

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

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ={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.'
              )				)
     _SCREAMING_SNAKE_CASE							    =max(lists_lengths.values()							, default=0				)
     return max(1							, _UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			List[range]:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     dict							, _UpperCamelCase						:     int				)      ->			List[dict]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =_number_of_shards_in_gen_kwargs(_UpperCamelCase				)
     if num_shards == 1:
          return [dict(_UpperCamelCase				)]
     else:
          _SCREAMING_SNAKE_CASE							    =_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        _lowerCAmelCase					(						_UpperCamelCase						:     List[dict]				)      ->			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]							, _UpperCamelCase				)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }







def        _lowerCAmelCase					(						_UpperCamelCase						:     np.random.Generator							, _UpperCamelCase						:     dict				)      ->			dict:
     """simple docstring"""




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







'''simple docstring'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()