




def 		lowerCamelCase__       (						_A = 1000				):







	'''simple docstring'''





	snake_case_						       =	2**power
	snake_case_						       =	str(_A				)
	snake_case_						       =	list(_A				)
	snake_case_						       =	0

	for i in list_num:
		sum_of_num += int(_A				)

	return sum_of_num


if __name__ == "__main__":
		lowercase__							:	Optional[int]											=		int(input("Enter the power of 2: ").strip())
		print("2 ^ ", power, " = ", 2**power)
		lowercase__							:	Tuple											=		solution(power)
		print("Sum of the digits is: ", result)






import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	torch.exp(_A				)
	snake_case_						       =	torch.sum(_A	,      dim=1				)  # sum of exp(x_i)
	snake_case_						       =	torch.sum(x * exp_x	,      dim=1				)  # sum of x_i * exp(x_i)
	return torch.log(_A				) - B / A
class 						UpperCAmelCase  (							nn.Module     ):







						'''simple docstring'''





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






							"""simple docstring"""






							super().__init__()
							snake_case_						       =	config.output_attentions
							snake_case_						       =	config.output_hidden_states
							snake_case_						       =	nn.ModuleList([BertLayer(__lowercase		) for _ in range(config.num_hidden_layers		)]		)
							snake_case_						       =	nn.ModuleList([BertHighway(__lowercase		) for _ in range(config.num_hidden_layers		)]		)

							snake_case_						       =	[-1 for _ in range(config.num_hidden_layers		)]






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							if (type(__lowercase		) is float) or (type(__lowercase		) is int):
								for i in range(len(self.early_exit_entropy		)		):
									snake_case_						       =	x
							else:
								snake_case_						       =	x






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	pooler.state_dict()
							for highway in self.highway:
								for name, param in highway.pooler.state_dict().items():
									param.copy_(loaded_model[name]		)






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[str]			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	List[str]=None			,      ):






							"""simple docstring"""






							snake_case_						       =	()
							snake_case_						       =	()
							snake_case_						       =	()
							for i, layer_module in enumerate(self.layer		):
								if self.output_hidden_states:
									snake_case_						       =	all_hidden_states + (hidden_states,)

								snake_case_						       =	layer_module(
								    __lowercase			,      __lowercase			,      head_mask[i]			,      __lowercase			,      __lowercase		)
								snake_case_						       =	layer_outputs[0]

								if self.output_attentions:
									snake_case_						       =	all_attentions + (layer_outputs[1],)

								snake_case_						       =	(hidden_states,)
								if self.output_hidden_states:
									snake_case_						       =	current_outputs + (all_hidden_states,)
								if self.output_attentions:
									snake_case_						       =	current_outputs + (all_attentions,)

								snake_case_						       =	self.highway[i](__lowercase		)
								# logits, pooled_output

								if not self.training:
									snake_case_						       =	highway_exit[0]
									snake_case_						       =	entropy(__lowercase		)
									snake_case_						       =	highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
									snake_case_						       =	all_highway_exits + (highway_exit,)

									if highway_entropy < self.early_exit_entropy[i]:
										snake_case_						       =	(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(__lowercase			,      i + 1		)
								else:
									snake_case_						       =	all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
								snake_case_						       =	all_hidden_states + (hidden_states,)

							snake_case_						       =	(hidden_states,)
							if self.output_hidden_states:
								snake_case_						       =	outputs + (all_hidden_states,)
							if self.output_attentions:
								snake_case_						       =	outputs + (all_attentions,)

							snake_case_						       =	outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits







@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''			,  UpperCAmelCase__			,  )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							super().__init__(__lowercase		)
							snake_case_						       =	config

							snake_case_						       =	BertEmbeddings(__lowercase		)
							snake_case_						       =	DeeBertEncoder(__lowercase		)
							snake_case_						       =	BertPooler(__lowercase		)

							self.init_weights()






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							self.encoder.init_highway_pooler(self.pooler		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return self.embeddings.word_embeddings






						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	Any		):






							"""simple docstring"""






							snake_case_						       =	value






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							for layer, heads in heads_to_prune.items():
								self.encoder.layer[layer].attention.prune_heads(__lowercase		)






