
'''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			):
    lowerCAmelCase_					     =		RobertaConfig
    lowerCAmelCase_					     =		'roberta'



    def __init__(						self   :							int,__A   :							Optional[Any]  ):
          super().__init__(__A  )

          _lowerCamelCase					: int		    = RobertaEmbeddings(__A  )
          self.init_weights()


@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '    ,						A    ,						)
class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		RobertaConfig
    lowerCAmelCase_					     =		'roberta'



    def __init__(						self   :							Dict,__A   :							int  ):
          super().__init__(__A  )
          _lowerCamelCase					: Tuple		    = config.num_labels
          _lowerCamelCase					: Union[str, Any]		    = config.num_hidden_layers

          _lowerCamelCase					: List[Any]		    = DeeRobertaModel(__A  )
          _lowerCamelCase					: Optional[Any]		    = nn.Dropout(config.hidden_dropout_prob  )
          _lowerCamelCase					: Any		    = nn.Linear(config.hidden_size,self.config.num_labels  )



    @add_start_docstrings_to_model_forward(__A  )
    def 	lowerCamelCase_	(						self   :							Tuple,__A   :							Dict=None,__A   :							List[str]=None,__A   :							Any=None,__A   :							List[str]=None,__A   :							Optional[int]=None,__A   :							Any=None,__A   :							List[str]=None,__A   :							List[str]=-1,__A   :							Dict=False,):
          _lowerCamelCase					: Optional[Any]		    = self.num_layers
          try:
                _lowerCamelCase					: Optional[Any]		    = self.roberta(
                    __A,attention_mask=__A,token_type_ids=__A,position_ids=__A,head_mask=__A,inputs_embeds=__A,)

                _lowerCamelCase					: int		    = outputs[1]

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

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

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

                if train_highway:
                      _lowerCamelCase					: str		    = (sum(highway_losses[:-1]  ),) + outputs
                      # exclude the final highway, of course
                else:
                      _lowerCamelCase					: str		    = (loss,) + outputs
          if not self.training:
                _lowerCamelCase					: List[Any]		    = outputs + ((original_entropy, highway_entropy), exit_layer)
                if output_layer >= 0:
                      _lowerCamelCase					: Optional[Any]		    = (
                          (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'''




import math
import sys






def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	int:
     if number != int(__snake_case     ):
          raise ValueError('the value of input must be a natural number'     )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'     )
     if number == 0:
          return 1
     __A		:					str						=			[-1] * (number + 1)
     __A		:					Dict						=			0
     for i in range(1					,				number + 1     ):
          __A		:					int						=			sys.maxsize
          __A		:					int						=			int(math.sqrt(__snake_case     )     )
          for j in range(1					,				root + 1     ):
               __A		:					str						=			1 + answers[i - (j**2)]
               __A		:					Dict						=			min(__snake_case					,				__snake_case     )
          __A		:					Union[str, Any]						=			answer
     return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()