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   _lowercase(     _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE				):



   """simple docstring"""





   @register_to_config
   def __init__(							self:    Any			,a:    Any			,a:    Optional[Any]			,a:    Optional[Any]			,a:    int			,a:    Dict			,a:    Union[str, Any]			,a:    Union[str, Any]			,a:    Any			,a:    Optional[Any]			,a:    Optional[int] = False			,):
    super().__init__()

    __UpperCAmelCase         =					nn.Linear(__A			,__A			,bias=__A	)

    __UpperCAmelCase         =					nn.Embedding(__A			,__A	)
    __UpperCAmelCase         =					False

    __UpperCAmelCase         =					nn.Dropout(p=__A	)

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

    __UpperCAmelCase         =					TaLayerNorm(__A	)
    __UpperCAmelCase         =					nn.Dropout(p=__A	)





   def 			snake_case	(							self:    Union[str, Any]			,a:    Optional[int]			,a:    int	):
    __UpperCAmelCase         =					self.input_proj(__A	)

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

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

    __UpperCAmelCase         =					self.dropout_pre(__A	)

    # inverted the attention mask
    __UpperCAmelCase         =					encoder_inputs.size()
    __UpperCAmelCase         =					self.get_extended_attention_mask(__A			,__A	)

    for lyr in self.encoders:
     __UpperCAmelCase         =					lyr(__A			,__A	)[0]
    __UpperCAmelCase         =					self.layer_norm(__A	)

    return self.dropout_post(__A	), encoder_inputs_mask




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					5381
 for x in s:
  __UpperCAmelCase         =					((hash_value << 5) + hash_value) + ord(lowerCAmelCase			)
 return hash_value & 0Xffff_ffff



