

from typing import List

import numpy as np




def 			UpperCAmelCase__				(			_A      : dict							):





  '''simple docstring'''







  a__  ={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__  =max(lists_lengths.values()	,      default=0							)
  return max(1	,      _A							)




def 			UpperCAmelCase__				(			_A      : int	,      _A      : int							):





  '''simple docstring'''







  a__  =[]
  for group_idx in range(_A							):
    a__  =num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
    if num_shards_to_add == 0:
      break
    a__  =shards_indices_per_group[-1].stop if shards_indices_per_group else 0
    a__  =range(_A	,      start + num_shards_to_add							)
    shards_indices_per_group.append(_A							)
  return shards_indices_per_group




def 			UpperCAmelCase__				(			_A      : dict	,      _A      : int							):





  '''simple docstring'''







  a__  =_number_of_shards_in_gen_kwargs(_A							)
  if num_shards == 1:
    return [dict(_A							)]
  else:
    a__  =_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 			UpperCAmelCase__				(			_A      : List[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 			UpperCAmelCase__				(			_A      : np.random.Generator	,      _A      : dict							):





  '''simple docstring'''







  a__  ={len(_A							) for value in gen_kwargs.values() if isinstance(_A	,      _A							)}
  a__  ={}
  for size in list_sizes:
    a__  =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__  =dict(_A							)
  for key, value in shuffled_kwargs.items():
    if isinstance(_A	,      _A							):
      a__  =[value[i] for i in indices_per_size[len(_A							)]]
  return shuffled_kwargs



from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
