






"""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        a__     (				lowerCAmelCase__=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 				lowercase__       (   A__				):







       '''simple docstring'''


       UpperCamelCase						=					None
       UpperCamelCase						=					None



       def 					lowercase__    (							self      :  Any		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Any   )			->						Dict:


             '''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        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							tmp_path_factory.mktemp("test_hf_gcp"				) / """test_wikipedia_simple"""
      UpperCAmelCase_      			=							dataset_module_factory("wikipedia"     ,      cache_dir=lowercase__				)
      UpperCAmelCase_      			=							import_main_class(dataset_module.module_path				)
      UpperCAmelCase_      			=							builder_cls(
          cache_dir=lowercase__     ,      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        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							dataset_module_factory("wikipedia"     ,      cache_dir=lowercase__				)
      UpperCAmelCase_      			=							import_main_class(dataset_module.module_path     ,      dataset=lowercase__				)
      UpperCAmelCase_      			=							builder_cls(
          cache_dir=lowercase__     ,      config_name="20220301.frr"     ,      hash=dataset_module.hash     ,      )
      UpperCAmelCase_      			=							builder_instance.as_streaming_dataset()
      assert ds
      assert isinstance(lowercase__     ,      lowercase__				)
      assert "train" in ds
      assert isinstance(ds["train"]     ,      lowercase__				)
      assert next(iter(ds["train"]				)				)









"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


lowerCamelCase       	=						logging.get_logger(__name__)
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       def __init__(							self      :  Union[str, Any]		,    *_UpperCAmelCase      :  List[Any]		,    **_UpperCAmelCase      :  Union[str, Any]   )			->						None:


             '''simple docstring'''




             warnings.warn(
                 "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use FlavaImageProcessor instead."		,    _UpperCAmelCase		,    )
             super().__init__(*_UpperCAmelCase		,    **_UpperCAmelCase   )




