



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__SCREAMING_SNAKE_CASE           =  500000

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




@get_duration
def 	UpperCAmelCase   (						_lowerCamelCase ,    **_lowerCamelCase    ):
			A							:					int              =						dataset.map(**__UpperCamelCase    )




@get_duration
def 	UpperCAmelCase   (						_lowerCamelCase ,    **_lowerCamelCase    ):
			A							:					Optional[Any]              =						dataset.filter(**__UpperCamelCase    )




def 	UpperCAmelCase   (						):
			A							:					List[Any]              =						{"""num examples""": SPEED_TEST_N_EXAMPLES}
			with tempfile.TemporaryDirectory() as tmp_dir:
						A							:					str              =						datasets.Features({"text": datasets.Value("string"    ), "numbers": datasets.Value("float32"    )}    )
						A							:					Tuple              =						generate_example_dataset(
						    os.path.join(__UpperCamelCase ,    "dataset.arrow"    ) ,    __UpperCamelCase ,    num_examples=__UpperCamelCase    )

						A							:					Dict              =						transformers.AutoTokenizer.from_pretrained("bert-base-cased" ,    use_fast=__UpperCamelCase    )

						def tokenize(_lowerCamelCase    ):
									return tokenizer(examples["text"]    )

						A							:					Optional[int]              =						map(__UpperCamelCase    )

						A							:					int              =						map(__UpperCamelCase ,    batched=__UpperCamelCase    )

						A							:					int              =						map(__UpperCamelCase ,    function=lambda _lowerCamelCase    : None ,    batched=__UpperCamelCase    )

						with dataset.formatted_as(type="numpy"    ):
									A							:					List[str]              =						map(__UpperCamelCase ,    function=lambda _lowerCamelCase    : None ,    batched=__UpperCamelCase    )

						with dataset.formatted_as(type="pandas"    ):
									A							:					str              =						map(__UpperCamelCase ,    function=lambda _lowerCamelCase    : None ,    batched=__UpperCamelCase    )

						with dataset.formatted_as(type="torch" ,    columns="numbers"    ):
									A							:					str              =						map(__UpperCamelCase ,    function=lambda _lowerCamelCase    : None ,    batched=__UpperCamelCase    )

						with dataset.formatted_as(type="tensorflow" ,    columns="numbers"    ):
									A							:					int              =						map(__UpperCamelCase ,    function=lambda _lowerCamelCase    : None ,    batched=__UpperCamelCase    )

						A							:					Tuple              =						map(__UpperCamelCase ,    function=__UpperCamelCase ,    batched=__UpperCamelCase    )

						A							:					Union[str, Any]              =						filter(__UpperCamelCase    )

						# Activate later when tokenizer support batched inputs
						# with dataset.formatted_as(type='numpy'):
						#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()