


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). '		,       _snake_case		,       )
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			RobertaConfig
 SCREAMING_SNAKE_CASE__ =			'roberta'




 def __init__(						self   ,    _lowerCamelCase       ):
       super().__init__(_lowerCamelCase       )

       a		:Any									=			RobertaEmbeddings(_lowerCamelCase       )
       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. '		,       _snake_case		,       )
class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			RobertaConfig
 SCREAMING_SNAKE_CASE__ =			'roberta'




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

       a		:int									=			DeeRobertaModel(_lowerCamelCase       )
       a		:Optional[int]									=			nn.Dropout(config.hidden_dropout_prob       )
       a		:Optional[int]									=			nn.Linear(config.hidden_size   ,    self.config.num_labels       )




 @add_start_docstrings_to_model_forward(_lowerCamelCase       )
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=-1   ,    _lowerCamelCase=False   ,    ):
       a		:Dict									=			self.num_layers
       try:
             a		:int									=			self.roberta(
                 _lowerCamelCase   ,    attention_mask=_lowerCamelCase   ,    token_type_ids=_lowerCamelCase   ,    position_ids=_lowerCamelCase   ,    head_mask=_lowerCamelCase   ,    inputs_embeds=_lowerCamelCase   ,    )

             a		:Tuple									=			outputs[1]

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

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

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

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

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









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      while b:
            a				,							a		:int									=			b, a % b
      return a
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_					,    a % b		)
def 							__lowerCamelCase		(					):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5		)}'''		)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3		)}'''		)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3		)}'''		)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6		)}'''		)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3		)}'''		)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5		)}'''		)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3		)}'''		)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3		)}'''		)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6		)}'''		)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3		)}'''		)


if __name__ == "__main__":
     main()





