



'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE__      					=     5_0000
SCREAMING_SNAKE_CASE__      					=     5000

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




@get_duration
def        lowerCamelCase  (				_snake_case  :						datasets.Dataset     ,   _snake_case  :						Dict       ):





    '''simple docstring'''


    for i in range(snake_case__       ):
        lowercase__		       =						dataset[i]




@get_duration
def        lowerCamelCase  (				_snake_case  :						datasets.Dataset     ,   _snake_case  :						Optional[Any]     ,   _snake_case  :						str       ):





    '''simple docstring'''


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




@get_duration
def        lowerCamelCase  (				_snake_case  :						datasets.Dataset     ,   _snake_case  :						str     ,   _snake_case  :						int       ):





    '''simple docstring'''


    with dataset.formatted_as(type=snake_case__       ):
        for i in range(snake_case__       ):
            lowercase__		       =						dataset[i]




@get_duration
def        lowerCamelCase  (				_snake_case  :						datasets.Dataset     ,   _snake_case  :						Any     ,   _snake_case  :						int     ,   _snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						{"""num examples""": SPEED_TEST_N_EXAMPLES}
    lowercase__		       =						[
        (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""": 1_000}),
        (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""": 1_000}),
    ]

    lowercase__		       =						[
        (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""": 1_000}),
        (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""": 1_000}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"       )
        lowercase__		       =						datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"       )       ), "numbers": datasets.Value("float32"       )}       )
        lowercase__		       =						generate_example_dataset(
            os.path.join(snake_case__     ,   "dataset.arrow"       )     ,   snake_case__     ,   num_examples=snake_case__     ,   seq_shapes={"list": (100,)}     ,   )
        print("first set of iterations"       )
        for func, kwargs in functions:
            print(func.__name__     ,   str(snake_case__       )       )
            lowercase__		       =						func(snake_case__     ,   **snake_case__       )

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

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


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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return x if y == 0 else greatest_common_divisor(_snake_case     ,x % y       )




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int       ):





    '''simple docstring'''


    return (x * y) // greatest_common_divisor(_snake_case     ,_snake_case       )




def        lowerCamelCase  (				_snake_case  :						int = 20       ):





    '''simple docstring'''


    lowercase__		       =						1
    for i in range(1     ,n + 1       ):
        lowercase__		       =						lcm(_snake_case     ,_snake_case       )
    return g


if __name__ == "__main__":
 print(f'''{solution() = }''')




