






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 (				snake_case__			,       snake_case__			,       snake_case__ ):







		"""simple docstring"""



		@register_to_config
		def __init__(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       float						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       bool = False						,)    ->   List[str]:


						'''simple docstring'''
						super().__init__()

						A	 =      nn.Embedding(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

						A	 =      nn.Embedding(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						A	 =      False

						A	 =      nn.Dropout(p=_SCREAMING_SNAKE_CASE		)

						A	 =      TaConfig(
						    vocab_size=_SCREAMING_SNAKE_CASE						,d_model=_SCREAMING_SNAKE_CASE						,num_heads=_SCREAMING_SNAKE_CASE						,d_kv=_SCREAMING_SNAKE_CASE						,d_ff=_SCREAMING_SNAKE_CASE						,dropout_rate=_SCREAMING_SNAKE_CASE						,feed_forward_proj=_SCREAMING_SNAKE_CASE						,is_decoder=_SCREAMING_SNAKE_CASE						,is_encoder_decoder=_SCREAMING_SNAKE_CASE						,)

						A	 =      nn.ModuleList()
						for lyr_num in range(_SCREAMING_SNAKE_CASE		):
										A	 =      TaBlock(_SCREAMING_SNAKE_CASE		)
										self.encoders.append(_SCREAMING_SNAKE_CASE		)

						A	 =      TaLayerNorm(_SCREAMING_SNAKE_CASE		)
						A	 =      nn.Dropout(p=_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Dict						,_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.token_embedder(_SCREAMING_SNAKE_CASE		)

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

						A	 =      self.dropout_pre(_SCREAMING_SNAKE_CASE		)

						# inverted the attention mask
						A	 =      encoder_input_tokens.size()
						A	 =      self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

						for lyr in self.encoders:
										A	 =      lyr(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)[0]
						A	 =      self.layer_norm(_SCREAMING_SNAKE_CASE		)

						return self.dropout_post(_SCREAMING_SNAKE_CASE		), encoder_inputs_mask













from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache





