"""simple docstring"""



from typing import List

import numpy as np





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							{key: len(snake_case) for key, value in gen_kwargs.items() if isinstance(snake_case,      snake_case)}
     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.'''
              ))
     __snake_case				=							max(lists_lengths.values(),      default=0)
     return max(1,      snake_case)





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





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





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





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



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}