



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_										=					50000
UpperCamelCase_										=					5000

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







@get_duration
def 	lowerCamelCase_   (  _a    :		List[str]					,						_a    :		Union[str, Any]  ):



    '''simple docstring'''




    for i in range(SCREAMING_SNAKE_CASE__  ):
        UpperCAmelCase_			:    Union[str, Any]   						= dataset[i]







@get_duration
def 	lowerCamelCase_   (  _a    :		int					,						_a    :		Dict					,						_a    :		str  ):



    '''simple docstring'''




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







@get_duration
def 	lowerCamelCase_   (  _a    :		List[Any]					,						_a    :		List[str]					,						_a    :		int  ):



    '''simple docstring'''




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







@get_duration
def 	lowerCamelCase_   (  _a    :		Optional[Any]					,						_a    :		List[str]					,						_a    :		Tuple					,						_a    :		Optional[Any]  ):



    '''simple docstring'''




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







def 	lowerCamelCase_   (  ):



    '''simple docstring'''




    UpperCAmelCase_			:    Tuple   						= {"""num examples""": SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase_			:    List[str]   						= [
        (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}),
    ]

    UpperCAmelCase_			:    Optional[Any]   						= [
        (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"""  )
        UpperCAmelCase_			:    str   						= datasets.Features(
            {"""list""": datasets.Sequence(datasets.Value("""float32"""  )  ), """numbers""": datasets.Value("""float32"""  )}  )
        UpperCAmelCase_			:    Tuple   						= generate_example_dataset(
            os.path.join(SCREAMING_SNAKE_CASE__					,						"""dataset.arrow"""  )					,						SCREAMING_SNAKE_CASE__					,						num_examples=SCREAMING_SNAKE_CASE__					,						seq_shapes={"""list""": (100,)}					,						)
        print("""first set of iterations"""  )
        for func, kwargs in functions:
            print(func.__name__					,						str(SCREAMING_SNAKE_CASE__  )  )
            UpperCAmelCase_			:    Tuple   						= func(SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__  )

        print("""shuffling dataset"""  )
        UpperCAmelCase_			:    Any   						= dataset.shuffle()
        print("""Second set of iterations (after shuffling"""  )
        for func, kwargs in functions_shuffled:
            print("""shuffled """					,						func.__name__					,						str(SCREAMING_SNAKE_CASE__  )  )
            UpperCAmelCase_			:    Union[str, Any]   						= func(
                SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__  )

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


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



from __future__ import annotations

from math import pi, sqrt


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):

						if inductance <= 0:
												raise ValueError('''Inductance cannot be 0 or negative'''       )

						elif capacitance <= 0:
												raise ValueError('''Capacitance cannot be 0 or negative'''       )

						else:
												return (
												    "Resonant frequency",
												    float(1 / (2 * pi * (sqrt(inductance * capacitance       )))       ),
												)


if __name__ == "__main__":
					import doctest

					doctest.testmod()