



"""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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@register_to_config
		def __init__(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase = False							,    ) ->						Dict:







								'''simple docstring'''

								super().__init__()

								snake_case_   : List[Any]						=			nn.Embedding(_lowercase							,    _lowercase   )

								snake_case_   : str						=			nn.Embedding(_lowercase							,    _lowercase   )
								snake_case_   : Dict						=			False

								snake_case_   : Optional[int]						=			nn.Dropout(p=_lowercase   )

								snake_case_   : Any						=			TaConfig(
								    vocab_size=_lowercase							,    d_model=_lowercase							,    num_heads=_lowercase							,    d_kv=_lowercase							,    d_ff=_lowercase							,    dropout_rate=_lowercase							,    feed_forward_proj=_lowercase							,    is_decoder=_lowercase							,    is_encoder_decoder=_lowercase							,    )

								snake_case_   : Optional[Any]						=			nn.ModuleList()
								for lyr_num in range(_lowercase   ):
														snake_case_   : Dict						=			TaBlock(_lowercase   )
														self.encoders.append(_lowercase   )

								snake_case_   : Tuple						=			TaLayerNorm(_lowercase   )
								snake_case_   : List[str]						=			nn.Dropout(p=_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : List[str]						=			self.token_embedder(_lowercase   )

								snake_case_   : Optional[Any]						=			encoder_input_tokens.shape[1]
								snake_case_   : str						=			torch.arange(_lowercase							,    device=encoder_input_tokens.device   )
								x += self.position_encoding(_lowercase   )

								snake_case_   : Optional[Any]						=			self.dropout_pre(_lowercase   )

								# inverted the attention mask
								snake_case_   : Optional[int]						=			encoder_input_tokens.size()
								snake_case_   : Optional[int]						=			self.get_extended_attention_mask(_lowercase							,    _lowercase   )

								for lyr in self.encoders:
														snake_case_   : Union[str, Any]						=			lyr(_lowercase							,    _lowercase   )[0]
								snake_case_   : str						=			self.layer_norm(_lowercase   )

								return self.dropout_post(_lowercase   ), encoder_inputs_mask






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						return " ".join(input_str.split()[::-1]    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

