'''simple docstring'''

from typing import List

import numpy as np







def __UpperCamelCase( _A       :					dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Dict 					=	{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.'''
          )     )
   UpperCAmelCase__				:					Optional[Any] 					=	max(lists_lengths.values()	,      default=0     )
   return max(1	,      _A     )







def __UpperCamelCase( _A       :					int	,      _A       :					int     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	[]
   for group_idx in range(_A     ):
      UpperCAmelCase__				:					int 					=	num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      UpperCAmelCase__				:					Tuple 					=	shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      UpperCAmelCase__				:					List[Any] 					=	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'''
   UpperCAmelCase__				:					str 					=	_number_of_shards_in_gen_kwargs(_A     )
   if num_shards == 1:
      return [dict(_A     )]
   else:
      UpperCAmelCase__				:					Dict 					=	_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'''
   UpperCAmelCase__				:					str 					=	{len(_A     ) for value in gen_kwargs.values() if isinstance(_A	,      _A     )}
   UpperCAmelCase__				:					List[str] 					=	{}
   for size in list_sizes:
      UpperCAmelCase__				:					int 					=	list(range(_A     )     )
      rng.shuffle(indices_per_size[size]     )
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   UpperCAmelCase__				:					Any 					=	dict(_A     )
   for key, value in shuffled_kwargs.items():
      if isinstance(_A	,      _A     ):
         UpperCAmelCase__				:					Any 					=	[value[i] for i in indices_per_size[len(_A     )]]
   return shuffled_kwargs





'''simple docstring'''

import baseaa







def __UpperCamelCase( _A       :					str     ):
   '''simple docstring'''
   return baseaa.baaencode(string.encode('''utf-8'''     )     )







def __UpperCamelCase( _A       :					bytes     ):
   '''simple docstring'''
   return baseaa.baadecode(_A     ).decode('''utf-8'''     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[str]					=      'Hello World!'
  UpperCamelCase__  :						str					=      baseaa_encode(test)
  print(encoded)

  UpperCamelCase__  :						int					=      baseaa_decode(encoded)
  print(decoded)




