




'''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


A									=[
    {'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 		snake_case_							(_a			:							Optional[int]=True  ):






    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=_a				)				)
class     _a						(			_a				):
       __a       :  List[Any]            =      None
       __a       :  Tuple            =      None
       def 					A   (			self		:			str     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			str       ):



           '''simple docstring'''

           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 		snake_case_							(_a			:							Dict  ):
    UpperCAmelCase     =	tmp_path_factory.mktemp('''test_hf_gcp'''  ) / '''test_wikipedia_simple'''
    UpperCAmelCase     =	dataset_module_factory('''wikipedia'''					,     cache_dir=UpperCAmelCase__  )
    UpperCAmelCase     =	import_main_class(dataset_module.module_path  )
    UpperCAmelCase     =	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
    UpperCAmelCase     =	None
    builder_instance.download_and_prepare()
    UpperCAmelCase     =	builder_instance.as_dataset()
    assert ds







@pytest.mark.integration
def 		snake_case_							(_a			:							Dict  ):
    UpperCAmelCase     =	dataset_module_factory('''wikipedia'''					,     cache_dir=UpperCAmelCase__  )
    UpperCAmelCase     =	import_main_class(dataset_module.module_path					,     dataset=UpperCAmelCase__  )
    UpperCAmelCase     =	builder_cls(
        cache_dir=UpperCAmelCase__					,     config_name='''20220301.frr'''					,     hash=dataset_module.hash					,     )
    UpperCAmelCase     =	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''']  )  )







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

   A									=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




