
from typing import List

import numpy as np


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : Union[str, Any]											=					{key: len(UpperCamelCase__		) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__			,		UpperCamelCase__		)}
      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			,		UpperCamelCase__		)


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


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


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


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




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if index == number_of_items:
            return 0
      UpperCamelCase__     : str											=					0
      UpperCamelCase__     : Optional[Any]											=					0
      UpperCamelCase__     : int											=					knapsack(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		index + 1		)
      if weights[index] <= max_weight:
            UpperCamelCase__     : List[Any]											=					values[index] + knapsack(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		max_weight - weights[index]			,		index + 1		)
      return max(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


