
from __future__ import annotations





class 							A__		:
      def __init__(      self    ,  __magic_name__ = 0      ):
       lowerCamelCase		:	List[str]							=      key
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	int							=      key or self.__key or 1

       # make sure key is an appropriate size
       key %= 2_5_5

       return [chr(ord(__magic_name__      ) ^ key      ) for ch in content]
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      key or self.__key or 1

       # make sure key is an appropriate size
       key %= 2_5_5

       return [chr(ord(__magic_name__      ) ^ key      ) for ch in content]
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = 0      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	Any							=      key or self.__key or 1

       # make sure key can be any size
       while key > 2_5_5:
        key -= 2_5_5

       # This will be returned
       lowerCamelCase		:	int							=      """"""

       for ch in content:
        ans += chr(ord(__magic_name__      ) ^ key      )

       return ans
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = 0      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	List[Any]							=      key or self.__key or 1

       # make sure key can be any size
       while key > 2_5_5:
        key -= 2_5_5

       # This will be returned
       lowerCamelCase		:	Union[str, Any]							=      """"""

       for ch in content:
        ans += chr(ord(__magic_name__      ) ^ key      )

       return ans
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = 0      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       try:
        with open(__magic_name__      ) as fin, open("""encrypt.out"""    ,  """w+"""      ) as fout:
         # actual encrypt-process
         for line in fin:
          fout.write(self.encrypt_string(__magic_name__    ,  __magic_name__      )      )

       except OSError:
        return False

       return True






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       assert isinstance(__magic_name__    ,  __magic_name__      ) and isinstance(__magic_name__    ,  __magic_name__      )

       try:
        with open(__magic_name__      ) as fin, open("""decrypt.out"""    ,  """w+"""      ) as fout:
         # actual encrypt-process
         for line in fin:
          fout.write(self.decrypt_string(__magic_name__    ,  __magic_name__      )      )

       except OSError:
        return False

       return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase					   =5_0_0_0_0_0

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





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.map(**lowerCamelCase							)





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.filter(**lowerCamelCase							)





def     _a					(						):
 lowerCamelCase		:	Optional[Any]							=      {"""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		:	Tuple							=      generate_example_dataset(
      os.path.join(lowerCamelCase,	"""dataset.arrow"""							),	lowerCamelCase,	num_examples=lowerCamelCase							)

  lowerCamelCase		:	Tuple							=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",	use_fast=lowerCamelCase							)

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

  lowerCamelCase		:	List[str]							=      map(lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""numpy"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""pandas"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

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

  with dataset.formatted_as(type="""tensorflow""",	columns="""numbers"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	Union[str, 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()




