




'''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   ( __lowerCAmelCase				,   __lowerCAmelCase				,   __lowerCAmelCase):


       @register_to_config
       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE = False    ,  )      ->  Tuple:



            '''simple docstring'''




            super().__init__()

            __snake_case    					=  nn.Linear(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE    ,  bias=__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  nn.Embedding(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
            __snake_case    					=  False

            __snake_case    					=  nn.Dropout(p=__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  TaConfig(
                d_model=__SCREAMING_SNAKE_CASE    ,  num_heads=__SCREAMING_SNAKE_CASE    ,  d_kv=__SCREAMING_SNAKE_CASE    ,  d_ff=__SCREAMING_SNAKE_CASE    ,  feed_forward_proj=__SCREAMING_SNAKE_CASE    ,  dropout_rate=__SCREAMING_SNAKE_CASE    ,  is_decoder=__SCREAMING_SNAKE_CASE    ,  is_encoder_decoder=__SCREAMING_SNAKE_CASE    ,  )
            __snake_case    					=  nn.ModuleList()
            for lyr_num in range(__SCREAMING_SNAKE_CASE  ):
                 __snake_case    					=  TaBlock(__SCREAMING_SNAKE_CASE  )
                 self.encoders.append(__SCREAMING_SNAKE_CASE  )

            __snake_case    					=  TaLayerNorm(__SCREAMING_SNAKE_CASE  )
            __snake_case    					=  nn.Dropout(p=__SCREAMING_SNAKE_CASE  )




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Dict:



            '''simple docstring'''




            __snake_case    					=  self.input_proj(__SCREAMING_SNAKE_CASE  )

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

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

            __snake_case    					=  self.dropout_pre(__SCREAMING_SNAKE_CASE  )

            # inverted the attention mask
            __snake_case    					=  encoder_inputs.size()
            __snake_case    					=  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )

            for lyr in self.encoders:
                 __snake_case    					=  lyr(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )[0]
            __snake_case    					=  self.layer_norm(__SCREAMING_SNAKE_CASE  )

            return self.dropout_post(__SCREAMING_SNAKE_CASE  ), encoder_inputs_mask












'''simple docstring'''



from transformers import ProcessorMixin





class     lowerCAmelCase   ( __lowerCAmelCase):
       __lowercase  :      Union[str, Any]        =			'''AutoFeatureExtractor'''
       __lowercase  :      List[Any]        =			'''AutoTokenizer'''






