code: 
  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    _snake_case      (  snake_case       ,		snake_case       ,		snake_case       ):
  
  
  
  
  
  
  
  			@register_to_config
  			def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = False		,     ):
  				super().__init__()
  
  				__magic_name__	:	Tuple			     =  nn.Linear(_a		,     _a		,     bias=_a	)
  
  				__magic_name__	:	List[str]			     =  nn.Embedding(_a		,     _a	)
  				__magic_name__	:	Tuple			     =  False
  
  				__magic_name__	:	Optional[Any]			     =  nn.Dropout(p=_a	)
  
  				__magic_name__	:	Optional[int]			     =  TaConfig(
  				    d_model=_a		,     num_heads=_a		,     d_kv=_a		,     d_ff=_a		,     feed_forward_proj=_a		,     dropout_rate=_a		,     is_decoder=_a		,     is_encoder_decoder=_a		,     )
  				__magic_name__	:	Any			     =  nn.ModuleList()
  				for lyr_num in range(_a	):
  					__magic_name__	:	Optional[int]			     =  TaBlock(_a	)
  					self.encoders.append(_a	)
  
  				__magic_name__	:	Union[str, Any]			     =  TaLayerNorm(_a	)
  				__magic_name__	:	Tuple			     =  nn.Dropout(p=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				__magic_name__	:	int			     =  self.input_proj(_a	)
  
  				# terminal relative positional encodings
  				__magic_name__	:	str			     =  encoder_inputs.shape[1]
  				__magic_name__	:	List[Any]			     =  torch.arange(_a		,     device=encoder_inputs.device	)
  
  				__magic_name__	:	Tuple			     =  encoder_inputs_mask.sum(-1	)
  				__magic_name__	:	Tuple			     =  torch.roll(input_positions.unsqueeze(0	)		,     tuple(seq_lens.tolist()	)		,     dims=0	)
  				x += self.position_encoding(_a	)
  
  				__magic_name__	:	Optional[int]			     =  self.dropout_pre(_a	)
  
  				# inverted the attention mask
  				__magic_name__	:	Any			     =  encoder_inputs.size()
  				__magic_name__	:	List[str]			     =  self.get_extended_attention_mask(_a		,     _a	)
  
  				for lyr in self.encoders:
  					__magic_name__	:	List[Any]			     =  lyr(_a		,     _a	)[0]
  				__magic_name__	:	Optional[int]			     =  self.layer_norm(_a	)
  
  				return self.dropout_post(_a	), encoder_inputs_mask
  
style_context: 
  from __future__ import annotations
  
  snake_case				:	List[str]							   =						"Alexander Joslin"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if number <= 0:
  		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
  		raise ValueError(_snake_case     )
  
  	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)
  
  	# starting position
  	__magic_name__	:	Any			     =  0
  	for i in range(1   ,     _snake_case     ):
  		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 2 <= number:
  			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 3 <= number:
  			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
  	return table[number]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  