





"""simple docstring"""




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


UpperCamelCase_     =(30, 487)
UpperCamelCase_     =(36, 1_024)
UpperCamelCase_     =(100, 100)
UpperCamelCase_     =100

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

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





@get_duration
def  a_       (							_lowercase ,		_lowercase ,		_lowercase     ):
						with ArrowWriter(features=_lowercase ,		path=os.path.join(_lowercase ,		'''beta.arrow'''     )     ) as writer:
												for key, record in dummy_data:
																		_UpperCamelCase    :      Tuple						     =					my_features.encode_example(_lowercase     )
																		writer.write(_lowercase     )
												_UpperCamelCase     ,							_UpperCamelCase    :      List[str]						     =					writer.finalize()





@get_duration
def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Union[str, Any]						     =					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     ):
						_UpperCamelCase    :      List[Any]						     =					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     ):
						_UpperCamelCase    :      Optional[int]						     =					10
						_UpperCamelCase    :      List[str]						     =					datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase ,		'''beta.arrow'''     ) ,		info=datasets.DatasetInfo(features=_lowercase     )     )
						for i in range(0 ,		len(_lowercase     ) ,		_lowercase     ):
												_UpperCamelCase    :      Union[str, Any]						     =					dataset[i : i + batch_size]





@get_duration
def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      List[str]						     =					10
						_UpperCamelCase    :      Tuple						     =					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     ):
												_UpperCamelCase    :      List[str]						     =					dataset[i : i + batch_size]





@get_duration
def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Dict						     =					datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase ,		'''beta.arrow'''     ) ,		info=datasets.DatasetInfo(features=_lowercase     )     )
						for col in feats:
												_UpperCamelCase    :      List[str]						     =					dataset[col]





@get_duration
def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					datasets.Dataset.from_file(
						    filename=os.path.join(_lowercase ,		'''beta.arrow'''     ) ,		info=datasets.DatasetInfo(features=_lowercase     )     )
						dataset.set_format('''numpy'''     )
						for col in feats:
												_UpperCamelCase    :      Optional[int]						     =					dataset[col]





def  a_       (							):
						_UpperCamelCase    :      Any						     =					{}
						_UpperCamelCase    :      List[Any]						     =					(
						    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:
												_UpperCamelCase    :      int						     =					datasets.Features({'''image''': ArrayaD(_lowercase ,		dtype='''float32'''     )}     )
												_UpperCamelCase    :      Any						     =					generate_examples(features=_lowercase ,		num_examples=_lowercase     )
												_UpperCamelCase    :      Union[str, Any]						     =					write(_lowercase ,		_lowercase ,		_lowercase     )
												for read_func in read_functions:
																		_UpperCamelCase    :      Dict						     =					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])}
												# )
												_UpperCamelCase    :      Dict						     =					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''     )     )     )}     )
												_UpperCamelCase    :      Optional[int]						     =					generate_examples(
												    features=_lowercase ,		num_examples=_lowercase ,		seq_shapes={'''image''': SPEED_TEST_SHAPE}     )
												_UpperCamelCase    :      Optional[int]						     =					write(_lowercase ,		_lowercase ,		_lowercase     )
												for read_func in read_functions:
																		_UpperCamelCase    :      Any						     =					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])}
												# )
												_UpperCamelCase    :      List[str]						     =					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''     )     )}     )
												_UpperCamelCase    :      Tuple						     =					generate_examples(
												    features=_lowercase ,		num_examples=_lowercase ,		seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,		)
												_UpperCamelCase    :      List[Any]						     =					write(_lowercase ,		_lowercase ,		_lowercase     )
												for read_func in read_functions:
																		_UpperCamelCase    :      Any						     =					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"""




def  a_       (							_lowercase ,		_lowercase     ):
						sequence.append(_lowercase     )

						_UpperCamelCase    :      int						     =					0
						while sequence[index] != target:
												index += 1

						sequence.pop()

						if index == len(_lowercase     ):
												return None

						return index


if __name__ == "__main__":
						UpperCamelCase_     =input("""Enter numbers separated by comma:\n""").strip()
						UpperCamelCase_     =[int(item) for item in user_input.split(""",""")]

						UpperCamelCase_     =input("""Enter a single number to be found in the list:\n""")
						UpperCamelCase_     =int(target_input)
						UpperCamelCase_     =sentinel_linear_search(sequence, target)
						if result is not None:
												print(F"{target} found at positions: {result}")
						else:
												print("""Not found""")



