

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      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				_lowerCAmelCase):




 @register_to_config
 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase = False  ,  ):
   super().__init__()

   __UpperCAmelCase	:  List[str]		=     nn.Linear(_UpperCAmelCase  ,  _UpperCAmelCase  ,  bias=_UpperCAmelCase							)

   __UpperCAmelCase	:  int		=     nn.Embedding(_UpperCAmelCase  ,  _UpperCAmelCase							)
   __UpperCAmelCase	:  Optional[int]		=     False

   __UpperCAmelCase	:  Optional[Any]		=     nn.Dropout(p=_UpperCAmelCase							)

   __UpperCAmelCase	:  List[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  ,  )
   __UpperCAmelCase	:  str		=     nn.ModuleList()
   for lyr_num in range(_UpperCAmelCase							):
     __UpperCAmelCase	:  Tuple		=     TaBlock(_UpperCAmelCase							)
     self.encoders.append(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     TaLayerNorm(_UpperCAmelCase							)
   __UpperCAmelCase	:  str		=     nn.Dropout(p=_UpperCAmelCase							)




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     self.input_proj(_UpperCAmelCase							)

   # terminal relative positional encodings
   __UpperCAmelCase	:  int		=     encoder_inputs.shape[1]
   __UpperCAmelCase	:  Dict		=     torch.arange(_UpperCAmelCase  ,  device=encoder_inputs.device							)

   __UpperCAmelCase	:  Union[str, Any]		=     encoder_inputs_mask.sum(-1							)
   __UpperCAmelCase	:  Optional[Any]		=     torch.roll(input_positions.unsqueeze(0							)  ,  tuple(seq_lens.tolist()							)  ,  dims=0							)
   x += self.position_encoding(_UpperCAmelCase							)

   __UpperCAmelCase	:  str		=     self.dropout_pre(_UpperCAmelCase							)

   # inverted the attention mask
   __UpperCAmelCase	:  List[Any]		=     encoder_inputs.size()
   __UpperCAmelCase	:  List[str]		=     self.get_extended_attention_mask(_UpperCAmelCase  ,  _UpperCAmelCase							)

   for lyr in self.encoders:
     __UpperCAmelCase	:  Optional[int]		=     lyr(_UpperCAmelCase  ,  _UpperCAmelCase							)[0]
   __UpperCAmelCase	:  Union[str, Any]		=     self.layer_norm(_UpperCAmelCase							)

   return self.dropout_post(_UpperCAmelCase							), encoder_inputs_mask








def  _snake_case		(snake_case_					:     int = 100    ,       snake_case_					:     int = 100  )							->		int:
  return max(
      sum(int(snake_case_  ) for x in str(base**power  )  )
      for base in range(snake_case_  )
      for power in range(snake_case_  )  )


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()





