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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @register_to_config
 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       bool = False	,			)	->int:



   '''simple docstring'''



   super().__init__()

   lowerCamelCase__:				Any        =nn.Embedding(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Dict        =nn.Embedding(UpperCAmelCase_	,			UpperCAmelCase_)
   lowerCamelCase__:				Any        =False

   lowerCamelCase__:				List[str]        =nn.Dropout(p=UpperCAmelCase_)

   lowerCamelCase__:				str        =TaConfig(
       vocab_size=UpperCAmelCase_	,			d_model=UpperCAmelCase_	,			num_heads=UpperCAmelCase_	,			d_kv=UpperCAmelCase_	,			d_ff=UpperCAmelCase_	,			dropout_rate=UpperCAmelCase_	,			feed_forward_proj=UpperCAmelCase_	,			is_decoder=UpperCAmelCase_	,			is_encoder_decoder=UpperCAmelCase_	,			)

   lowerCamelCase__:				int        =nn.ModuleList()
   for lyr_num in range(UpperCAmelCase_):
     lowerCamelCase__:				str        =TaBlock(UpperCAmelCase_)
     self.encoders.append(UpperCAmelCase_)

   lowerCamelCase__:				int        =TaLayerNorm(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =nn.Dropout(p=UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       Any)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.token_embedder(UpperCAmelCase_)

   lowerCamelCase__:				Any        =encoder_input_tokens.shape[1]
   lowerCamelCase__:				Union[str, Any]        =torch.arange(UpperCAmelCase_	,			device=encoder_input_tokens.device)
   x += self.position_encoding(UpperCAmelCase_)

   lowerCamelCase__:				Optional[Any]        =self.dropout_pre(UpperCAmelCase_)

   # inverted the attention mask
   lowerCamelCase__:				Optional[int]        =encoder_input_tokens.size()
   lowerCamelCase__:				Optional[Any]        =self.get_extended_attention_mask(UpperCAmelCase_	,			UpperCAmelCase_)

   for lyr in self.encoders:
     lowerCamelCase__:				Optional[int]        =lyr(UpperCAmelCase_	,			UpperCAmelCase_)[0]
   lowerCamelCase__:				int        =self.layer_norm(UpperCAmelCase_)

   return self.dropout_post(UpperCAmelCase_), encoder_inputs_mask








def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Tuple:







  """simple docstring"""





  assert x is not None
  assert y is not None

  lowerCamelCase__:				Any        =len(__a		)
  lowerCamelCase__:				int        =len(__a		)

  # declaring the array for storing the dp values
  lowerCamelCase__:				List[Any]        =[[0] * (n + 1) for _ in range(m + 1		)]  # noqa: E741

  for i in range(1     ,   m + 1		):
    for j in range(1     ,   n + 1		):
      lowerCamelCase__:				str        =1 if x[i - 1] == y[j - 1] else 0

      lowerCamelCase__:				str        =max(l[i - 1][j]     ,   l[i][j - 1]     ,   l[i - 1][j - 1] + match		)

  lowerCamelCase__:				Any        =""
  lowerCamelCase__  , lowerCamelCase__:				str        =m, n
  while i > 0 and j > 0:
    lowerCamelCase__:				Union[str, Any]        =1 if x[i - 1] == y[j - 1] else 0

    if l[i][j] == l[i - 1][j - 1] + match:
      if match == 1:
        lowerCamelCase__:				Any        =x[i - 1] + seq
      i -= 1
      j -= 1
    elif l[i][j] == l[i - 1][j]:
      i -= 1
    else:
      j -= 1

  return l[m][n], seq


if __name__ == "__main__":
     __A     =    "AGGTAB"
     __A     =    "GXTXAYB"
     __A     =    4
     __A     =    "GTAB"

     __A ,     __A     =    longest_common_subsequence(a, b)
     print("len =", ln, ", sub-sequence =", subseq)
     import doctest

     doctest.testmod()

