



'''simple docstring'''
import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__SCREAMING_SNAKE_CASE :      Dict             =50_0000

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



@get_duration
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]				):


   '''simple docstring'''







   A:      List[Any]														=   dataset.select(range(0							,							len(_UpperCAmelCase				)							,							2				)				)



@get_duration
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[int]				):


   '''simple docstring'''







   A:      Tuple														=   dataset.sort("""numbers"""				)



@get_duration
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[int]				):


   '''simple docstring'''







   A:      List[str]														=   dataset.shuffle()



@get_duration
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str				):


   '''simple docstring'''







   A:      List[Any]														=   dataset.train_test_split(0.1				)



@get_duration
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      List[str]=10				):


   '''simple docstring'''







   for shard_id in range(_UpperCAmelCase				):
      A:      str														=   dataset.shard(_UpperCAmelCase							,							_UpperCAmelCase				)



def 		_SCREAMING_SNAKE_CASE							(				):


   '''simple docstring'''







   A:      Dict														=   {'num examples': SPEED_TEST_N_EXAMPLES}
   A:      List[Any]														=   (select, sort, shuffle, train_test_split, shard)
   with tempfile.TemporaryDirectory() as tmp_dir:
      print("""generating dataset"""				)
      A:      Optional[Any]														=   datasets.Features({"""text""": datasets.Value("""string"""				), """numbers""": datasets.Value("""float32"""				)}				)
      A:      List[str]														=   generate_example_dataset(
          os.path.join(_UpperCAmelCase							,							"""dataset.arrow"""				)							,							_UpperCAmelCase							,							num_examples=_UpperCAmelCase				)
      print("""Functions"""				)
      for func in functions:
         print(func.__name__				)
         A:      int														=   func(_UpperCAmelCase				)

   with open(_UpperCAmelCase							,							"""wb"""				) as f:
      f.write(json.dumps(_UpperCAmelCase				).encode("""utf-8"""				)				)


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










'''simple docstring'''
from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	def __init__(		self					,   *A					,   **A			) ->							Dict:
				super().__init__(*A					,   **A			)


	def      a__ (		self					,   A					,   A			) ->							Dict:
				A:      int														=   (
				    next_prime(value % self.size_table			)
				    if not is_prime(value % self.size_table			)
				    else value % self.size_table
				)  # gt = bigger than
				return next_prime_gt - (data % next_prime_gt)


	def      a__ (		self					,   A					,   A					,   A			) ->							int:
				return (increment * self.__hash_function_a(A					,   A			)) % self.size_table





	def      a__ (		self					,   A					,   A=None			) ->							str:
				A:      Tuple														=   1
				A:      str														=   self.hash_function(A			)

				while self.values[new_key] is not None and self.values[new_key] != key:
							A:      int														=   (
							    self.__hash_double_function(A					,   A					,   A			)
							    if self.balanced_factor() >= self.lim_charge
							    else None
							)
							if new_key is None:
										break
							else:
										i += 1

				return new_key





