




"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


SCREAMING_SNAKE_CASE    =	50_0000

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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			**lowercase_						)						->		Union[str, Any]:
 A__	=    dataset.map(**a_						)







@get_duration
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			**lowercase_						)						->		List[str]:
 A__	=    dataset.filter(**a_						)







def   _SCREAMING_SNAKE_CASE       (							)						->		Any:
 A__	=    {"num examples": SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  A__	=    datasets.Features({"text": datasets.Value("string"						), "numbers": datasets.Value("float32"						)}						)
  A__	=    generate_example_dataset(
      os.path.join(a_      ,			"dataset.arrow"						)      ,			a_      ,			num_examples=a_						)

  A__	=    transformers.AutoTokenizer.from_pretrained("bert-base-cased"      ,			use_fast=a_						)

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

  A__	=    map(a_						)

  A__	=    map(a_      ,			batched=a_						)

  A__	=    map(a_      ,			function=lambda lowercase_						: None      ,			batched=a_						)

  with dataset.formatted_as(type="numpy"						):
   A__	=    map(a_      ,			function=lambda lowercase_						: None      ,			batched=a_						)

  with dataset.formatted_as(type="pandas"						):
   A__	=    map(a_      ,			function=lambda lowercase_						: None      ,			batched=a_						)

  with dataset.formatted_as(type="torch"      ,			columns="numbers"						):
   A__	=    map(a_      ,			function=lambda lowercase_						: None      ,			batched=a_						)

  with dataset.formatted_as(type="tensorflow"      ,			columns="numbers"						):
   A__	=    map(a_      ,			function=lambda lowercase_						: None      ,			batched=a_						)

  A__	=    map(a_      ,			function=a_      ,			batched=a_						)

  A__	=    filter(a_						)

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


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











"""simple docstring"""



import baseaa







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		bytes:
 return baseaa.aaaencode(string.encode("utf-8"						)						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		str:
 return baseaa.aaadecode(lowercase_						).decode("utf-8"						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()





