
"""simple docstring"""

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 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   torch.exp(lowercase__      )
		lowerCamelCase					:      Union[str, Any]          =   torch.sum(lowercase__,						dim=1      )  # sum of exp(x_i)
		lowerCamelCase					:      Any          =   torch.sum(x * exp_x,						dim=1      )  # sum of x_i * exp(x_i)
		return torch.log(lowercase__      ) - B / A






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_ )		->							Any:
			super().__init__()
			lowerCamelCase					:      Union[str, Any]          =   config.output_attentions
			lowerCamelCase					:      Dict          =   config.output_hidden_states
			lowerCamelCase					:      Tuple          =   nn.ModuleList([BertLayer(UpperCAmelCase_ ) for _ in range(config.num_hidden_layers )] )
			lowerCamelCase					:      List[Any]          =   nn.ModuleList([BertHighway(UpperCAmelCase_ ) for _ in range(config.num_hidden_layers )] )

			lowerCamelCase					:      Any          =   [-1 for _ in range(config.num_hidden_layers )]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			if (type(UpperCAmelCase_ ) is float) or (type(UpperCAmelCase_ ) is int):
					for i in range(len(self.early_exit_entropy ) ):
							lowerCamelCase					:      Tuple          =   x
			else:
					lowerCamelCase					:      List[str]          =   x




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      List[Any]          =   pooler.state_dict()
			for highway in self.highway:
					for name, param in highway.pooler.state_dict().items():
							param.copy_(loaded_model[name] )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			)		->							str:
			lowerCamelCase					:      Optional[Any]          =   ()
			lowerCamelCase					:      Dict          =   ()
			lowerCamelCase					:      List[Any]          =   ()
			for i, layer_module in enumerate(self.layer ):
					if self.output_hidden_states:
							lowerCamelCase					:      str          =   all_hidden_states + (hidden_states,)

					lowerCamelCase					:      Optional[int]          =   layer_module(
					    UpperCAmelCase_		,			UpperCAmelCase_		,			head_mask[i]		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					lowerCamelCase					:      Optional[Any]          =   layer_outputs[0]

					if self.output_attentions:
							lowerCamelCase					:      List[Any]          =   all_attentions + (layer_outputs[1],)

					lowerCamelCase					:      Optional[int]          =   (hidden_states,)
					if self.output_hidden_states:
							lowerCamelCase					:      Any          =   current_outputs + (all_hidden_states,)
					if self.output_attentions:
							lowerCamelCase					:      int          =   current_outputs + (all_attentions,)

					lowerCamelCase					:      Any          =   self.highway[i](UpperCAmelCase_ )
					# logits, pooled_output

					if not self.training:
							lowerCamelCase					:      str          =   highway_exit[0]
							lowerCamelCase					:      Tuple          =   entropy(UpperCAmelCase_ )
							lowerCamelCase					:      List[Any]          =   highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
							lowerCamelCase					:      Union[str, Any]          =   all_highway_exits + (highway_exit,)

							if highway_entropy < self.early_exit_entropy[i]:
									lowerCamelCase					:      List[str]          =   (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
									raise HighwayException(UpperCAmelCase_		,			i + 1 )
					else:
							lowerCamelCase					:      str          =   all_highway_exits + (highway_exit,)

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

			lowerCamelCase					:      List[Any]          =   (hidden_states,)
			if self.output_hidden_states:
					lowerCamelCase					:      str          =   outputs + (all_hidden_states,)
			if self.output_attentions:
					lowerCamelCase					:      List[str]          =   outputs + (all_attentions,)

			lowerCamelCase					:      Optional[Any]          =   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 	_lowercase       (    __UpperCamelCase		):




	def __init__(			self		,			UpperCAmelCase_ )		->							str:
			super().__init__(UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   config

			lowerCamelCase					:      Dict          =   BertEmbeddings(UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   DeeBertEncoder(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   BertPooler(UpperCAmelCase_ )

			self.init_weights()




	def       _UpperCamelCase   (			self )		->							str:
			self.encoder.init_highway_pooler(self.pooler )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			return self.embeddings.word_embeddings




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      Optional[Any]          =   value




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Dict:
			for layer, heads in heads_to_prune.items():
					self.encoder.layer[layer].attention.prune_heads(UpperCAmelCase_ )




