
"""simple docstring"""




from bisect import bisect
from itertools import accumulate


def   __magic_name__	(      __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]     , __snake_case				:       List[str]     , __snake_case				:       Tuple  ) ->				Union[str, Any]:
       lowercase    :				Optional[Any]       		=							sorted(zip(__A     , __A  )     , key=lambda __snake_case  : x[0] / x[1]     , reverse=__A  )
       lowercase		,    lowercase    :				Union[str, Any]       		=							[i[0] for i in r], [i[1] for i in r]
       lowercase    :				Tuple       		=							list(accumulate(__A  )  )
       lowercase    :				List[str]       		=							bisect(__A     , __A  )
       return (
           0
           if k == 0
           else sum(vl[:k]  ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]  )
       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         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 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



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

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

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  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=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



