






"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					int      )		->list:
						A__						:  Dict		  =  word.split()

						def justify(UpperCAmelCase__       :					list,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      ) -> str:
												A__						:  Any		  =  max_width - width
												A__						:  List[Any]		  =  len(UpperCAmelCase__      )
												if len(UpperCAmelCase__      ) == 1:
																		# if there is only word in line
																		# just insert overall_spaces_count for the remainder of line
																		return line[0] + " " * overall_spaces_count
												else:
																		A__						:  List[str]		  =  words_count - 1
																		# num_spaces_between_words_list[i] : tells you to insert
																		# num_spaces_between_words_list[i] spaces
																		# after word on line[i]
																		A__						:  Dict		  =  spaces_to_insert_between_words * [
																		    overall_spaces_count // spaces_to_insert_between_words
																		]
																		A__						:  Any		  =  (
																		    overall_spaces_count % spaces_to_insert_between_words
																		)
																		# distribute spaces via round robin to the left words
																		for i in range(UpperCAmelCase__      ):
																								num_spaces_between_words_list[i] += 1
																		A__						:  Tuple		  =  []
																		for i in range(UpperCAmelCase__      ):
																								# add the word
																								aligned_words_list.append(line[i]      )
																								# add the spaces to insert
																								aligned_words_list.append(num_spaces_between_words_list[i] * """ """      )
																		# just add the last word to the sentence
																		aligned_words_list.append(line[-1]      )
																		# join the aligned words list to form a justified line
																		return "".join(UpperCAmelCase__      )

						A__						:  int		  =  []
						A__						:  list[str]		  =  []
						A__						:  Any		  =  0
						for word in words:
												if width + len(UpperCAmelCase__      ) + len(UpperCAmelCase__      ) <= max_width:
																		# keep adding words until we can fill out max_width
																		# width = sum of length of all words (without overall_spaces_count)
																		# len(word) = length of current word
																		# len(line) = number of overall_spaces_count to insert between words
																		line.append(UpperCAmelCase__      )
																		width += len(UpperCAmelCase__      )
												else:
																		# justify the line and add it to result
																		answer.append(justify(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )      )
																		# reset new line and new width
																		A__  ,  A__						:  Optional[int]		  =  [word], len(UpperCAmelCase__      )
						A__						:  int		  =  max_width - width - len(UpperCAmelCase__      )
						answer.append(""" """.join(UpperCAmelCase__      ) + (remaining_spaces + 1) * """ """      )
						return answer


if __name__ == "__main__":
						from doctest import testmod

						testmod()














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


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

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

A_    =							time.time()
A_    =							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')

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

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






