





"""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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							_lowerCamelCase       ):



	"""simple docstring"""

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


					"""simple docstring"""

					super().__init__()

					A__							 =       nn.Embedding(__a  ,					__a						)

					A__							 =       nn.Embedding(__a  ,					__a						)
					A__							 =       False

					A__							 =       nn.Dropout(p=__a						)

					A__							 =       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  ,					)

					A__							 =       nn.ModuleList()
					for lyr_num in range(__a						):
									A__							 =       TaBlock(__a						)
									self.encoders.append(__a						)

					A__							 =       TaLayerNorm(__a						)
					A__							 =       nn.Dropout(p=__a						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       self.token_embedder(__a						)

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

					A__							 =       self.dropout_pre(__a						)

					# inverted the attention mask
					A__							 =       encoder_input_tokens.size()
					A__							 =       self.get_extended_attention_mask(__a  ,					__a						)

					for lyr in self.encoders:
									A__							 =       lyr(__a  ,					__a						)[0]
					A__							 =       self.layer_norm(__a						)

					return self.dropout_post(__a						), encoder_inputs_mask










"""simple docstring"""






import numpy as np



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return 1 / (1 + np.exp(-vector				))



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return vector * sigmoid(lowerCAmelCase__				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


