





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							=   [
    {"""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					(      lowercase__						:							Dict=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=__SCREAMING_SNAKE_CASE   )   )
class 		__a				(						__SCREAMING_SNAKE_CASE   ):
       __snake_case    : Any   =		None
       __snake_case    : Optional[int]   =		None



       def 			A				(  self						: Union[str, Any]       ,      UpperCAmelCase						: int       ,      UpperCAmelCase						: List[str]      ):

           with TemporaryDirectory() as tmp_dir:
               lowerCAmelCase_       :						str							= dataset_module_factory(A_       ,      cache_dir=A_      )

               lowerCAmelCase_       :						Dict							= import_main_class(dataset_module.module_path       ,      dataset=A_      )

               lowerCAmelCase_       :						Any							= builder_cls(
                   cache_dir=A_       ,      config_name=A_       ,      hash=dataset_module.hash       ,      )

               lowerCAmelCase_       :						Union[str, Any]							= """/""".join(
                   [
                       HF_GCP_BASE_URL,
                       builder_instance._relative_data_dir(with_hash=A_      ).replace(os.sep       ,      """/"""      ),
                       config.DATASET_INFO_FILENAME,
                   ]      )
               lowerCAmelCase_       :						List[Any]							= cached_path(A_       ,      cache_dir=A_      )
               self.assertTrue(os.path.exists(A_      )      )






@pytest.mark.integration
def       __UpperCamelCase					(      lowercase__						:							Any		)						->   List[str]:
    '''simple docstring'''





    lowerCAmelCase_       :						Optional[Any]							= tmp_path_factory.mktemp("""test_hf_gcp"""		) / """test_wikipedia_simple"""
    lowerCAmelCase_       :						List[Any]							= dataset_module_factory("""wikipedia"""		,     cache_dir=lowerCAmelCase_		)
    lowerCAmelCase_       :						List[str]							= import_main_class(dataset_module.module_path		)
    lowerCAmelCase_       :						int							= 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
    lowerCAmelCase_       :						List[str]							= None
    builder_instance.download_and_prepare()
    lowerCAmelCase_       :						Optional[int]							= builder_instance.as_dataset()
    assert ds

@pytest.mark.integration
def       __UpperCamelCase					(      lowercase__						:							Union[str, Any]		)						->   Dict:
    '''simple docstring'''





    lowerCAmelCase_       :						Dict							= dataset_module_factory("""wikipedia"""		,     cache_dir=lowerCAmelCase_		)
    lowerCAmelCase_       :						Union[str, Any]							= import_main_class(dataset_module.module_path		,     dataset=lowerCAmelCase_		)
    lowerCAmelCase_       :						Union[str, Any]							= builder_cls(
        cache_dir=lowerCAmelCase_		,     config_name="""20220301.frr"""		,     hash=dataset_module.hash		,     )
    lowerCAmelCase_       :						Dict							= 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"""]		)		)









class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			name
        SCREAMING_SNAKE_CASE__											=			value
        SCREAMING_SNAKE_CASE__											=			weight



    def __repr__(       self     ):





        '''simple docstring'''

        return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.name



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.weight




    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.value / self.weight





def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[]
    for i in range(len(lowerCAmelCase_	)	):
        menu.append(Things(name[i] ,							value[i] ,							weight[i]	)	)
    return menu


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			sorted(lowerCAmelCase_ ,							key=lowerCAmelCase_ ,							reverse=lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			0.0, 0.0
    for i in range(len(lowerCAmelCase_	)	):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]	)
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)


def 							__snake_case							(    )				->						str:
    pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
