






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(       __UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase   ):



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

    """simple docstring"""






    super().__init__()

    lowercase__    :						Dict       =			nn.Linear(UpperCAmelCase__			,		UpperCAmelCase__			,		bias=UpperCAmelCase__						)

    lowercase__    :						Tuple       =			nn.Embedding(UpperCAmelCase__			,		UpperCAmelCase__						)
    lowercase__    :						str       =			False

    lowercase__    :						List[str]       =			nn.Dropout(p=UpperCAmelCase__						)

    lowercase__    :						Tuple       =			TaConfig(
        d_model=UpperCAmelCase__			,		num_heads=UpperCAmelCase__			,		d_kv=UpperCAmelCase__			,		d_ff=UpperCAmelCase__			,		feed_forward_proj=UpperCAmelCase__			,		dropout_rate=UpperCAmelCase__			,		is_decoder=UpperCAmelCase__			,		is_encoder_decoder=UpperCAmelCase__			,		)
    lowercase__    :						str       =			nn.ModuleList()
    for lyr_num in range(UpperCAmelCase__						):
       lowercase__    :						Union[str, Any]       =			TaBlock(UpperCAmelCase__						)
       self.encoders.append(UpperCAmelCase__						)

    lowercase__    :						int       =			TaLayerNorm(UpperCAmelCase__						)
    lowercase__    :						Union[str, Any]       =			nn.Dropout(p=UpperCAmelCase__						)







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			self.input_proj(UpperCAmelCase__						)

    # terminal relative positional encodings
    lowercase__    :						str       =			encoder_inputs.shape[1]
    lowercase__    :						Optional[Any]       =			torch.arange(UpperCAmelCase__			,		device=encoder_inputs.device						)

    lowercase__    :						int       =			encoder_inputs_mask.sum(-1						)
    lowercase__    :						List[Any]       =			torch.roll(input_positions.unsqueeze(0						)			,		tuple(seq_lens.tolist()						)			,		dims=0						)
    x += self.position_encoding(UpperCAmelCase__						)

    lowercase__    :						List[str]       =			self.dropout_pre(UpperCAmelCase__						)

    # inverted the attention mask
    lowercase__    :						int       =			encoder_inputs.size()
    lowercase__    :						List[Any]       =			self.get_extended_attention_mask(UpperCAmelCase__			,		UpperCAmelCase__						)

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

    return self.dropout_post(UpperCAmelCase__						), encoder_inputs_mask










def    a__    (	A       ):
   return int(A       ) if x - int(A       ) >= 0 else int(A       ) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()


