import doctest
from collections import deque

import numpy as np




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Any)				->None:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						[2, 1, 2, -1]
          _lowerCamelCase     :		Union[str, Any] 						=						[1, 2, 3, 4]




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->list[float]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						len(self.first_signal)
          _lowerCamelCase     :		Tuple 						=						len(self.second_signal)

          _lowerCamelCase     :		Dict 						=						max(_UpperCamelCase    ,  _UpperCamelCase)

          # create a zero matrix of max_length x max_length
          _lowerCamelCase     :		Tuple 						=						[[0] * max_length for i in range(_UpperCamelCase)]

          # fills the smaller signal with zeros to make both signals of same length
          if length_first_signal < length_second_signal:
              self.first_signal += [0] * (max_length - length_first_signal)
          elif length_first_signal > length_second_signal:
              self.second_signal += [0] * (max_length - length_second_signal)
          for i in range(_UpperCamelCase):
              _lowerCamelCase     :		Tuple 						=						deque(self.second_signal)
              rotated_signal.rotate(_UpperCamelCase)
              for j, item in enumerate(_UpperCamelCase):
                  matrix[i][j] += item

        # multiply the matrix with the first signal
          _lowerCamelCase     :		Dict 						=						np.matmul(np.transpose(_UpperCamelCase)    ,  np.transpose(self.first_signal))

          # rounding-off to two decimal places
          return [round(_UpperCamelCase    ,  2) for i in final_signal]


if __name__ == "__main__":
    doctest.testmod()





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =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""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




