





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__: Optional[int]							   =						[
    {'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__     :			Optional[int]=True    )->Union[str, Any]:
       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__ ):
 __SCREAMING_SNAKE_CASE							=      None
 __SCREAMING_SNAKE_CASE							=      None



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        with TemporaryDirectory() as tmp_dir:
               A__		   =		dataset_module_factory(__lowerCamelCase,cache_dir=__lowerCamelCase					)

               A__		   =		import_main_class(dataset_module.module_path,dataset=__lowerCamelCase					)

               A__		   =		builder_cls(
                   cache_dir=__lowerCamelCase,config_name=__lowerCamelCase,hash=dataset_module.hash,)

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


@pytest.mark.integration
def       UpperCamelCase__(			UpperCamelCase__     :			int    )->str:
       A__		   =		tmp_path_factory.mktemp('''test_hf_gcp'''    ) / '''test_wikipedia_simple'''
       A__		   =		dataset_module_factory('''wikipedia'''					,	cache_dir=UpperCamelCase__    )
       A__		   =		import_main_class(dataset_module.module_path    )
       A__		   =		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
       A__		   =		None
       builder_instance.download_and_prepare()
       A__		   =		builder_instance.as_dataset()
       assert ds


@pytest.mark.integration
def       UpperCamelCase__(			UpperCamelCase__     :			List[str]    )->Optional[Any]:
       A__		   =		dataset_module_factory('''wikipedia'''					,	cache_dir=UpperCamelCase__    )
       A__		   =		import_main_class(dataset_module.module_path					,	dataset=UpperCamelCase__    )
       A__		   =		builder_cls(
           cache_dir=UpperCamelCase__					,	config_name='''20220301.frr'''					,	hash=dataset_module.hash					,	)
       A__		   =		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''']    )    )












a__: str							   =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
       from .utils import rich





