
"""simple docstring"""





from typing import List

import numpy as np







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Dict			     =    {key: len(__lowerCamelCase  ) for key, value in gen_kwargs.items() if isinstance(__lowerCamelCase,		__lowerCamelCase  )}
   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_					:					List[str]			     =    max(lists_lengths.values(),		default=0  )
   return max(1,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[int]			     =    []
   for group_idx in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					Union[str, Any]			     =    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      UpperCAmelCase_					:					Optional[Any]			     =    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      UpperCAmelCase_					:					List[Any]			     =    range(__lowerCamelCase,		start + num_shards_to_add  )
      shards_indices_per_group.append(__lowerCamelCase  )
   return shards_indices_per_group







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    _number_of_shards_in_gen_kwargs(__lowerCamelCase  )
   if num_shards == 1:
      return [dict(__lowerCamelCase  )]
   else:
      UpperCAmelCase_					:					Any			     =    _distribute_shards(num_shards=__lowerCamelCase,		max_num_jobs=__lowerCamelCase  )
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(__lowerCamelCase,		__lowerCamelCase  )
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(__lowerCamelCase  )  )
      ]







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







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








'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


if __name__ == "__main__":
   print(solution())




