





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_UpperCAmelCase       : Dict			     =       50_00_00

_UpperCAmelCase       : Optional[int]			     =       os.path.split(__file__)
_UpperCAmelCase       : str			     =       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			**_UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   str      				=	dataset.map(**__lowerCAmelCase				)


@get_duration
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			**_UpperCAmelCase				)    ->  List[Any]:
    lowerCamelCase__					:   Dict      				=	dataset.filter(**__lowerCAmelCase				)


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

        lowerCamelCase__					:   List[Any]      				=	transformers.AutoTokenizer.from_pretrained('bert-base-cased'		,			use_fast=__lowerCAmelCase				)

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

        lowerCamelCase__					:   str      				=	map(__lowerCAmelCase				)

        lowerCamelCase__					:   List[str]      				=	map(__lowerCAmelCase		,			batched=__lowerCAmelCase				)

        lowerCamelCase__					:   Dict      				=	map(__lowerCAmelCase		,			function=lambda _UpperCAmelCase				: None		,			batched=__lowerCAmelCase				)

        with dataset.formatted_as(type='numpy'				):
            lowerCamelCase__					:   Dict      				=	map(__lowerCAmelCase		,			function=lambda _UpperCAmelCase				: None		,			batched=__lowerCAmelCase				)

        with dataset.formatted_as(type='pandas'				):
            lowerCamelCase__					:   List[Any]      				=	map(__lowerCAmelCase		,			function=lambda _UpperCAmelCase				: None		,			batched=__lowerCAmelCase				)

        with dataset.formatted_as(type='torch'		,			columns='numbers'				):
            lowerCamelCase__					:   int      				=	map(__lowerCAmelCase		,			function=lambda _UpperCAmelCase				: None		,			batched=__lowerCAmelCase				)

        with dataset.formatted_as(type='tensorflow'		,			columns='numbers'				):
            lowerCamelCase__					:   Union[str, Any]      				=	map(__lowerCAmelCase		,			function=lambda _UpperCAmelCase				: None		,			batched=__lowerCAmelCase				)

        lowerCamelCase__					:   int      				=	map(__lowerCAmelCase		,			function=__lowerCAmelCase		,			batched=__lowerCAmelCase				)

        lowerCamelCase__					:   Optional[Any]      				=	filter(__lowerCAmelCase				)

        # 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(__lowerCAmelCase		,			'wb'				) as f:
        f.write(json.dumps(__lowerCAmelCase				).encode('utf-8'				)				)


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












_UpperCAmelCase       : str			     =       """Tobias Carryer"""

from time import time

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   str=int(time()			)			)    ->			Optional[int]:  # noqa: B008
     lowerCamelCase__					:   List[Any]      				=	multiplier
     lowerCamelCase__					:   Tuple      				=	increment
     lowerCamelCase__					:   int      				=	modulo
     lowerCamelCase__					:   Any      				=	seed







 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	(self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    _UpperCAmelCase       : List[Any]			     =       LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
    while True:
        print(lcg.next_number())





