





from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__  ):
      A_						:				Optional[torch.FloatTensor]							=    None
      A_						:				torch.FloatTensor							=    None
      A_						:				Optional[Tuple[torch.FloatTensor]]							=    None
      A_						:				Optional[Tuple[torch.FloatTensor]]							=    None






class 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__  ):


      def __init__(self       :      Tuple				,			a__       :      Dict=1				,			a__       :      Optional[int]=0				,			a__       :      int=2				,			a__       :      Optional[Any]=512				,			a__       :      List[str]="cls"				,			a__       :      str=False				,			a__       :      Dict=True				,			**a__       :      Any				,			):





         """simple docstring"""



         super().__init__(pad_token_id=__lowercase				,			bos_token_id=__lowercase				,			eos_token_id=__lowercase				,			**__lowercase      )
         __snake_case							       =   project_dim
         __snake_case							       =   pooler_fn
         __snake_case							       =   learn_encoder
         __snake_case							       =   use_attention_mask






class 		SCREAMING_SNAKE_CASE__				(       UpperCAmelCase__  ):
      A_						:				List[Any]							=    [r"""pooler""", r"""logit_scale"""]
      A_						:				Optional[int]							=    [r"""position_ids""", r"""predictions.decoder.bias"""]
      A_						:				Dict							=    """roberta"""
      A_						:				Any							=    RobertaSeriesConfig


      def __init__(self       :      List[Any]				,			a__       :      List[str]      ):





         """simple docstring"""



         super().__init__(__lowercase      )
         __snake_case							       =   XLMRobertaModel(__lowercase      )
         __snake_case							       =   nn.Linear(config.hidden_size				,			config.project_dim      )
         __snake_case							       =   getattr(__lowercase				,			'''has_pre_transformation'''				,			__lowercase      )
         if self.has_pre_transformation:
            __snake_case							       =   nn.Linear(config.hidden_size				,			config.project_dim      )
            __snake_case							       =   nn.LayerNorm(config.hidden_size				,			eps=config.layer_norm_eps      )
         self.post_init()


      def    a (self       :      int				,			a__       :      Tuple = None				,			a__       :      List[str] = None				,			a__       :      str = None				,			a__       :      Any = None				,			a__       :      Optional[int] = None				,			a__       :      int = None				,			a__       :      Optional[int] = None				,			a__       :      Any = None				,			a__       :      Tuple = None				,			a__       :      str = None				,			a__       :      List[Any] = None				,			):





         """simple docstring"""



         __snake_case							       =   return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case							       =   self.base_model(
             input_ids=__lowercase				,			attention_mask=__lowercase				,			token_type_ids=__lowercase				,			position_ids=__lowercase				,			head_mask=__lowercase				,			inputs_embeds=__lowercase				,			encoder_hidden_states=__lowercase				,			encoder_attention_mask=__lowercase				,			output_attentions=__lowercase				,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states				,			return_dict=__lowercase				,			)

         if self.has_pre_transformation:
            __snake_case							       =   outputs['''hidden_states'''][-2]
            __snake_case							       =   self.pre_LN(__lowercase      )
            __snake_case							       =   self.transformation_pre(__lowercase      )

            return TransformationModelOutput(
                projection_state=__lowercase				,			last_hidden_state=outputs.last_hidden_state				,			hidden_states=outputs.hidden_states				,			attentions=outputs.attentions				,			)
         else:
            __snake_case							       =   self.transformation(outputs.last_hidden_state      )
            return TransformationModelOutput(
                projection_state=__lowercase				,			last_hidden_state=outputs.last_hidden_state				,			hidden_states=outputs.hidden_states				,			attentions=outputs.attentions				,			)





def 	lowerCAmelCase       (						lowerCAmelCase_ = 10**9       )->       int:
				lowerCAmelCase_       :					List[Any]			 = 1
				lowerCAmelCase_       :					Optional[int]			 = 2

				lowerCAmelCase_       :					Any			 = 0
				lowerCAmelCase_       :					str			 = 0
				lowerCAmelCase_       :					str			 = 0
				while perimeter <= max_perimeter:
								perimeters_sum += perimeter

								prev_value += 2 * value
								value += prev_value

								lowerCAmelCase_       :					Any			 = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
								i += 1

				return perimeters_sum


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