

'''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


UpperCamelCase_						:      Union[str, Any]       =						[
    {"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     (       _UpperCamelCase:   Dict=True   )						->			Tuple:

     """simple docstring"""




     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__							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						None
     SCREAMING_SNAKE_CASE_							:   Tuple       =						None






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[str]:



          with TemporaryDirectory() as tmp_dir:
               _snake_case							=				dataset_module_factory(a_ ,cache_dir=a_   )

               _snake_case							=				import_main_class(dataset_module.module_path ,dataset=a_   )

               _snake_case							=				builder_cls(
                   cache_dir=a_ ,config_name=a_ ,hash=dataset_module.hash ,)

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







@pytest.mark.integration
def      __a     (       _UpperCamelCase:   List[str]   )						->			Dict:

     """simple docstring"""




     _snake_case							=				tmp_path_factory.mktemp("test_hf_gcp"   ) / "test_wikipedia_simple"
     _snake_case							=				dataset_module_factory("wikipedia" ,						cache_dir=_UpperCamelCase   )
     _snake_case							=				import_main_class(dataset_module.module_path   )
     _snake_case							=				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
     _snake_case							=				None
     builder_instance.download_and_prepare()
     _snake_case							=				builder_instance.as_dataset()
     assert ds





@pytest.mark.integration
def      __a     (       _UpperCamelCase:   Dict   )						->			Dict:

     """simple docstring"""




     _snake_case							=				dataset_module_factory("wikipedia" ,						cache_dir=_UpperCamelCase   )
     _snake_case							=				import_main_class(dataset_module.module_path ,						dataset=_UpperCamelCase   )
     _snake_case							=				builder_cls(
         cache_dir=_UpperCamelCase ,						config_name="20220301.frr" ,						hash=dataset_module.hash ,						)
     _snake_case							=				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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