						@add_start_docstrings_to_model_forward(__lowercase		)
						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	List[str]=None			,      __lowercase				:	int=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	Dict=None			,      __lowercase				:	str=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	List[str]=None			,      ):






							"""simple docstring"""






							if input_ids is not None and inputs_embeds is not None:
								raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
							elif input_ids is not None:
								snake_case_						       =	input_ids.size()
							elif inputs_embeds is not None:
								snake_case_						       =	inputs_embeds.size()[:-1]
							else:
								raise ValueError("You have to specify either input_ids or inputs_embeds"		)

							snake_case_						       =	input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
								snake_case_						       =	torch.ones(__lowercase			,      device=__lowercase		)
							if encoder_attention_mask is None:
								snake_case_						       =	torch.ones(__lowercase			,      device=__lowercase		)
							if token_type_ids is None:
								snake_case_						       =	torch.zeros(__lowercase			,      dtype=torch.long			,      device=__lowercase		)

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							snake_case_						       =	self.get_extended_attention_mask(__lowercase			,      __lowercase			,      __lowercase		)

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
								snake_case_						       =	encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
								snake_case_						       =	encoder_attention_mask[:, None, None, :]

							snake_case_						       =	encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
							snake_case_						       =	(1.0 - encoder_extended_attention_mask) * -1_0000.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							snake_case_						       =	self.get_head_mask(__lowercase			,      self.config.num_hidden_layers		)

							snake_case_						       =	self.embeddings(
							    input_ids=__lowercase			,      position_ids=__lowercase			,      token_type_ids=__lowercase			,      inputs_embeds=__lowercase		)
							snake_case_						       =	self.encoder(
							    __lowercase			,      attention_mask=__lowercase			,      head_mask=__lowercase			,      encoder_hidden_states=__lowercase			,      encoder_attention_mask=__lowercase			,      )
							snake_case_						       =	encoder_outputs[0]
							snake_case_						       =	self.pooler(__lowercase		)

							snake_case_						       =	(
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits







class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Tuple			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							snake_case_						       =	message
							snake_case_						       =	exit_layer  # start from 1!







class 						UpperCAmelCase  (							nn.Module     ):







						'''simple docstring'''





						def __init__(		self				:	int			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






							super().__init__()
							snake_case_						       =	BertPooler(__lowercase		)
							snake_case_						       =	nn.Dropout(config.hidden_dropout_prob		)
							snake_case_						       =	nn.Linear(config.hidden_size			,      config.num_labels		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	encoder_outputs[0]
							snake_case_						       =	self.pooler(__lowercase		)
							# "return" pooler_output

							# BertModel
							snake_case_						       =	(pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							snake_case_						       =	bmodel_output[1]

							snake_case_						       =	self.dropout(__lowercase		)
							snake_case_						       =	self.classifier(__lowercase		)

							return logits, pooled_output







@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''			,  UpperCAmelCase__			,  )
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							super().__init__(__lowercase		)
							snake_case_						       =	config.num_labels
							snake_case_						       =	config.num_hidden_layers

							snake_case_						       =	DeeBertModel(__lowercase		)
							snake_case_						       =	nn.Dropout(config.hidden_dropout_prob		)
							snake_case_						       =	nn.Linear(config.hidden_size			,      self.config.num_labels		)

							self.init_weights()






						@add_start_docstrings_to_model_forward(__lowercase		)
						def 			snake_case__		(		self				:	Optional[int]			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Dict=None			,      __lowercase				:	Optional[Any]=None			,      __lowercase				:	Optional[int]=None			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Tuple=None			,      __lowercase				:	List[str]=None			,      __lowercase				:	int=-1			,      __lowercase				:	Tuple=False			,      ):






							"""simple docstring"""






							snake_case_						       =	self.num_layers
							try:
								snake_case_						       =	self.bert(
								    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      position_ids=__lowercase			,      head_mask=__lowercase			,      inputs_embeds=__lowercase			,      )
								# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

								snake_case_						       =	outputs[1]

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

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

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

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