
'''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 			lowerCAmelCase__ (							UpperCamelCase       ,				UpperCamelCase       ,				UpperCamelCase							):



  '''simple docstring'''






  @register_to_config
  def __init__(self	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			float						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			bool = False						,			) ->							int:
        super().__init__()

        snake_case_    							=				nn.Linear(lowerCAmelCase_						,			lowerCAmelCase_						,			bias=lowerCAmelCase_     )

        snake_case_    							=				nn.Embedding(lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				False

        snake_case_    							=				nn.Dropout(p=lowerCAmelCase_     )

        snake_case_    							=				TaConfig(
            d_model=lowerCAmelCase_						,			num_heads=lowerCAmelCase_						,			d_kv=lowerCAmelCase_						,			d_ff=lowerCAmelCase_						,			feed_forward_proj=lowerCAmelCase_						,			dropout_rate=lowerCAmelCase_						,			is_decoder=lowerCAmelCase_						,			is_encoder_decoder=lowerCAmelCase_						,			)
        snake_case_    							=				nn.ModuleList()
        for lyr_num in range(lowerCAmelCase_     ):
              snake_case_    							=				TaBlock(lowerCAmelCase_     )
              self.encoders.append(lowerCAmelCase_     )

        snake_case_    							=				TaLayerNorm(lowerCAmelCase_     )
        snake_case_    							=				nn.Dropout(p=lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Optional[Any]     ) ->							Optional[Any]:
        snake_case_    							=				self.input_proj(lowerCAmelCase_     )

        # terminal relative positional encodings
        snake_case_    							=				encoder_inputs.shape[1]
        snake_case_    							=				torch.arange(lowerCAmelCase_						,			device=encoder_inputs.device     )

        snake_case_    							=				encoder_inputs_mask.sum(-1     )
        snake_case_    							=				torch.roll(input_positions.unsqueeze(0     )						,			tuple(seq_lens.tolist()     )						,			dims=0     )
        x += self.position_encoding(lowerCAmelCase_     )

        snake_case_    							=				self.dropout_pre(lowerCAmelCase_     )

        # inverted the attention mask
        snake_case_    							=				encoder_inputs.size()
        snake_case_    							=				self.get_extended_attention_mask(lowerCAmelCase_						,			lowerCAmelCase_     )

        for lyr in self.encoders:
              snake_case_    							=				lyr(lowerCAmelCase_						,			lowerCAmelCase_     )[0]
        snake_case_    							=				self.layer_norm(lowerCAmelCase_     )

        return self.dropout_post(lowerCAmelCase_     ), encoder_inputs_mask


def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

