_SNAKE_CASE (							__snake_case=True )				->						int:
       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 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  None
  _lowerCamelCase    =  None


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         with TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase							=    dataset_module_factory(lowerCamelCase       ,				cache_dir=lowerCamelCase							)

                _UpperCAmelCase							=    import_main_class(dataset_module.module_path       ,				dataset=lowerCamelCase							)

                _UpperCAmelCase							=    builder_cls(
                    cache_dir=lowerCamelCase       ,				config_name=lowerCamelCase       ,				hash=dataset_module.hash       ,				)

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




@pytest.mark.integration
def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       _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       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       _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"""] ) )





'''simple docstring'''
import numpy as np





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		np.array:
			return 1 / (1 + np.exp(-vector     ))





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		np.array:
			return vector * sigmoid(1.702 * vector     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




