


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 	__lowerCamelCase			(_a						):
       _lowercase											=     None
       _lowercase											=     None
       _lowercase											=     None
       _lowercase											=     None



class 	__lowerCamelCase			(_a						):







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






            '''simple docstring'''




            super().__init__(pad_token_id=A_,bos_token_id=A_,eos_token_id=A_,**A_					)
            __UpperCamelCase =			project_dim
            __UpperCamelCase =			pooler_fn
            __UpperCamelCase =			learn_encoder
            __UpperCamelCase =			use_attention_mask



class 	__lowerCamelCase			(_a						):
       _lowercase											=     [R"""pooler""", R"""logit_scale"""]
       _lowercase											=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       _lowercase											=     """roberta"""
       _lowercase											=     RobertaSeriesConfig







       def __init__(			self: int,A_: List[str]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[Any],A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[torch.Tensor] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,A_: Optional[bool] = None,):






            '''simple docstring'''




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

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

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

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






def 		_A		(		_lowercase = 1_00 )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     __UpperCamelCase =			0
     for i in range(1					,  n + 1 ):
          sum_of_squares += i**2
          sum_of_ints += i
     return sum_of_ints**2 - sum_of_squares


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


