'''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). '    ,			__lowercase    ,			)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      str       =							RobertaConfig
		lowercase   :      Dict       =							'roberta'






		def __init__(   self		,						__A				) ->     Union[str, Any]:
								super().__init__(__A				)

								_lowerCAmelCase										=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. '    ,			__lowercase    ,			)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							RobertaConfig
		lowercase   :      Tuple       =							'roberta'






		def __init__(   self		,						__A				) ->     Dict:
								super().__init__(__A				)
								_lowerCAmelCase										=config.num_labels
								_lowerCAmelCase										=config.num_hidden_layers

								_lowerCAmelCase										=DeeRobertaModel(__A				)
								_lowerCAmelCase										=nn.Dropout(config.hidden_dropout_prob				)
								_lowerCAmelCase										=nn.Linear(config.hidden_size		,						self.config.num_labels				)






		@add_start_docstrings_to_model_forward(__A				)
		def 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=-1		,						__A=False		,						) ->     str:
								_lowerCAmelCase										=self.num_layers
								try:
														_lowerCAmelCase										=self.roberta(
														    __A		,						attention_mask=__A		,						token_type_ids=__A		,						position_ids=__A		,						head_mask=__A		,						inputs_embeds=__A		,						)

														_lowerCAmelCase										=outputs[1]

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

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

														# work with highway exits
														_lowerCAmelCase										=[]
														for highway_exit in outputs[-1]:
																				_lowerCAmelCase										=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										=MSELoss()
																										_lowerCAmelCase										=loss_fct(highway_logits.view(-1				)		,						labels.view(-1				)				)
																				else:
																										_lowerCAmelCase										=CrossEntropyLoss()
																										_lowerCAmelCase										=loss_fct(highway_logits.view(-1		,						self.num_labels				)		,						labels.view(-1				)				)
																				highway_losses.append(__A				)

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

from __future__ import annotations

lowercase_     		= 10



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=1
						_lowerCAmelCase										=max(a__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												_lowerCAmelCase										=[[] for _ in range(a__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		_lowerCAmelCase										=int((i / placement) % RADIX      )
																		buckets[tmp].append(a__      )
												# put each buckets' contents into list_of_ints
												_lowerCAmelCase										=0
												for b in range(a__      ):
																		for i in buckets[b]:
																								_lowerCAmelCase										=i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
							import doctest

							doctest.testmod()





