

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


snake_case_			  = [
    {'''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__			(    SCREAMING_SNAKE_CASE_		:    Tuple=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=__snake_case     )     )
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Dict														=     None
    __lowerCamelCase						:       Union[str, Any]														=     None





    def       snake_case_				(      self						,   a						,   a):
         with TemporaryDirectory() as tmp_dir:
              lowercase__    :   Optional[Any]      =       dataset_module_factory(a						,   cache_dir=a)

              lowercase__    :   Optional[Any]      =       import_main_class(dataset_module.module_path						,   dataset=a)

              lowercase__    :   DatasetBuilder      =       builder_cls(
                  cache_dir=a						,   config_name=a						,   hash=dataset_module.hash						,   )

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



@pytest.mark.integration
def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





     lowercase__    :   int      =       tmp_path_factory.mktemp('test_hf_gcp'			) / 'test_wikipedia_simple'
     lowercase__    :   Tuple      =       dataset_module_factory('wikipedia'	,     cache_dir=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   str      =       import_main_class(dataset_module.module_path			)
     lowercase__    :   DatasetBuilder      =       builder_cls(
         cache_dir=SCREAMING_SNAKE_CASE_	,     config_name='20220301.frr'	,     hash=dataset_module.hash	,     )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     lowercase__    :   str      =       None
     builder_instance.download_and_prepare()
     lowercase__    :   str      =       builder_instance.as_dataset()
     assert ds

@pytest.mark.integration
def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





     lowercase__    :   List[Any]      =       dataset_module_factory('wikipedia'	,     cache_dir=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[str]      =       import_main_class(dataset_module.module_path	,     dataset=SCREAMING_SNAKE_CASE_			)
     lowercase__    :   DatasetBuilder      =       builder_cls(
         cache_dir=SCREAMING_SNAKE_CASE_	,     config_name='20220301.frr'	,     hash=dataset_module.hash	,     )
     lowercase__    :   str      =       builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)
     assert "train" in ds
     assert isinstance(ds['train']	,     SCREAMING_SNAKE_CASE_			)
     assert next(iter(ds['train']			)			)




import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
       # setup instructions, if using on-demand hardware

       # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
       # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
       # Throw an error if user passes both BYO and on-demand cluster args
       # Otherwise, use default values
       snake_case_			  = argparse.ArgumentParser()
       parser.add_argument('''--user''', type=str, default='''ubuntu''')
       parser.add_argument('''--host''', type=str, default='''localhost''')
       parser.add_argument('''--key_path''', type=str, default=None)
       parser.add_argument('''--instance''', type=str, default='''V100:1''')
       parser.add_argument('''--provider''', type=str, default='''cheapest''')
       parser.add_argument('''--use_spot''', type=bool, default=False)
       parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
       snake_case_  ,		snake_case_			  = parser.parse_known_args()
       if args.host != "localhost":
              if args.instance != "V100:1" or args.provider != "cheapest":
                     raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = args.example.rsplit('''/''', 1)[0]

       # Set up remote environment
       cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
       # Note transformers is copied into the home directory on the remote machine, so we can install from there
       cluster.run([F'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
       cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

       # Run example. You can bypass the CLI wrapper and paste your own code here.
       cluster.run([F'''python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}'''])

       # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
       # from my_script... import train
       # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
       # launch_train_gpu = rh.function(fn=train,
       #                                system=gpu,
       #                                reqs=reqs,
       #                                name='train_bert_glue')
       #
       # We can pass in arguments just like we would to a function:
       # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
       #                  stream_logs=True)

