




"""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). "							,    __magic_name__							,    )
class      a__				(	__magic_name__   ):
			lowercase_    =	RobertaConfig
			lowercase_    =	"roberta"


			def __init__(      self      :    Optional[Any]      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						super().__init__(UpperCamelCase_)

						__UpperCAmelCase	: str		=					RobertaEmbeddings(UpperCamelCase_)
						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. "							,    __magic_name__							,    )
class      a__				(	__magic_name__   ):
			lowercase_    =	RobertaConfig
			lowercase_    =	"roberta"


			def __init__(      self      :    Any      ,  UpperCamelCase_      :    int):


						"""simple docstring"""




						super().__init__(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					config.num_labels
						__UpperCAmelCase	: Union[str, Any]		=					config.num_hidden_layers

						__UpperCAmelCase	: str		=					DeeRobertaModel(UpperCamelCase_)
						__UpperCAmelCase	: int		=					nn.Dropout(config.hidden_dropout_prob)
						__UpperCAmelCase	: Optional[Any]		=					nn.Linear(config.hidden_size      ,  self.config.num_labels)


			@add_start_docstrings_to_model_forward(UpperCamelCase_)
			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    Optional[Any]=None      ,  UpperCamelCase_      :    Dict=None      ,  UpperCamelCase_      :    List[Any]=None      ,  UpperCamelCase_      :    List[Any]=None      ,  UpperCamelCase_      :    List[str]=None      ,  UpperCamelCase_      :    List[str]=None      ,  UpperCamelCase_      :    Dict=None      ,  UpperCamelCase_      :    Union[str, Any]=-1      ,  UpperCamelCase_      :    Union[str, Any]=False      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					self.num_layers
						try:
									__UpperCAmelCase	: Optional[int]		=					self.roberta(
									    UpperCamelCase_      ,  attention_mask=UpperCamelCase_      ,  token_type_ids=UpperCamelCase_      ,  position_ids=UpperCamelCase_      ,  head_mask=UpperCamelCase_      ,  inputs_embeds=UpperCamelCase_      ,  )

									__UpperCAmelCase	: List[str]		=					outputs[1]

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

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

									# work with highway exits
									__UpperCAmelCase	: str		=					[]
									for highway_exit in outputs[-1]:
												__UpperCAmelCase	: List[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
															__UpperCAmelCase	: List[Any]		=					MSELoss()
															__UpperCAmelCase	: List[Any]		=					loss_fct(highway_logits.view(-1)      ,  labels.view(-1))
												else:
															__UpperCAmelCase	: Optional[int]		=					CrossEntropyLoss()
															__UpperCAmelCase	: Optional[int]		=					loss_fct(highway_logits.view(-1      ,  self.num_labels)      ,  labels.view(-1))
												highway_losses.append(UpperCamelCase_)

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





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__UpperCAmelCase	: Union[str, Any]		=					_modexpt(UpperCamelCase				,       exponent // 2				,       UpperCamelCase							) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase				,       exponent - 1				,       UpperCamelCase							)) % modulo_value

def 					_UpperCamelCase    (			UpperCamelCase = 1777				,       UpperCamelCase = 1855				,       UpperCamelCase = 8							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					base
			for _ in range(1				,       UpperCamelCase							):
						__UpperCAmelCase	: str		=					_modexpt(UpperCamelCase				,       UpperCamelCase				,       10**digits							)

			return result


if __name__ == "__main__":
					print(f'''{solution() = }''')


