



'''simple docstring'''



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__magic_name__   :						Union[str, Any]    =			500_000

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




@get_duration
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				dataset.map(**SCREAMING_SNAKE_CASE__       )




@get_duration
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				dataset.filter(**SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				{"num examples": SPEED_TEST_N_EXAMPLES}
  with tempfile.TemporaryDirectory() as tmp_dir:
    _snake_case     		=				datasets.Features({"text": datasets.Value("string"       ), "numbers": datasets.Value("float32"       )}       )
    _snake_case     		=				generate_example_dataset(
        os.path.join(SCREAMING_SNAKE_CASE__					,	"dataset.arrow"       )					,	SCREAMING_SNAKE_CASE__					,	num_examples=SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"					,	use_fast=SCREAMING_SNAKE_CASE__       )

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

    _snake_case     		=				map(SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	batched=SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=lambda SCREAMING_SNAKE_CASE__       : None					,	batched=SCREAMING_SNAKE_CASE__       )

    with dataset.formatted_as(type="numpy"       ):
      _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=lambda SCREAMING_SNAKE_CASE__       : None					,	batched=SCREAMING_SNAKE_CASE__       )

    with dataset.formatted_as(type="pandas"       ):
      _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=lambda SCREAMING_SNAKE_CASE__       : None					,	batched=SCREAMING_SNAKE_CASE__       )

    with dataset.formatted_as(type="torch"					,	columns="numbers"       ):
      _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=lambda SCREAMING_SNAKE_CASE__       : None					,	batched=SCREAMING_SNAKE_CASE__       )

    with dataset.formatted_as(type="tensorflow"					,	columns="numbers"       ):
      _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=lambda SCREAMING_SNAKE_CASE__       : None					,	batched=SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				map(SCREAMING_SNAKE_CASE__					,	function=SCREAMING_SNAKE_CASE__					,	batched=SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				filter(SCREAMING_SNAKE_CASE__       )

    # 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(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_map_filter()









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


