






'''simple docstring'''



from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''				,       __A				,       )
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			RobertaConfig
      UpperCamelCase_								=			'''roberta'''






      def __init__( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]		)  ->  Optional[int]:


       '''simple docstring'''

       super().__init__(UpperCAmelCase		)

       lowercase					:   List[str]				  =RobertaEmbeddings(UpperCAmelCase		)
       self.init_weights()





@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''				,       __A				,       )
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			RobertaConfig
      UpperCamelCase_								=			'''roberta'''






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       super().__init__(UpperCAmelCase		)
       lowercase					:   Optional[int]				  =config.num_labels
       lowercase					:   List[Any]				  =config.num_hidden_layers

       lowercase					:   Any				  =DeeRobertaModel(UpperCAmelCase		)
       lowercase					:   str				  =nn.Dropout(config.hidden_dropout_prob		)
       lowercase					:   int				  =nn.Linear(config.hidden_size     ,      self.config.num_labels		)






      @add_start_docstrings_to_model_forward(UpperCAmelCase		)
      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		List[Any]=None     ,      UpperCAmelCase			:		str=None     ,      UpperCAmelCase			:		int=None     ,      UpperCAmelCase			:		List[str]=None     ,      UpperCAmelCase			:		int=None     ,      UpperCAmelCase			:		Any=None     ,      UpperCAmelCase			:		str=None     ,      UpperCAmelCase			:		Any=-1     ,      UpperCAmelCase			:		Union[str, Any]=False     ,      )  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.num_layers
       try:
        lowercase					:   Tuple				  =self.roberta(
            UpperCAmelCase     ,      attention_mask=UpperCAmelCase     ,      token_type_ids=UpperCAmelCase     ,      position_ids=UpperCAmelCase     ,      head_mask=UpperCAmelCase     ,      inputs_embeds=UpperCAmelCase     ,      )

        lowercase					:   Optional[Any]				  =outputs[1]

        lowercase					:   Any				  =self.dropout(UpperCAmelCase		)
        lowercase					:   int				  =self.classifier(UpperCAmelCase		)
        lowercase					:   int				  =(logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
        lowercase					:   List[str]				  =e.message
        lowercase					:   Optional[Any]				  =e.exit_layer
        lowercase					:   Any				  =outputs[0]

       if not self.training:
        lowercase					:   List[str]				  =entropy(UpperCAmelCase		)
        lowercase					:   int				  =[]
        lowercase					:   Optional[Any]				  =[]
       if labels is not None:
        if self.num_labels == 1:
         #  We are doing regression
         lowercase					:   Dict				  =MSELoss()
         lowercase					:   Any				  =loss_fct(logits.view(-1		)     ,      labels.view(-1		)		)
        else:
         lowercase					:   str				  =CrossEntropyLoss()
         lowercase					:   Any				  =loss_fct(logits.view(-1     ,      self.num_labels		)     ,      labels.view(-1		)		)

        # work with highway exits
        lowercase					:   List[Any]				  =[]
        for highway_exit in outputs[-1]:
         lowercase					:   Any				  =highway_exit[0]
         if not self.training:
          highway_logits_all.append(UpperCAmelCase		)
          highway_entropy.append(highway_exit[2]		)
         if self.num_labels == 1:
          #  We are doing regression
          lowercase					:   Union[str, Any]				  =MSELoss()
          lowercase					:   Optional[Any]				  =loss_fct(highway_logits.view(-1		)     ,      labels.view(-1		)		)
         else:
          lowercase					:   Tuple				  =CrossEntropyLoss()
          lowercase					:   List[Any]				  =loss_fct(highway_logits.view(-1     ,      self.num_labels		)     ,      labels.view(-1		)		)
         highway_losses.append(UpperCAmelCase		)

        if train_highway:
         lowercase					:   int				  =(sum(highway_losses[:-1]		),) + outputs
         # exclude the final highway, of course
        else:
         lowercase					:   Any				  =(loss,) + outputs
       if not self.training:
        lowercase					:   Union[str, Any]				  =outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
         lowercase					:   Tuple				  =(
             (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
         )  # use the highway of the last layer

       return outputs  # (loss), logits, (hidden_states), (attentions), entropy











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



