from typing import List

import numpy as np



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	{key: len(_A       ) for key, value in gen_kwargs.items() if isinstance(_A				,					_A       )}
 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				,					_A       )



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	[]
 for group_idx in range(_A       ):
  __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(_A				,					start + num_shards_to_add       )
  shards_indices_per_group.append(_A       )
 return shards_indices_per_group



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


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



def 	__A		(      _A       ):



 """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]				,					_A       )
     else gen_kwargs_list[0][key]
     for key in gen_kwargs_list[0]
 }






def 	__A		(      _A				,					_A       ):



 """simple docstring"""


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



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


