






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__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_						):





					"""simple docstring"""






					@register_to_config
					def __init__( self					:  Any							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  float							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  int							,		__A					:  str							,		__A					:  bool = False							,		):
						super().__init__()

						snake_case__			:				Optional[int]       =  nn.Embedding(__A							,		__A	)

						snake_case__			:				List[Any]       =  nn.Embedding(__A							,		__A	)
						snake_case__			:				str       =  False

						snake_case__			:				Optional[int]       =  nn.Dropout(p=__A	)

						snake_case__			:				Optional[int]       =  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__			:				int       =  nn.ModuleList()
						for lyr_num in range(__A	):
							snake_case__			:				int       =  TaBlock(__A	)
							self.encoders.append(__A	)

						snake_case__			:				Optional[Any]       =  TaLayerNorm(__A	)
						snake_case__			:				int       =  nn.Dropout(p=__A	)






					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				Dict       =  self.token_embedder(__A	)

						snake_case__			:				Tuple       =  encoder_input_tokens.shape[1]
						snake_case__			:				List[Any]       =  torch.arange(__A							,		device=encoder_input_tokens.device	)
						x += self.position_encoding(__A	)

						snake_case__			:				Optional[Any]       =  self.dropout_pre(__A	)

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

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

						return self.dropout_post(__A	), encoder_inputs_mask











__lowerCamelCase :      Optional[int]										=				"""Tobias Carryer"""

from time import time



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  List[Any]							,		__A					:  Optional[int]							,		__A					:  List[str]							,		__A					:  Dict=int(time()	)	):  # noqa: B008
						snake_case__			:				List[Any]       =  multiplier
						snake_case__			:				Optional[int]       =  increment
						snake_case__			:				Optional[int]       =  modulo
						snake_case__			:				Union[str, Any]       =  seed






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Union[str, Any]       =  (self.multiplier * self.seed + self.increment) % self.modulo
						return self.seed


if __name__ == "__main__":
	# Show the LCG in action.
	__lowerCamelCase :      int										=				LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
	while True:
		print(lcg.next_number())



