


import string
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						''
 for i in sequence:
  __lowerCamelCase		:							str		 =						ord(SCREAMING_SNAKE_CASE__						)
  if 65 <= extract <= 90:
   output += chr(155 - extract						)
  elif 97 <= extract <= 122:
   output += chr(219 - extract						)
  else:
   output += i
 return output
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						string.ascii_letters
 __lowerCamelCase		:							Tuple		 =						string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(SCREAMING_SNAKE_CASE__						)] if c in letters else c for c in sequence						)
def 				UpperCamelCase__					(  ):
 from timeit import timeit

 print('Running performance benchmarks...'						)
 __lowerCamelCase		:							Tuple		 =						'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f'> atbash_slow(): {timeit("atbash_slow(printable)"      ,	setup=SCREAMING_SNAKE_CASE__						)} seconds'						)
 print(f'>      atbash(): {timeit("atbash(printable)"      ,	setup=SCREAMING_SNAKE_CASE__						)} seconds'						)


if __name__ == "__main__":
       for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
              print(F"""{example} encrypted in atbash: {atbash(example)}""")
       benchmark()





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						dataset.map(**SCREAMING_SNAKE_CASE__						)
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						dataset.filter(**SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCamelCase		:							Any		 =						datasets.Features({'text': datasets.Value('string'						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							Any		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__						)

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

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

  __lowerCamelCase		:							str		 =						map(SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							int		 =						map(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

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

  with dataset.formatted_as(type='pandas'						):
   __lowerCamelCase		:							Any		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='torch'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[str]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='tensorflow'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						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()

