

'''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					:			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__			(    a__=True		):




     """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=a   )   )
class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = None
 lowerCAmelCase__       = None
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					str:


      """simple docstring"""




      with TemporaryDirectory() as tmp_dir:
           __SCREAMING_SNAKE_CASE      = dataset_module_factory(__SCREAMING_SNAKE_CASE   ,				cache_dir=__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE      = import_main_class(dataset_module.module_path   ,				dataset=__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE      = builder_cls(
               cache_dir=__SCREAMING_SNAKE_CASE   ,				config_name=__SCREAMING_SNAKE_CASE   ,				hash=dataset_module.hash   ,				)

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







@pytest.mark.integration
def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = tmp_path_factory.mktemp("""test_hf_gcp"""		) / """test_wikipedia_simple"""
     __SCREAMING_SNAKE_CASE      = dataset_module_factory("""wikipedia""" ,       cache_dir=a__		)
     __SCREAMING_SNAKE_CASE      = import_main_class(dataset_module.module_path		)
     __SCREAMING_SNAKE_CASE      = builder_cls(
         cache_dir=a__ ,       config_name="""20220301.frr""" ,       hash=dataset_module.hash ,       )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     __SCREAMING_SNAKE_CASE      = None
     builder_instance.download_and_prepare()
     __SCREAMING_SNAKE_CASE      = builder_instance.as_dataset()
     assert ds







@pytest.mark.integration
def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = dataset_module_factory("""wikipedia""" ,       cache_dir=a__		)
     __SCREAMING_SNAKE_CASE      = import_main_class(dataset_module.module_path ,       dataset=a__		)
     __SCREAMING_SNAKE_CASE      = builder_cls(
         cache_dir=a__ ,       config_name="""20220301.frr""" ,       hash=dataset_module.hash ,       )
     __SCREAMING_SNAKE_CASE      = builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(a__ ,       a__		)
     assert "train" in ds
     assert isinstance(ds["""train"""] ,       a__		)
     assert next(iter(ds["""train"""]		)		)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






