

'''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


__lowerCamelCase										=			[
    {'''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__			(					UpperCAmelCase__=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=__SCREAMING_SNAKE_CASE	)	)
class     A__					(					__SCREAMING_SNAKE_CASE	):
    lowercase		    =						None
    lowercase		    =						None



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Optional[int]:





           '''simple docstring'''



           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__			(					UpperCAmelCase__ )	->	Dict:
       A_		    =					tmp_path_factory.mktemp("""test_hf_gcp""" ) / "test_wikipedia_simple"
       A_		    =					dataset_module_factory("""wikipedia""",				cache_dir=__a )
       A_		    =					import_main_class(dataset_module.module_path )
       A_		    =					builder_cls(
           cache_dir=__a,				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__			(					UpperCAmelCase__ )	->	List[str]:
       A_		    =					dataset_module_factory("""wikipedia""",				cache_dir=__a )
       A_		    =					import_main_class(dataset_module.module_path,				dataset=__a )
       A_		    =					builder_cls(
           cache_dir=__a,				config_name="""20220301.frr""",				hash=dataset_module.hash,				)
       A_		    =					builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(__a,				__a )
       assert "train" in ds
       assert isinstance(ds["""train"""],				__a )
       assert next(iter(ds["""train"""] ) )







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

