


"""simple docstring"""







from typing import List

import numpy as np


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







  a						:		Union[str, Any]      							=			{key: len(A_       ) for key, value in gen_kwargs.items() if isinstance(A_  ,   A_       )}
  if len(set(lists_lengths.values()       )       ) > 1:
    raise RuntimeError(
        (
            "Sharding is ambiguous for this dataset: "
            + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
            + "\n".join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()       )
            + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
            + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
        )       )
  a						:		str      							=			max(lists_lengths.values()  ,   default=0       )
  return max(1  ,   A_       )


def      lowercase			(				A_  ,   A_       )->      List[range]:
  '''simple docstring'''







  a						:		Tuple      							=			[]
  for group_idx in range(A_       ):
    a						:		Union[str, Any]      							=			num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
    if num_shards_to_add == 0:
      break
    a						:		str      							=			shards_indices_per_group[-1].stop if shards_indices_per_group else 0
    a						:		Tuple      							=			range(A_  ,   start + num_shards_to_add       )
    shards_indices_per_group.append(A_       )
  return shards_indices_per_group


def      lowercase			(				A_  ,   A_       )->      List[dict]:
  '''simple docstring'''







  a						:		Tuple      							=			_number_of_shards_in_gen_kwargs(A_       )
  if num_shards == 1:
    return [dict(A_       )]
  else:
    a						:		Tuple      							=			_distribute_shards(num_shards=A_  ,   max_num_jobs=A_       )
    return [
        {
            key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
            if isinstance(A_  ,   A_       )
            else value
            for key, value in gen_kwargs.items()
        }
        for group_idx in range(len(A_       )       )
    ]


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







  return {
      key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
      if isinstance(gen_kwargs_list[0][key]  ,   A_       )
      else gen_kwargs_list[0][key]
      for key in gen_kwargs_list[0]
  }







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







  a						:		Union[str, Any]      							=			{len(A_       ) for value in gen_kwargs.values() if isinstance(A_  ,   A_       )}
  a						:		Optional[int]      							=			{}
  for size in list_sizes:
    a						:		Optional[Any]      							=			list(range(A_       )       )
    rng.shuffle(indices_per_size[size]       )
  # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
  a						:		Union[str, Any]      							=			dict(A_       )
  for key, value in shuffled_kwargs.items():
    if isinstance(A_  ,   A_       ):
      a						:		int      							=			[value[i] for i in indices_per_size[len(A_       )]]
  return shuffled_kwargs









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


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

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

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       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''')

_UpperCAmelCase		:   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''')




