
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCamelCase								=					5_0_0_0_0_0

__lowerCamelCase						,						__lowerCamelCase								=					os.path.split(__file__)
__lowerCamelCase								=					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Optional[int]:

							"""simple docstring"""






							_UpperCAmelCase  :			Tuple	       =		dataset.select(range(0      ,	len(UpperCAmelCase__  )      ,	2  )  )




@get_duration
def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      str:

							"""simple docstring"""






							_UpperCAmelCase  :			Any	       =		dataset.sort("numbers"  )




@get_duration
def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      List[Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[int]	       =		dataset.shuffle()




@get_duration
def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Dict:

							"""simple docstring"""






							_UpperCAmelCase  :			int	       =		dataset.train_test_split(0.1  )




@get_duration
def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=1_0  )   ->      List[Any]:

							"""simple docstring"""






							for shard_id in range(UpperCAmelCase__  ):
														_UpperCAmelCase  :			List[Any]	       =		dataset.shard(UpperCAmelCase__      ,	UpperCAmelCase__  )




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

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[Any]	       =		{"""num examples""": SPEED_TEST_N_EXAMPLES}
							_UpperCAmelCase  :			Union[str, Any]	       =		(select, sort, shuffle, train_test_split, shard)
							with tempfile.TemporaryDirectory() as tmp_dir:
														print("generating dataset"  )
														_UpperCAmelCase  :			Tuple	       =		datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
														_UpperCAmelCase  :			str	       =		generate_example_dataset(
														    os.path.join(UpperCAmelCase__      ,	"dataset.arrow"  )      ,	UpperCAmelCase__      ,	num_examples=UpperCAmelCase__  )
														print("Functions"  )
														for func in functions:
																					print(func.__name__  )
																					_UpperCAmelCase  :			Dict	       =		func(UpperCAmelCase__  )

							with open(UpperCAmelCase__      ,	"wb"  ) as f:
														f.write(json.dumps(UpperCAmelCase__  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_indices_mapping()












"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_     =				{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				['''DeformableDetrFeatureExtractor''']
						A_     =				['''DeformableDetrImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				[
						    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DeformableDetrForObjectDetection''',
						    '''DeformableDetrModel''',
						    '''DeformableDetrPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

						A_     =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






