


from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__				       =			{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
   from .tokenization_phobert import PhobertTokenizer

else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



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 						UpperCAmelCase__   (      A_      ,						A_      ,						A_		):



     '''simple docstring'''




     @register_to_config
     def __init__( self       :       Dict	,      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					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)

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

            _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)

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

            _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)

            # terminal relative positional encodings
            _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
            _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)

            _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
            _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
            x += self.position_encoding(UpperCamelCase		)

            _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)

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

            for lyr in self.encoders:
                   _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
            _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)

            return self.dropout_post(UpperCamelCase		), encoder_inputs_mask