


"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  50000
_lowerCamelCase     =  5000

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


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					lowerCamelCase_  :      Optional[Any]  ):


    for i in range(lowerCamelCase_  ):
        SCREAMING_SNAKE_CASE__     							=				dataset[i]


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[Any]  ):


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


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Dict  ):


    with dataset.formatted_as(type=lowerCamelCase_  ):
        for i in range(lowerCamelCase_  ):
            SCREAMING_SNAKE_CASE__     							=				dataset[i]


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[Any]  ):


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


def __lowercase     (      ):
    SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
    SCREAMING_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}),
    ]

    SCREAMING_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"  )
        SCREAMING_SNAKE_CASE__     							=				datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"  )  ), "numbers": datasets.Value("float32"  )}  )
        SCREAMING_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_  )  )
            SCREAMING_SNAKE_CASE__     							=				func(lowerCamelCase_						,					**lowerCamelCase_  )

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




"""simple docstring"""






def __lowercase     (      lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				set({"(", "[", "{"}  )
				SCREAMING_SNAKE_CASE__     							=				set({")", "]", "}"}  )
				SCREAMING_SNAKE_CASE__     							=				{"{": "}", "[": "]", "(": ")"}

				for i in range(len(lowerCamelCase_  )  ):
								if s[i] in open_brackets:
												stack.append(s[i]  )

								elif s[i] in closed_brackets and (
								    len(lowerCamelCase_  ) == 0 or (len(lowerCamelCase_  ) > 0 and open_to_closed[stack.pop()] != s[i])
								):
												return False

				return len(lowerCamelCase_  ) == 0


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				input("Enter sequence of brackets: "  )
				if is_balanced(lowerCamelCase_  ):
								print(lowerCamelCase_						,					"is balanced"  )
				else:
								print(lowerCamelCase_						,					"is not balanced"  )


if __name__ == "__main__":
	main()
