code: 
  
  
  from typing import TypeVar
  
  from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
  from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset
  
  
  UpperCamelCase__				       =			TypeVar('DatasetType', Dataset, IterableDataset)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							DatasetType:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if isinstance(UpperCAmelCase_    ,     UpperCAmelCase_			):
                return _split_by_node_map_style_dataset(UpperCAmelCase_    ,     rank=UpperCAmelCase_    ,     world_size=UpperCAmelCase_			)
         else:
                return _split_by_node_iterable_dataset(UpperCAmelCase_    ,     rank=UpperCAmelCase_    ,     world_size=UpperCAmelCase_			)
  
  
  
style_context: 
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  class 						UpperCAmelCase__   (      A_      ,						A_      ,						A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       @register_to_config
       def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
              _lowercase					:				Any           =			False
  
              _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)
  
              _lowercase					:				Any           =			TaConfig(
                  d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
              _lowercase					:				Any           =			nn.ModuleList()
              for lyr_num in range(UpperCamelCase		):
                     _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                     self.encoders.append(UpperCamelCase		)
  
              _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)
  
              # terminal relative positional encodings
              _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
              _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)
  
              _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
              _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
              x += self.position_encoding(UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)
  
              # inverted the attention mask
              _lowercase					:				Optional[int]           =			encoder_inputs.size()
              _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)
  
              for lyr in self.encoders:
                     _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
              _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)
  
              return self.dropout_post(UpperCamelCase		), encoder_inputs_mask