


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_ ( SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_   ):

   '''simple docstring'''




   @register_to_config
   def __init__(		self						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A						,    __A = False						,    ):






     """simple docstring"""





     super().__init__()

     lowerCamelCase			: str   =    nn.Linear(lowercase_						,    lowercase_						,    bias=lowercase_			)

     lowerCamelCase			: Optional[Any]   =    nn.Embedding(lowercase_						,    lowercase_			)
     lowerCamelCase			: Optional[int]   =    False

     lowerCamelCase			: Tuple   =    nn.Dropout(p=lowercase_			)

     lowerCamelCase			: Tuple   =    TaConfig(
         d_model=lowercase_						,    num_heads=lowercase_						,    d_kv=lowercase_						,    d_ff=lowercase_						,    feed_forward_proj=lowercase_						,    dropout_rate=lowercase_						,    is_decoder=lowercase_						,    is_encoder_decoder=lowercase_						,    )
     lowerCamelCase			: int   =    nn.ModuleList()
     for lyr_num in range(lowercase_			):
       lowerCamelCase			: Any   =    TaBlock(lowercase_			)
       self.encoders.append(lowercase_			)

     lowerCamelCase			: Optional[Any]   =    TaLayerNorm(lowercase_			)
     lowerCamelCase			: List[Any]   =    nn.Dropout(p=lowercase_			)






   def _snake_case   (		self						,    __A						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Optional[int]   =    self.input_proj(lowercase_			)

     # terminal relative positional encodings
     lowerCamelCase			: Dict   =    encoder_inputs.shape[1]
     lowerCamelCase			: Union[str, Any]   =    torch.arange(lowercase_						,    device=encoder_inputs.device			)

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

     lowerCamelCase			: int   =    self.dropout_pre(lowercase_			)

     # inverted the attention mask
     lowerCamelCase			: List[str]   =    encoder_inputs.size()
     lowerCamelCase			: int   =    self.get_extended_attention_mask(lowercase_						,    lowercase_			)

     for lyr in self.encoders:
       lowerCamelCase			: Optional[int]   =    lyr(lowercase_						,    lowercase_			)[0]
     lowerCamelCase			: List[Any]   =    self.layer_norm(lowercase_			)

     return self.dropout_post(lowercase_			), encoder_inputs_mask







def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  sequence.append(SCREAMING_SNAKE_CASE_     )

  lowerCamelCase			: str   =    0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(SCREAMING_SNAKE_CASE_     ):
    return None

  return index


if __name__ == "__main__":
   _snake_case					=					input('''Enter numbers separated by comma:\n''').strip()
   _snake_case					=					[int(item) for item in user_input.split(''',''')]

   _snake_case					=					input('''Enter a single number to be found in the list:\n''')
   _snake_case					=					int(target_input)
   _snake_case					=					sentinel_linear_search(sequence, target)
   if result is not None:
      print(f'''{target} found at positions: {result}''')
   else:
      print('''Not found''')



