"""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_       :       Optional[int]							   =		(30, 487)
UpperCamelCase_       :       str							   =		(36, 1024)
UpperCamelCase_       :       int							   =		(100, 100)
UpperCamelCase_       :       Union[str, Any]							   =		100

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

UpperCamelCase_   ,						UpperCamelCase_       :       Tuple							   =		os.path.split(__file__)
UpperCamelCase_       :       List[Any]							   =		os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def   a__      (		UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			List[Any]			)->	List[str]:
						with ArrowWriter(features=UpperCAmelCase__		,			path=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)			) as writer:
												for key, record in dummy_data:
																		UpperCAmelCase__						=		my_features.encode_example(UpperCAmelCase__			)
																		writer.write(UpperCAmelCase__			)
												UpperCAmelCase__	,      UpperCAmelCase__						=		writer.finalize()


@get_duration
def   a__      (		UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			int			)->	List[str]:
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						for _ in dataset:
												pass


@get_duration
def   a__      (		UpperCAmelCase__       :			List[str]		,			UpperCAmelCase__       :			Optional[Any]			)->	List[Any]:
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						dataset.set_format("""numpy"""			)
						for _ in dataset:
												pass


@get_duration
def   a__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			Any			)->	Optional[int]:
						UpperCAmelCase__						=		1_0
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						for i in range(0		,			len(UpperCAmelCase__			)		,			UpperCAmelCase__			):
												UpperCAmelCase__						=		dataset[i : i + batch_size]


@get_duration
def   a__      (		UpperCAmelCase__       :			Optional[int]		,			UpperCAmelCase__       :			Union[str, Any]			)->	Optional[int]:
						UpperCAmelCase__						=		1_0
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						dataset.set_format("""numpy"""			)
						for i in range(0		,			len(UpperCAmelCase__			)		,			UpperCAmelCase__			):
												UpperCAmelCase__						=		dataset[i : i + batch_size]


@get_duration
def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Union[str, Any]			)->	List[Any]:
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						for col in feats:
												UpperCAmelCase__						=		dataset[col]


@get_duration
def   a__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			Optional[Any]			)->	Optional[int]:
						UpperCAmelCase__						=		datasets.Dataset.from_file(
						    filename=os.path.join(UpperCAmelCase__		,			"""beta.arrow"""			)		,			info=datasets.DatasetInfo(features=UpperCAmelCase__			)			)
						dataset.set_format("""numpy"""			)
						for col in feats:
												UpperCAmelCase__						=		dataset[col]


def   a__      (		)->	int:
						UpperCAmelCase__						=		{}
						UpperCAmelCase__						=		(
						    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__						=		datasets.Features({"""image""": ArrayaD(UpperCAmelCase__		,			dtype="""float32"""			)}			)
												UpperCAmelCase__						=		generate_examples(features=UpperCAmelCase__		,			num_examples=UpperCAmelCase__			)
												UpperCAmelCase__						=		write(UpperCAmelCase__		,			UpperCAmelCase__		,			UpperCAmelCase__			)
												for read_func in read_functions:
																		UpperCAmelCase__						=		read_func(UpperCAmelCase__		,			UpperCAmelCase__			)

						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__						=		datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""			)			)			)}			)
												UpperCAmelCase__						=		generate_examples(
												    features=UpperCAmelCase__		,			num_examples=UpperCAmelCase__		,			seq_shapes={"""image""": SPEED_TEST_SHAPE}			)
												UpperCAmelCase__						=		write(UpperCAmelCase__		,			UpperCAmelCase__		,			UpperCAmelCase__			)
												for read_func in read_functions:
																		UpperCAmelCase__						=		read_func(UpperCAmelCase__		,			UpperCAmelCase__			)

						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__						=		datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""			)			)}			)
												UpperCAmelCase__						=		generate_examples(
												    features=UpperCAmelCase__		,			num_examples=UpperCAmelCase__		,			seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,			)
												UpperCAmelCase__						=		write(UpperCAmelCase__		,			UpperCAmelCase__		,			UpperCAmelCase__			)
												for read_func in read_functions:
																		UpperCAmelCase__						=		read_func(UpperCAmelCase__		,			UpperCAmelCase__			)

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


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


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

