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        a_				(		SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 @register_to_config
 def __init__(    self      :						str    ,					lowercase__      :						int    ,					lowercase__      :						int    ,					lowercase__      :						int    ,					lowercase__      :						float    ,					lowercase__      :						int    ,					lowercase__      :						int    ,					lowercase__      :						int    ,					lowercase__      :						int    ,					lowercase__      :						str    ,					lowercase__      :						bool = False    ,					):







        '''simple docstring'''



        super().__init__()

        lowerCAmelCase__  			=      nn.Linear(lowercase__    ,					lowercase__    ,					bias=lowercase__)

        lowerCAmelCase__  			=      nn.Embedding(lowercase__    ,					lowercase__)
        lowerCAmelCase__  			=      False

        lowerCAmelCase__  			=      nn.Dropout(p=lowercase__)

        lowerCAmelCase__  			=      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__  			=      nn.ModuleList()
        for lyr_num in range(lowercase__):
               lowerCAmelCase__  			=      TaBlock(lowercase__)
               self.encoders.append(lowercase__)

        lowerCAmelCase__  			=      TaLayerNorm(lowercase__)
        lowerCAmelCase__  			=      nn.Dropout(p=lowercase__)




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						List[str]    ,					lowercase__      :						Tuple):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.input_proj(lowercase__)

        # terminal relative positional encodings
        lowerCAmelCase__  			=      encoder_inputs.shape[1]
        lowerCAmelCase__  			=      torch.arange(lowercase__    ,					device=encoder_inputs.device)

        lowerCAmelCase__  			=      encoder_inputs_mask.sum(-1)
        lowerCAmelCase__  			=      torch.roll(input_positions.unsqueeze(0)    ,					tuple(seq_lens.tolist())    ,					dims=0)
        x += self.position_encoding(lowercase__)

        lowerCAmelCase__  			=      self.dropout_pre(lowercase__)

        # inverted the attention mask
        lowerCAmelCase__  			=      encoder_inputs.size()
        lowerCAmelCase__  			=      self.get_extended_attention_mask(lowercase__    ,					lowercase__)

        for lyr in self.encoders:
               lowerCAmelCase__  			=      lyr(lowercase__    ,					lowercase__)[0]
        lowerCAmelCase__  			=      self.layer_norm(lowercase__)

        return self.dropout_post(lowercase__), encoder_inputs_mask

def    __lowerCamelCase	(		lowerCAmelCase__			):
       return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
