


"""simple docstring"""







import math


def      lowercase			(				A_  ,   A_ = 0  ,   A_ = 0       )->      list:
  '''simple docstring'''







  a						:		Optional[Any]      							=			end or len(A_       )
  for i in range(A_  ,   A_       ):
    a						:		Optional[Any]      							=			i
    a						:		Dict      							=			array[i]
    while temp_index != start and temp_index_value < array[temp_index - 1]:
      a						:		str      							=			array[temp_index - 1]
      temp_index -= 1
    a						:		Optional[Any]      							=			temp_index_value
  return array


def      lowercase			(				A_  ,   A_  ,   A_       )->      None:  # Max Heap
  '''simple docstring'''







  a						:		Union[str, Any]      							=			index
  a						:		str      							=			2 * index + 1  # Left Node
  a						:		Optional[int]      							=			2 * index + 2  # Right Node

  if left_index < heap_size and array[largest] < array[left_index]:
    a						:		List[Any]      							=			left_index

  if right_index < heap_size and array[largest] < array[right_index]:
    a						:		Optional[int]      							=			right_index

  if largest != index:
    a							,		a						:		List[Any]      							=			array[largest], array[index]
    heapify(A_  ,   A_  ,   A_       )


def      lowercase			(				A_       )->      list:
  '''simple docstring'''







  a						:		Optional[int]      							=			len(A_       )

  for i in range(n // 2  ,   -1  ,   -1       ):
    heapify(A_  ,   A_  ,   A_       )

  for i in range(n - 1  ,   0  ,   -1       ):
    a							,		a						:		Any      							=			array[0], array[i]
    heapify(A_  ,   0  ,   A_       )

  return array


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      int:
  '''simple docstring'''







  if (array[first_index] > array[middle_index]) != (
      array[first_index] > array[last_index]
  ):
    return array[first_index]
  elif (array[middle_index] > array[first_index]) != (
      array[middle_index] > array[last_index]
  ):
    return array[middle_index]
  else:
    return array[last_index]


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      int:
  '''simple docstring'''







  a						:		str      							=			low
  a						:		int      							=			high
  while True:
    while array[i] < pivot:
      i += 1
    j -= 1
    while pivot < array[j]:
      j -= 1
    if i >= j:
      return i
    a							,		a						:		int      							=			array[j], array[i]
    i += 1


def      lowercase			(				A_       )->      list:
  '''simple docstring'''







  if len(A_       ) == 0:
    return array
  a						:		Union[str, Any]      							=			2 * math.ceil(math.loga(len(A_       )       )       )
  a						:		Optional[int]      							=			16
  return intro_sort(A_  ,   0  ,   len(A_       )  ,   A_  ,   A_       )


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      list:
  '''simple docstring'''







  while end - start > size_threshold:
    if max_depth == 0:
      return heap_sort(A_       )
    max_depth -= 1
    a						:		Tuple      							=			median_of_a(A_  ,   A_  ,   start + ((end - start) // 2) + 1  ,   end - 1       )
    a						:		Optional[Any]      							=			partition(A_  ,   A_  ,   A_  ,   A_       )
    intro_sort(A_  ,   A_  ,   A_  ,   A_  ,   A_       )
    a						:		Dict      							=			p
  return insertion_sort(A_  ,   A_  ,   A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __lowercase    					=      input("""Enter numbers separated by a comma : """).strip()
 __lowercase    					=      [float(item) for item in user_input.split(""",""")]
 print(sort(unsorted))










'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





