






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 						lowercase_   (      UpperCAmelCase__					,    UpperCAmelCase__					,    UpperCAmelCase__  ):






      '''simple docstring'''




      @register_to_config
      def __init__( self :		Tuple					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		float					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		bool = False					,   )       ->Tuple:




       """simple docstring"""


       super().__init__()

       a						    = nn.Embedding(__lowercase					,   __lowercase    )

       a						    = nn.Embedding(__lowercase					,   __lowercase    )
       a						    = False

       a						    = nn.Dropout(p=__lowercase    )

       a						    = TaConfig(
           vocab_size=__lowercase					,   d_model=__lowercase					,   num_heads=__lowercase					,   d_kv=__lowercase					,   d_ff=__lowercase					,   dropout_rate=__lowercase					,   feed_forward_proj=__lowercase					,   is_decoder=__lowercase					,   is_encoder_decoder=__lowercase					,   )

       a						    = nn.ModuleList()
       for lyr_num in range(__lowercase    ):
        a						    = TaBlock(__lowercase    )
        self.encoders.append(__lowercase    )

       a						    = TaLayerNorm(__lowercase    )
       a						    = nn.Dropout(p=__lowercase    )







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		Dict					,   __UpperCAmelCase :		str    )       ->int:




       """simple docstring"""


       a						    = self.token_embedder(__lowercase    )

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

       a						    = self.dropout_pre(__lowercase    )

       # inverted the attention mask
       a						    = encoder_input_tokens.size()
       a						    = self.get_extended_attention_mask(__lowercase					,   __lowercase    )

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

       return self.dropout_post(__lowercase    ), encoder_inputs_mask








def 		lowerCamelCase__       (						):







	'''simple docstring'''





	for n in range(1	,      1000000				):
		yield n * (n + 1) // 2


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	1
	snake_case_						       =	2
	while i * i <= n:
		snake_case_						       =	0
		while n % i == 0:
			n //= i
			multiplicity += 1
		divisors_count *= multiplicity + 1
		i += 1
	if n > 1:
		divisors_count *= 2
	return divisors_count


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	return next(i for i in triangle_number_generator() if count_divisors(_A				) > 500				)


if __name__ == "__main__":
		print(solution())
