
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A   =			5_00_00
_A   =			50_00

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



@get_duration
def 						lowercase_      (       A__			,		A__		)							->							str:
				"""simple docstring"""

				for i in range(lowerCAmelCase__		):
								snake_case								= dataset[i]



@get_duration
def 						lowercase_      (       A__			,		A__			,		A__		)							->							List[str]:
				"""simple docstring"""

				for i in range(0			,		len(lowerCAmelCase__		)			,		lowerCAmelCase__		):
								snake_case								= dataset[i : i + batch_size]



@get_duration
def 						lowercase_      (       A__			,		A__			,		A__		)							->							Dict:
				"""simple docstring"""

				with dataset.formatted_as(type=lowerCAmelCase__		):
								for i in range(lowerCAmelCase__		):
												snake_case								= dataset[i]



@get_duration
def 						lowercase_      (       A__			,		A__			,		A__			,		A__		)							->							int:
				"""simple docstring"""

				with dataset.formatted_as(type=lowerCAmelCase__		):
								for i in range(0			,		lowerCAmelCase__			,		lowerCAmelCase__		):
												snake_case								= dataset[i : i + batch_size]



def 						lowercase_      (       )							->							Dict:
				"""simple docstring"""

				snake_case								= {"num examples": SPEED_TEST_N_EXAMPLES}
				snake_case								= [
				    (read, {"length": SMALL_TEST}),
				    (read, {"length": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
				    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
				    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
				    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
				    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
				    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
				    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
				]

				snake_case								= [
				    (read, {"length": SMALL_TEST}),
				    (read, {"length": SPEED_TEST_N_EXAMPLES}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
				    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
				    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
				    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
				    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
				]
				with tempfile.TemporaryDirectory() as tmp_dir:
								print("generating dataset"		)
								snake_case								= datasets.Features(
								    {"list": datasets.Sequence(datasets.Value("float32"		)		), "numbers": datasets.Value("float32"		)}		)
								snake_case								= generate_example_dataset(
								    os.path.join(lowerCAmelCase__			,		"dataset.arrow"		)			,		lowerCAmelCase__			,		num_examples=lowerCAmelCase__			,		seq_shapes={"list": (100,)}			,		)
								print("first set of iterations"		)
								for func, kwargs in functions:
												print(func.__name__			,		str(lowerCAmelCase__		)		)
												snake_case								= func(lowerCAmelCase__			,		**lowerCAmelCase__		)

								print("shuffling dataset"		)
								snake_case								= dataset.shuffle()
								print("Second set of iterations (after shuffling"		)
								for func, kwargs in functions_shuffled:
												print("shuffled "			,		func.__name__			,		str(lowerCAmelCase__		)		)
												snake_case								= func(
												    lowerCAmelCase__			,		**lowerCAmelCase__		)

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


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



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

