

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   Any       )					->				Union[str, Any]:
	assert isinstance(a__			,a__       )
	assert dataset.num_rows == 4
	assert dataset.num_columns == 1
	assert dataset.column_names == ["text"]
	for feature, expected_dtype in expected_features.items():
		assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""			,[False, True]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   Optional[Any]			,a__    :   Tuple       )					->				List[Any]:
	__A :			int		    =			tmp_path / """cache"""
	__A :			List[str]		    =			{"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__A :			Any		    =			TextDatasetReader(a__			,cache_dir=a__			,keep_in_memory=a__       ).read()
	_check_text_dataset(a__			,a__       )
@pytest.mark.parametrize(
    """features"""			,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   List[str]			,a__    :   List[str]       )					->				List[str]:
	__A :			Optional[int]		    =			tmp_path / """cache"""
	__A :			List[Any]		    =			{"""text""": """string"""}
	__A :			List[str]		    =			features.copy() if features else default_expected_features
	__A :			Union[str, Any]		    =			(
	    Features({feature: Value(a__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__A :			str		    =			TextDatasetReader(a__			,features=a__			,cache_dir=a__       ).read()
	_check_text_dataset(a__			,a__       )
@pytest.mark.parametrize("""split"""			,[None, NamedSplit("""train"""       ), """train""", """test"""]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   List[str]			,a__    :   str       )					->				Dict:
	__A :			Optional[int]		    =			tmp_path / """cache"""
	__A :			List[Any]		    =			{"""text""": """string"""}
	__A :			List[Any]		    =			TextDatasetReader(a__			,cache_dir=a__			,split=a__       ).read()
	_check_text_dataset(a__			,a__       )
	assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""			,[str, list]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Dict			,a__    :   List[Any]			,a__    :   Optional[int]       )					->				Optional[int]:
	if issubclass(a__			,a__       ):
		__A :			Optional[Any]		    =			text_path
	elif issubclass(a__			,a__       ):
		__A :			str		    =			[text_path]
	__A :			List[str]		    =			tmp_path / """cache"""
	__A :			Tuple		    =			{"""text""": """string"""}
	__A :			List[Any]		    =			TextDatasetReader(a__			,cache_dir=a__       ).read()
	_check_text_dataset(a__			,a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   List[Any]			,a__    :   Optional[Any]=("train",)       )					->				Tuple:
	assert isinstance(a__			,a__       )
	for split in splits:
		__A :			Union[str, Any]		    =			dataset_dict[split]
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
			assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory"""			,[False, True]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   str       )					->				Any:
	__A :			Dict		    =			tmp_path / """cache"""
	__A :			Optional[int]		    =			{"""text""": """string"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		__A :			Dict		    =			TextDatasetReader({"""train""": text_path}			,cache_dir=a__			,keep_in_memory=a__       ).read()
	_check_text_datasetdict(a__			,a__       )
@pytest.mark.parametrize(
    """features"""			,[
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   int			,a__    :   Dict       )					->				Tuple:
	__A :			Any		    =			tmp_path / """cache"""
	# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
	__A :			Optional[Any]		    =			{"""text""": """string"""}
	__A :			int		    =			features.copy() if features else default_expected_features
	__A :			Optional[Any]		    =			(
	    Features({feature: Value(a__       ) for feature, dtype in features.items()}       ) if features is not None else None
	)
	__A :			Tuple		    =			TextDatasetReader({"""train""": text_path}			,features=a__			,cache_dir=a__       ).read()
	_check_text_datasetdict(a__			,a__       )







@pytest.mark.parametrize("""split"""			,[None, NamedSplit("""train"""       ), """train""", """test"""]       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   List[Any]			,a__    :   Tuple       )					->				Any:
	if split:
		__A :			int		    =			{split: text_path}
	else:
		__A :			Union[str, Any]		    =			"""train"""
		__A :			int		    =			{"""train""": text_path, """test""": text_path}
	__A :			Any		    =			tmp_path / """cache"""
	__A :			str		    =			{"""text""": """string"""}
	__A :			List[str]		    =			TextDatasetReader(a__			,cache_dir=a__       ).read()
	_check_text_datasetdict(a__			,a__			,splits=list(path.keys()       )       )
	assert all(dataset[split].split == split for split in path.keys()       )








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def 							__SCREAMING_SNAKE_CASE     (   )					->				Tuple:
	__A :			List[Any]		    =			ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
	    )       )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""			,type=a__			,default=1			,help="""Number of TPU cores to use (1 or 8)."""       )

	# positional
	parser.add_argument(
	    """training_script"""			,type=a__			,help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )			,)

	# rest from the training program
	parser.add_argument("""training_script_args"""			,nargs=a__       )

	return parser.parse_args()
def 							__SCREAMING_SNAKE_CASE     (   )					->				str:
	__A :			Union[str, Any]		    =			parse_args()

	# Import training_script as a module.
	__A :			List[Any]		    =			Path(args.training_script       )
	sys.path.append(str(script_fpath.parent.resolve()       )       )
	__A :			str		    =			script_fpath.stem
	__A :			int		    =			importlib.import_module(a__       )

	# Patch sys.argv
	__A :			List[str]		    =			[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores       )]

	xmp.spawn(mod._mp_fn			,args=()			,nprocs=args.num_cores       )


if __name__ == "__main__":
			main()





