
'''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  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = False							,		):

       '''simple docstring'''

       super().__init__()

       a_      :	List[str]      =							nn.Embedding(lowerCAmelCase_							,		lowerCAmelCase_ )

       a_      :	str      =							nn.Embedding(lowerCAmelCase_							,		lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							False

       a_      :	int      =							nn.Dropout(p=lowerCAmelCase_ )

       a_      :	Tuple      =							TaConfig(
           vocab_size=lowerCAmelCase_							,		d_model=lowerCAmelCase_							,		num_heads=lowerCAmelCase_							,		d_kv=lowerCAmelCase_							,		d_ff=lowerCAmelCase_							,		dropout_rate=lowerCAmelCase_							,		feed_forward_proj=lowerCAmelCase_							,		is_decoder=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		)

       a_      :	Any      =							nn.ModuleList()
       for lyr_num in range(lowerCAmelCase_ ):
         a_      :	Dict      =							TaBlock(lowerCAmelCase_ )
         self.encoders.append(lowerCAmelCase_ )

       a_      :	int      =							TaLayerNorm(lowerCAmelCase_ )
       a_      :	int      =							nn.Dropout(p=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Dict      =							self.token_embedder(lowerCAmelCase_ )

       a_      :	int      =							encoder_input_tokens.shape[1]
       a_      :	Optional[Any]      =							torch.arange(lowerCAmelCase_							,		device=encoder_input_tokens.device )
       x += self.position_encoding(lowerCAmelCase_ )

       a_      :	List[Any]      =							self.dropout_pre(lowerCAmelCase_ )

       # inverted the attention mask
       a_      :	Tuple      =							encoder_input_tokens.size()
       a_      :	Optional[int]      =							self.get_extended_attention_mask(lowerCAmelCase_							,		lowerCAmelCase_ )

       for lyr in self.encoders:
         a_      :	Optional[Any]      =							lyr(lowerCAmelCase_							,		lowerCAmelCase_ )[0]
       a_      :	Dict      =							self.layer_norm(lowerCAmelCase_ )

       return self.dropout_post(lowerCAmelCase_ ), encoder_inputs_mask




'''simple docstring'''

from torch import nn






def 	_snake_case						(      A_       :    Dict   ):
  """simple docstring"""







  if act_fn in ["swish", "silu"]:
    return nn.SiLU()
  elif act_fn == "mish":
    return nn.Mish()
  elif act_fn == "gelu":
    return nn.GELU()
  else:
    raise ValueError(f'''Unsupported activation function: {act_fn}'''   )


