


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


lowercase_			       =      [
    {'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__	(    SCREAMING_SNAKE_CASE__=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=UpperCamelCase__       )       )
class 			A_   (			UpperCamelCase__       ):




      '''simple docstring'''


      __snake_case   			=  None
      __snake_case   			=  None




      def 	_snake_case      (							self:			List[Any]		, a:			str		, a:			Any							):
       with TemporaryDirectory() as tmp_dir:
        __lowerCamelCase						:				int						   =		dataset_module_factory(__lowerCamelCase		, cache_dir=__lowerCamelCase							)

        __lowerCamelCase						:				Optional[int]						   =		import_main_class(dataset_module.module_path		, dataset=__lowerCamelCase							)

        __lowerCamelCase						:				Optional[Any]						   =		builder_cls(
            cache_dir=__lowerCamelCase		, config_name=__lowerCamelCase		, hash=dataset_module.hash		, )

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



@pytest.mark.integration
def  UpperCamelCase__	(    SCREAMING_SNAKE_CASE__		):
 __lowerCamelCase						:				Any						   =		tmp_path_factory.mktemp('test_hf_gcp'		) / 'test_wikipedia_simple'
 __lowerCamelCase						:				Optional[int]						   =		dataset_module_factory('wikipedia'     ,			cache_dir=UpperCamelCase__		)
 __lowerCamelCase						:				List[Any]						   =		import_main_class(dataset_module.module_path		)
 __lowerCamelCase						:				Optional[int]						   =		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
 __lowerCamelCase						:				Any						   =		None
 builder_instance.download_and_prepare()
 __lowerCamelCase						:				List[str]						   =		builder_instance.as_dataset()
 assert ds



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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





