



'''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       __SCREAMING_SNAKE_CASE	(						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase			):



     '''simple docstring'''






     @register_to_config
     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = False						,   ):
       super().__init__()

       _snake_case     		=				nn.Embedding(a_						,   a_	)

       _snake_case     		=				nn.Embedding(a_						,   a_	)
       _snake_case     		=				False

       _snake_case     		=				nn.Dropout(p=a_	)

       _snake_case     		=				TaConfig(
           vocab_size=a_						,   d_model=a_						,   num_heads=a_						,   d_kv=a_						,   d_ff=a_						,   dropout_rate=a_						,   feed_forward_proj=a_						,   is_decoder=a_						,   is_encoder_decoder=a_						,   )

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

       _snake_case     		=				TaLayerNorm(a_	)
       _snake_case     		=				nn.Dropout(p=a_	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.token_embedder(a_	)

       _snake_case     		=				encoder_input_tokens.shape[1]
       _snake_case     		=				torch.arange(a_						,   device=encoder_input_tokens.device	)
       x += self.position_encoding(a_	)

       _snake_case     		=				self.dropout_pre(a_	)

       # inverted the attention mask
       _snake_case     		=				encoder_input_tokens.size()
       _snake_case     		=				self.get_extended_attention_mask(a_						,   a_	)

       for lyr in self.encoders:
         _snake_case     		=				lyr(a_						,   a_	)[0]
       _snake_case     		=				self.layer_norm(a_	)

       return self.dropout_post(a_	), encoder_inputs_mask









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


