




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			:	Dict							 =     [
    {"""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 SCREAMING_SNAKE_CASE       ( lowercase_=True		)     -> Optional[int]:



       """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=UpperCAmelCase__  )  )
class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   None


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any)		->int:





           '''simple docstring'''

           with TemporaryDirectory() as tmp_dir:
                  A__   				=					dataset_module_factory(UpperCAmelCase__      ,				cache_dir=UpperCAmelCase__)

                  A__   				=					import_main_class(dataset_module.module_path      ,				dataset=UpperCAmelCase__)

                  A__   				=					builder_cls(
                      cache_dir=UpperCAmelCase__      ,				config_name=UpperCAmelCase__      ,				hash=dataset_module.hash      ,				)

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







@pytest.mark.integration
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					tmp_path_factory.mktemp('''test_hf_gcp'''		) / '''test_wikipedia_simple'''
       A__   				=					dataset_module_factory('''wikipedia'''				,       cache_dir=lowercase_		)
       A__   				=					import_main_class(dataset_module.module_path		)
       A__   				=					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
       A__   				=					None
       builder_instance.download_and_prepare()
       A__   				=					builder_instance.as_dataset()
       assert ds







@pytest.mark.integration
def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       A__   				=					dataset_module_factory('''wikipedia'''				,       cache_dir=lowercase_		)
       A__   				=					import_main_class(dataset_module.module_path				,       dataset=lowercase_		)
       A__   				=					builder_cls(
           cache_dir=lowercase_				,       config_name='''20220301.frr'''				,       hash=dataset_module.hash				,       )
       A__   				=					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''']		)		)








from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       return DownloadCommand(args.model				,       args.cache_dir				,       args.force				,       args.trust_remote_code		)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    @staticmethod
    def  SCREAMING_SNAKE_CASE							(    UpperCAmelCase__   :			ArgumentParser)		->Any:





           '''simple docstring'''

           A__   				=					parser.add_parser('''download''')
           download_parser.add_argument(
               '''--cache-dir'''      ,				type=UpperCAmelCase__      ,				default=UpperCAmelCase__      ,				help='''Path to location to store the models''')
           download_parser.add_argument(
               '''--force'''      ,				action='''store_true'''      ,				help='''Force the model to be download even if already in cache-dir''')
           download_parser.add_argument(
               '''--trust-remote-code'''      ,				action='''store_true'''      ,				help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,				)
           download_parser.add_argument('''model'''      ,				type=UpperCAmelCase__      ,				help='''Name of the model to download''')
           download_parser.set_defaults(func=UpperCAmelCase__)


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			bool      ,				UpperCAmelCase__   :			bool)		->int:





           '''simple docstring'''

           A__   				=					model
           A__   				=					cache
           A__   				=					force
           A__   				=					trust_remote_code





    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Union[str, Any]:





           '''simple docstring'''

           from ..models.auto import AutoModel, AutoTokenizer

           AutoModel.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)
           AutoTokenizer.from_pretrained(
               self._model      ,				cache_dir=self._cache      ,				force_download=self._force      ,				trust_remote_code=self._trust_remote_code)


