


'''simple docstring'''
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__          = 5_0_0_0_0_0

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





@get_duration
def    a__			(   lowerCAmelCase__    ,   **lowerCAmelCase__		)       ->      int:
    UpperCAmelCase__     :						Optional[Any]						=							dataset.map(**UpperCAmelCase_		)





@get_duration
def    a__			(   lowerCAmelCase__    ,   **lowerCAmelCase__		)       ->      str:
    UpperCAmelCase__     :						Union[str, Any]						=							dataset.filter(**UpperCAmelCase_		)





def    a__			(   )       ->      int:
    UpperCAmelCase__     :						Optional[Any]						=							{'''num examples''': SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        UpperCAmelCase__     :						List[Any]						=							datasets.Features({'''text''': datasets.Value('''string'''		), '''numbers''': datasets.Value('''float32'''		)}		)
        UpperCAmelCase__     :						Optional[Any]						=							generate_example_dataset(
            os.path.join(UpperCAmelCase_    ,   '''dataset.arrow'''		)    ,   UpperCAmelCase_    ,   num_examples=UpperCAmelCase_		)

        UpperCAmelCase__     :						Tuple						=							transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''    ,   use_fast=UpperCAmelCase_		)

        def tokenize(lowerCAmelCase__		):
            return tokenizer(examples['''text''']		)

        UpperCAmelCase__     :						Optional[int]						=							map(UpperCAmelCase_		)

        UpperCAmelCase__     :						int						=							map(UpperCAmelCase_    ,   batched=UpperCAmelCase_		)

        UpperCAmelCase__     :						int						=							map(UpperCAmelCase_    ,   function=lambda lowerCAmelCase__		: None    ,   batched=UpperCAmelCase_		)

        with dataset.formatted_as(type='''numpy'''		):
            UpperCAmelCase__     :						Union[str, Any]						=							map(UpperCAmelCase_    ,   function=lambda lowerCAmelCase__		: None    ,   batched=UpperCAmelCase_		)

        with dataset.formatted_as(type='''pandas'''		):
            UpperCAmelCase__     :						List[str]						=							map(UpperCAmelCase_    ,   function=lambda lowerCAmelCase__		: None    ,   batched=UpperCAmelCase_		)

        with dataset.formatted_as(type='''torch'''    ,   columns='''numbers'''		):
            UpperCAmelCase__     :						List[Any]						=							map(UpperCAmelCase_    ,   function=lambda lowerCAmelCase__		: None    ,   batched=UpperCAmelCase_		)

        with dataset.formatted_as(type='''tensorflow'''    ,   columns='''numbers'''		):
            UpperCAmelCase__     :						Dict						=							map(UpperCAmelCase_    ,   function=lambda lowerCAmelCase__		: None    ,   batched=UpperCAmelCase_		)

        UpperCAmelCase__     :						List[Any]						=							map(UpperCAmelCase_    ,   function=UpperCAmelCase_    ,   batched=UpperCAmelCase_		)

        UpperCAmelCase__     :						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()







'''simple docstring'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



