


"""simple docstring"""





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_(       a__							,					a__							,					a__			):






     @register_to_config
     def __init__(	self  :					List[str]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					List[Any] = False					,				):
           super().__init__()

           lowerCAmelCase					:	int       =   nn.Embedding(UpperCamelCase_					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[Any]       =   nn.Embedding(UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	Any       =   False

           lowerCAmelCase					:	int       =   nn.Dropout(p=UpperCamelCase_				)

           lowerCAmelCase					:	Optional[Any]       =   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					:	str       =   nn.ModuleList()
           for lyr_num in range(UpperCamelCase_				):
                 lowerCAmelCase					:	Tuple       =   TaBlock(UpperCamelCase_				)
                 self.encoders.append(UpperCamelCase_				)

           lowerCAmelCase					:	Tuple       =   TaLayerNorm(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   nn.Dropout(p=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[Any]					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	str       =   self.token_embedder(UpperCamelCase_				)

           lowerCAmelCase					:	Dict       =   encoder_input_tokens.shape[1]
           lowerCAmelCase					:	str       =   torch.arange(UpperCamelCase_					,				device=encoder_input_tokens.device				)
           x += self.position_encoding(UpperCamelCase_				)

           lowerCAmelCase					:	Any       =   self.dropout_pre(UpperCamelCase_				)

           # inverted the attention mask
           lowerCAmelCase					:	Union[str, Any]       =   encoder_input_tokens.size()
           lowerCAmelCase					:	Union[str, Any]       =   self.get_extended_attention_mask(UpperCamelCase_					,				UpperCamelCase_				)

           for lyr in self.encoders:
                 lowerCAmelCase					:	Optional[int]       =   lyr(UpperCamelCase_					,				UpperCamelCase_				)[0]
           lowerCAmelCase					:	Any       =   self.layer_norm(UpperCamelCase_				)

           return self.dropout_post(UpperCamelCase_				), encoder_inputs_mask






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int = 4000000			):
      lowerCAmelCase					:	int       =   [0, 1]
      lowerCAmelCase					:	List[str]       =   0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]			)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCAmelCase					:	int       =   0
      for j in range(len(_snake_case			) - 1			):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
 print(f"""{solution() = }""")


