


'''simple docstring'''
from typing import List

import numpy as np







def       a_				(	UpperCamelCase_ ):
							A_			    =		{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."
														    ) )
							A_			    =		max(lists_lengths.values()						,  default=0 )
							return max(1						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		[]
							for group_idx in range(UpperCamelCase_ ):
														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(UpperCamelCase_						,  start + num_shards_to_add )
														shards_indices_per_group.append(UpperCamelCase_ )
							return shards_indices_per_group







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		_number_of_shards_in_gen_kwargs(UpperCamelCase_ )
							if num_shards == 1:
														return [dict(UpperCamelCase_ )]
							else:
														A_			    =		_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       a_				(	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       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		{len(UpperCamelCase_ ) for value in gen_kwargs.values() if isinstance(UpperCamelCase_						,  UpperCamelCase_ )}
							A_			    =		{}
							for size in list_sizes:
														A_			    =		list(range(UpperCamelCase_ ) )
														rng.shuffle(indices_per_size[size] )
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							A_			    =		dict(UpperCamelCase_ )
							for key, value in shuffled_kwargs.items():
														if isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
																					A_			    =		[value[i] for i in indices_per_size[len(UpperCamelCase_ )]]
							return shuffled_kwargs









'''simple docstring'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





