




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 (       __magic_name__			,				__magic_name__			,				__magic_name__ ):

			"""simple docstring"""







			@register_to_config
			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__ = False			,						)    ->      Optional[Any]:



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

							__lowerCamelCase		       =    nn.Embedding(lowerCamelCase__			,						lowerCamelCase__							)

							__lowerCamelCase		       =    nn.Embedding(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    False

							__lowerCamelCase		       =    nn.Dropout(p=lowerCamelCase__							)

							__lowerCamelCase		       =    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__			,						)

							__lowerCamelCase		       =    nn.ModuleList()
							for lyr_num in range(lowerCamelCase__							):
											__lowerCamelCase		       =    TaBlock(lowerCamelCase__							)
											self.encoders.append(lowerCamelCase__							)

							__lowerCamelCase		       =    TaLayerNorm(lowerCamelCase__							)
							__lowerCamelCase		       =    nn.Dropout(p=lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.token_embedder(lowerCamelCase__							)

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

							__lowerCamelCase		       =    self.dropout_pre(lowerCamelCase__							)

							# inverted the attention mask
							__lowerCamelCase		       =    encoder_input_tokens.size()
							__lowerCamelCase		       =    self.get_extended_attention_mask(lowerCamelCase__			,						lowerCamelCase__							)

							for lyr in self.encoders:
											__lowerCamelCase		       =    lyr(lowerCamelCase__			,						lowerCamelCase__							)[0]
							__lowerCamelCase		       =    self.layer_norm(lowerCamelCase__							)

							return self.dropout_post(lowerCamelCase__							), encoder_inputs_mask








def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       bool:





				"""simple docstring"""


				return number & 1 == 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()


