



'''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


lowerCamelCase	 =       [
    {"""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       (   _lowerCAmelCase=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 _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    None
				lowerCAmelCase__					    =    None

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict):







											'''simple docstring'''

											with TemporaryDirectory() as tmp_dir:
																		__lowercase       	=dataset_module_factory(_lowerCAmelCase  ,       cache_dir=_lowerCAmelCase)

																		__lowercase       	=import_main_class(dataset_module.module_path  ,       dataset=_lowerCAmelCase)

																		__lowercase       	=builder_cls(
																		    cache_dir=_lowerCAmelCase  ,       config_name=_lowerCAmelCase  ,       hash=dataset_module.hash  ,       )

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



@pytest.mark.integration
def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



@pytest.mark.integration
def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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








'''simple docstring'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase	 =       """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=_ask_options(
							    'In which compute environment are you running?'  ,   ['This machine', 'AWS (Amazon SageMaker)']  ,   _convert_compute_environment  ,   )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase       	=get_sagemaker_input()
							else:
														__lowercase       	=get_cluster_input()
							return config



def   _A       (   _lowerCAmelCase=None					):



							"""simple docstring"""



							if subparsers is not None:
														__lowercase       	=subparsers.add_parser('config'  ,   description=_lowerCAmelCase					)
							else:
														__lowercase       	=argparse.ArgumentParser('Accelerate config command'  ,   description=_lowerCAmelCase					)

							parser.add_argument(
							    '--config_file'  ,   default=_lowerCAmelCase  ,   help=(
							        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
							        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
							        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
							        'with \'huggingface\'.'
							    )  ,   )

							if subparsers is not None:
														parser.set_defaults(func=_lowerCAmelCase					)
							return parser



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=get_user_input()
							if args.config_file is not None:
														__lowercase       	=args.config_file
							else:
														if not os.path.isdir(_lowerCAmelCase					):
																					os.makedirs(_lowerCAmelCase					)
														__lowercase       	=default_yaml_config_file

							if config_file.endswith('.json'					):
														config.to_json_file(_lowerCAmelCase					)
							else:
														config.to_yaml_file(_lowerCAmelCase					)
							print(f"""accelerate configuration saved at {config_file}"""					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=config_command_parser()
							__lowercase       	=parser.parse_args()
							config_command(_lowerCAmelCase					)


if __name__ == "__main__":
		main()



