


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). '''		,					__lowerCamelCase		,					)
class 				SCREAMING_SNAKE_CASE__						(       __lowerCamelCase    ):
      snake_case__       :  Tuple       =		RobertaConfig
      snake_case__       :  Union[str, Any]       =		'''roberta'''
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    List[str]:
             super().__init__(SCREAMING_SNAKE_CASE_  )

             a_      :	str							=						RobertaEmbeddings(SCREAMING_SNAKE_CASE_  )
             self.init_weights()
@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,					__lowerCamelCase		,					)
class 				SCREAMING_SNAKE_CASE__						(       __lowerCamelCase    ):
      snake_case__       :  Any       =		RobertaConfig
      snake_case__       :  int       =		'''roberta'''
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Any:
             super().__init__(SCREAMING_SNAKE_CASE_  )
             a_      :	Optional[Any]							=						config.num_labels
             a_      :	Union[str, Any]							=						config.num_hidden_layers

             a_      :	Union[str, Any]							=						DeeRobertaModel(SCREAMING_SNAKE_CASE_  )
             a_      :	Any							=						nn.Dropout(config.hidden_dropout_prob  )
             a_      :	List[Any]							=						nn.Linear(config.hidden_size  ,							self.config.num_labels  )

      @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : int=None  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=None  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  ,							SCREAMING_SNAKE_CASE__       : Dict=None  ,							SCREAMING_SNAKE_CASE__       : Any=-1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=False  ,							)	->    List[Any]:
             a_      :	Dict							=						self.num_layers
             try:
                    a_      :	Any							=						self.roberta(
                        SCREAMING_SNAKE_CASE_  ,							attention_mask=SCREAMING_SNAKE_CASE_  ,							token_type_ids=SCREAMING_SNAKE_CASE_  ,							position_ids=SCREAMING_SNAKE_CASE_  ,							head_mask=SCREAMING_SNAKE_CASE_  ,							inputs_embeds=SCREAMING_SNAKE_CASE_  ,							)

                    a_      :	List[str]							=						outputs[1]

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

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

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

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

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






import random
class 				SCREAMING_SNAKE_CASE__						:
      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : str  )	->    tuple[list[int], list[int]]:
             a_      :	int							=						[ord(SCREAMING_SNAKE_CASE__  ) for i in text]
             a_      :	Any							=						[]
             a_      :	Optional[int]							=						[]
             for i in plain:
                    a_      :	Tuple							=						random.randint(1  ,							3_0_0  )
                    a_      :	Optional[int]							=						(i + k) * k
                    cipher.append(SCREAMING_SNAKE_CASE__  )
                    key.append(SCREAMING_SNAKE_CASE__  )
             return cipher, key

      @staticmethod
      def  SCREAMING_SNAKE_CASE							( SCREAMING_SNAKE_CASE__       : list[int]  ,							SCREAMING_SNAKE_CASE__       : list[int]  )	->    str:
             a_      :	List[Any]							=						[]
             for i in range(len(SCREAMING_SNAKE_CASE__  )  ):
                    a_      :	str							=						int((cipher[i] - (key[i]) ** 2) / key[i]  )
                    plain.append(chr(SCREAMING_SNAKE_CASE__  )  )
             return "".join(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   UpperCAmelCase_      ,     UpperCAmelCase_   :				Any						      = Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))


