"""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). "    ,    _SCREAMING_SNAKE_CASE    ,    )
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		RobertaConfig
					a   	=		"roberta"



					def __init__(						self			:				Tuple   , _snake_case			:				str		)							->       str:
									super().__init__(_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				RobertaEmbeddings(_snake_case		)
									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. "    ,    _SCREAMING_SNAKE_CASE    ,    )
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		RobertaConfig
					a   	=		"roberta"



					def __init__(						self			:				Union[str, Any]   , _snake_case			:				Optional[int]		)							->       str:
									super().__init__(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				config.num_labels
									SCREAMING_SNAKE_CASE__		  =				config.num_hidden_layers

									SCREAMING_SNAKE_CASE__		  =				DeeRobertaModel(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				nn.Dropout(config.hidden_dropout_prob		)
									SCREAMING_SNAKE_CASE__		  =				nn.Linear(config.hidden_size   , self.config.num_labels		)



					@add_start_docstrings_to_model_forward(_snake_case		)
					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]=None   , _snake_case			:				Optional[Any]=None   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				List[Any]=None   , _snake_case			:				Tuple=None   , _snake_case			:				Any=None   , _snake_case			:				str=None   , _snake_case			:				List[Any]=-1   , _snake_case			:				str=False   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.num_layers
									try:
													SCREAMING_SNAKE_CASE__		  =				self.roberta(
													    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , position_ids=_snake_case   , head_mask=_snake_case   , inputs_embeds=_snake_case   , )

													SCREAMING_SNAKE_CASE__		  =				outputs[1]

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

									if not self.training:
													SCREAMING_SNAKE_CASE__		  =				entropy(_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				[]
													SCREAMING_SNAKE_CASE__		  =				[]
									if labels is not None:
													if self.num_labels == 1:
																	#  We are doing regression
																	SCREAMING_SNAKE_CASE__		  =				MSELoss()
																	SCREAMING_SNAKE_CASE__		  =				loss_fct(logits.view(-1		)   , labels.view(-1		)		)
													else:
																	SCREAMING_SNAKE_CASE__		  =				CrossEntropyLoss()
																	SCREAMING_SNAKE_CASE__		  =				loss_fct(logits.view(-1   , self.num_labels		)   , labels.view(-1		)		)

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

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