	@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			)		->							Optional[int]:
			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:
					lowerCamelCase					:      Union[str, Any]          =   input_ids.size()
			elif inputs_embeds is not None:
					lowerCamelCase					:      Any          =   inputs_embeds.size()[:-1]
			else:
					raise ValueError('You have to specify either input_ids or inputs_embeds' )

			lowerCamelCase					:      List[str]          =   input_ids.device if input_ids is not None else inputs_embeds.device

			if attention_mask is None:
					lowerCamelCase					:      List[Any]          =   torch.ones(UpperCAmelCase_		,			device=UpperCAmelCase_ )
			if encoder_attention_mask is None:
					lowerCamelCase					:      Any          =   torch.ones(UpperCAmelCase_		,			device=UpperCAmelCase_ )
			if token_type_ids is None:
					lowerCamelCase					:      List[str]          =   torch.zeros(UpperCAmelCase_		,			dtype=torch.long		,			device=UpperCAmelCase_ )

			# 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.
			lowerCamelCase					:      torch.Tensor          =   self.get_extended_attention_mask(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )

			# 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:
					lowerCamelCase					:      Optional[int]          =   encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.dim() == 2:
					lowerCamelCase					:      Tuple          =   encoder_attention_mask[:, None, None, :]

			lowerCamelCase					:      List[Any]          =   encoder_extended_attention_mask.to(
			    dtype=next(self.parameters() ).dtype )  # fp16 compatibility
			lowerCamelCase					:      str          =   (1.0 - encoder_extended_attention_mask) * -10000.0

			# 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]
			lowerCamelCase					:      Optional[Any]          =   self.get_head_mask(UpperCAmelCase_		,			self.config.num_hidden_layers )

			lowerCamelCase					:      List[str]          =   self.embeddings(
			    input_ids=UpperCAmelCase_		,			position_ids=UpperCAmelCase_		,			token_type_ids=UpperCAmelCase_		,			inputs_embeds=UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder(
			    UpperCAmelCase_		,			attention_mask=UpperCAmelCase_		,			head_mask=UpperCAmelCase_		,			encoder_hidden_states=UpperCAmelCase_		,			encoder_attention_mask=UpperCAmelCase_		,			)
			lowerCamelCase					:      Optional[int]          =   encoder_outputs[0]
			lowerCamelCase					:      Any          =   self.pooler(UpperCAmelCase_ )

			lowerCamelCase					:      List[Any]          =   (
			    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 	_lowercase       (    __UpperCamelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			lowerCamelCase					:      str          =   message
			lowerCamelCase					:      int          =   exit_layer  # start from 1!






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_ )		->							int:
			super().__init__()
			lowerCamelCase					:      Optional[int]          =   BertPooler(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   nn.Dropout(config.hidden_dropout_prob )
			lowerCamelCase					:      Optional[int]          =   nn.Linear(config.hidden_size		,			config.num_labels )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
			# Pooler
			lowerCamelCase					:      List[str]          =   encoder_outputs[0]
			lowerCamelCase					:      Dict          =   self.pooler(UpperCAmelCase_ )
			# "return" pooler_output

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

			# Dropout and classification
			lowerCamelCase					:      Tuple          =   bmodel_output[1]

			lowerCamelCase					:      List[str]          =   self.dropout(UpperCAmelCase_ )
			lowerCamelCase					:      List[Any]          =   self.classifier(UpperCAmelCase_ )

			return logits, pooled_output






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




	def __init__(			self		,			UpperCAmelCase_ )		->							int:
			super().__init__(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   config.num_labels
			lowerCamelCase					:      int          =   config.num_hidden_layers

			lowerCamelCase					:      Union[str, Any]          =   DeeBertModel(UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   nn.Dropout(config.hidden_dropout_prob )
			lowerCamelCase					:      str          =   nn.Linear(config.hidden_size		,			self.config.num_labels )

			self.init_weights()




	@add_start_docstrings_to_model_forward(UpperCAmelCase_ )
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=-1		,			UpperCAmelCase_=False		,			)		->							Optional[int]:
			lowerCamelCase					:      List[str]          =   self.num_layers
			try:
					lowerCamelCase					:      Any          =   self.bert(
					    UpperCAmelCase_		,			attention_mask=UpperCAmelCase_		,			token_type_ids=UpperCAmelCase_		,			position_ids=UpperCAmelCase_		,			head_mask=UpperCAmelCase_		,			inputs_embeds=UpperCAmelCase_		,			)
					# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

					lowerCamelCase					:      Optional[Any]          =   outputs[1]

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

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

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

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








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 1000      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   2**power
		lowerCamelCase					:      List[str]          =   str(a_      )
		lowerCamelCase					:      Dict          =   list(a_      )
		lowerCamelCase					:      Optional[Any]          =   0

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

		return sum_of_num


if __name__ == "__main__":
					_A						=			int(input('Enter the power of 2: ').strip())
					print('2 ^ ', power, ' = ', 2**power)
					_A						=			solution(power)
					print('Sum of the digits is: ', result)






