






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__A          = (30, 487)
__A          = (36, 1024)
__A          = (100, 100)
__A          = 100

__A          = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

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




@get_duration
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					with ArrowWriter(features=_lowercase    ,	path=os.path.join(_lowercase    ,	'''beta.arrow'''       )       ) as writer:
										for key, record in dummy_data:
															_A									=       my_features.encode_example(_lowercase       )
															writer.write(_lowercase       )
										_A							,_A									=       writer.finalize()




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       10
					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for i in range(0    ,	len(_lowercase       )    ,	_lowercase       ):
										_A									=       dataset[i : i + batch_size]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       10
					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for i in range(0    ,	len(_lowercase       )    ,	_lowercase       ):
										_A									=       dataset[i : i + batch_size]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for col in feats:
										_A									=       dataset[col]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for col in feats:
										_A									=       dataset[col]




def    __A    (   ):

					'''simple docstring'''







					_A									=       {}
					_A									=       (
					    read_unformated,
					    read_formatted_as_numpy,
					    read_batch_unformated,
					    read_batch_formatted_as_numpy,
					    read_col_unformated,
					    read_col_formatted_as_numpy,
					)
					with tempfile.TemporaryDirectory() as tmp_dir:
										_A									=       datasets.Features({'''image''': ArrayaD(_lowercase    ,	dtype='''float32'''       )}       )
										_A									=       generate_examples(features=_lowercase    ,	num_examples=_lowercase       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''       )       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': SPEED_TEST_SHAPE}       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}    ,	)
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

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


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








"""simple docstring"""







from itertools import combinations_with_replacement


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase = 100	):
		_lowercase							:      list[int]			=					[*list(range(1					,							21	)	), 25]
		_lowercase							:      list[int]			=					[2 * x for x in range(1					,							21	)] + [50]
		_lowercase							:      list[int]			=					[3 * x for x in range(1					,							21	)]
		_lowercase							:      list[int]			=					singles + doubles + triples + [0]

		_lowercase							:      int			=					0
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int
		_lowercase							:      int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(__UpperCAmelCase					,							2	):
						_lowercase							:      Union[str, Any]			=					double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


if __name__ == "__main__":
		print(F'{solution() = }')



