






'''simple docstring'''





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     {}
 lowerCamelCase__  =     tokenizer(example["content"]				,     truncation=lowercase__)["input_ids"]
 lowerCamelCase__  =     len(example["content"]) / len(output["input_ids"])
 return output


__A							:							Optional[Any]  							=		HfArgumentParser(PretokenizationArguments)
__A							:							Any  							=		parser.parse_args()
if args.num_workers is None:
 __A							:							Optional[Any]  							=		multiprocessing.cpu_count()
__A							:							Union[str, Any]  							=		AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__A							:							List[Any]  							=		time.time()
__A							:							Tuple  							=		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							:							Dict  							=		time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")









'''simple docstring'''





from __future__ import annotations
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     order

     # a_{0} ... a_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order
     # b_{0} ... b_{k}
     lowerCamelCase__  =     [1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     lowerCamelCase__  =     [0.0] * self.order
     # y[n-1] ... y[n-k]
     lowerCamelCase__  =     [0.0] * self.order

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     list[float]				,     __lowerCamelCase     :     list[float]     )							->							None:

     '''simple docstring'''




     if len(__lowerCamelCase     ) < self.order:
      lowerCamelCase__  =     [1.0, *a_coeffs]

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected a_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     if len(__lowerCamelCase     ) != self.order + 1:
      lowerCamelCase__  =     (
          f'''Expected b_coeffs to have {self.order + 1} elements '''
          f'''for {self.order}-order filter, got {len(__lowerCamelCase     )}'''
      )
      raise ValueError(__lowerCamelCase     )

     lowerCamelCase__  =     a_coeffs
     lowerCamelCase__  =     b_coeffs




    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     float     )							->							float:

     '''simple docstring'''




     lowerCamelCase__  =     0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1				,     self.order + 1     ):
      result += (
          self.b_coeffs[i] * self.input_history[i - 1]
          - self.a_coeffs[i] * self.output_history[i - 1]
      )

     lowerCamelCase__  =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     lowerCamelCase__  =     self.input_history[:-1]
     lowerCamelCase__  =     self.output_history[:-1]

     lowerCamelCase__  =     sample
     lowerCamelCase__  =     result

     return result

