

class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__    ,       A__			):
						A__							:    Union[str, Any]        =		name
						A__							:    int        =		value
						A__							:    List[str]        =		weight






					def __repr__( self			):
						return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






					def   __A			( self			):
						return self.value






					def   __A			( self			):
						return self.name






					def   __A			( self			):
						return self.weight






					def   __A			( self			):
						return self.value / self.weight



def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: List[str]    ,      lowercase_: Any					)     ->      Optional[Any]:
	A__							:    str        =		[]
	for i in range(len(SCREAMING_SNAKE_CASE__					)					):
		menu.append(Things(name[i]    ,      value[i]    ,      weight[i]					)					)
	return menu

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: Tuple    ,      lowercase_: str					)     ->      Optional[Any]:
	A__							:    List[Any]        =		sorted(SCREAMING_SNAKE_CASE__    ,      key=SCREAMING_SNAKE_CASE__    ,      reverse=SCREAMING_SNAKE_CASE__					)
	A__							:    Optional[int]        =		[]
	A__							:    List[Any]        =		0.0, 0.0
	for i in range(len(SCREAMING_SNAKE_CASE__					)					):
		if (total_cost + items_copy[i].get_weight()) <= max_cost:
			result.append(items_copy[i]					)
			total_cost += items_copy[i].get_weight()
			total_value += items_copy[i].get_value()
	return (result, total_value)

def 						UpperCamelCase					()     ->      List[str]:
	pass


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     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[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							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						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:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						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:
           __snake_case:						Optional[int]   =						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							,				)






