





"""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							(		a__						,			a__						,			a__		):






							@register_to_config
							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = False ,   )      ->					Optional[int]:
								super().__init__()

								a :						List[str]      =							nn.Embedding(lowerCAmelCase__ ,   lowerCAmelCase__ )

								a :						Dict      =							nn.Embedding(lowerCAmelCase__ ,   lowerCAmelCase__ )
								a :						int      =							False

								a :						str      =							nn.Dropout(p=lowerCAmelCase__ )

								a :						List[str]      =							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 :						int      =							nn.ModuleList()
								for lyr_num in range(lowerCAmelCase__ ):
									a :						int      =							TaBlock(lowerCAmelCase__ )
									self.encoders.append(lowerCAmelCase__ )

								a :						int      =							TaLayerNorm(lowerCAmelCase__ )
								a :						int      =							nn.Dropout(p=lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
								a :						Dict      =							self.token_embedder(lowerCAmelCase__ )

								a :						Any      =							encoder_input_tokens.shape[1]
								a :						str      =							torch.arange(lowerCAmelCase__ ,   device=encoder_input_tokens.device )
								x += self.position_encoding(lowerCAmelCase__ )

								a :						List[str]      =							self.dropout_pre(lowerCAmelCase__ )

								# inverted the attention mask
								a :						Optional[int]      =							encoder_input_tokens.size()
								a :						List[Any]      =							self.get_extended_attention_mask(lowerCAmelCase__ ,   lowerCAmelCase__ )

								for lyr in self.encoders:
									a :						str      =							lyr(lowerCAmelCase__ ,   lowerCAmelCase__ )[0]
								a :						int      =							self.layer_norm(lowerCAmelCase__ )

								return self.dropout_post(lowerCAmelCase__ ), encoder_inputs_mask









"""simple docstring"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


