
"""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 				a			(					lowercase						,		lowercase						,		lowercase      ):



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

          UpperCAmelCase__	:      Optional[int]     							=					nn.Embedding(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					nn.Embedding(UpperCamelCase_     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					False

          UpperCAmelCase__	:      str     							=					nn.Dropout(p=UpperCamelCase_      )

          UpperCAmelCase__	:      List[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_     ,	)

          UpperCAmelCase__	:      Any     							=					nn.ModuleList()
          for lyr_num in range(UpperCamelCase_      ):
              UpperCAmelCase__	:      Tuple     							=					TaBlock(UpperCamelCase_      )
              self.encoders.append(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					TaLayerNorm(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					nn.Dropout(p=UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Dict     							=					self.token_embedder(UpperCamelCase_      )

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

          UpperCAmelCase__	:      Optional[int]     							=					self.dropout_pre(UpperCamelCase_      )

          # inverted the attention mask
          UpperCAmelCase__	:      int     							=					encoder_input_tokens.size()
          UpperCAmelCase__	:      Tuple     							=					self.get_extended_attention_mask(UpperCamelCase_     ,	UpperCamelCase_      )

          for lyr in self.encoders:
              UpperCAmelCase__	:      Any     							=					lyr(UpperCamelCase_     ,	UpperCamelCase_      )[0]
          UpperCAmelCase__	:      List[str]     							=					self.layer_norm(UpperCamelCase_      )

          return self.dropout_post(UpperCamelCase_      ), encoder_inputs_mask








"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





