






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      (						__snake_case       ):
     __lowerCAmelCase								=					torch.exp(__snake_case       )
     __lowerCAmelCase								=					torch.sum(__snake_case					, dim=1       )  # sum of exp(x_i)
     __lowerCAmelCase								=					torch.sum(x * exp_x					, dim=1       )  # sum of x_i * exp(x_i)
     return torch.log(__snake_case       ) - B / A



class 							_UpperCamelCase			(nn.Module			):


      def __init__(	self					,						__UpperCamelCase	)->	int:
           super().__init__()
           __lowerCAmelCase								=					config.output_attentions
           __lowerCAmelCase								=					config.output_hidden_states
           __lowerCAmelCase								=					nn.ModuleList([BertLayer(__UpperCamelCase	) for _ in range(config.num_hidden_layers	)]	)
           __lowerCAmelCase								=					nn.ModuleList([BertHighway(__UpperCamelCase	) for _ in range(config.num_hidden_layers	)]	)

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


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Optional[int]:
           if (type(__UpperCamelCase	) is float) or (type(__UpperCamelCase	) is int):
                for i in range(len(self.early_exit_entropy	)	):
                     __lowerCAmelCase								=					x
           else:
                __lowerCAmelCase								=					x


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Optional[int]:
           __lowerCAmelCase								=					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					,						)->	int:
           __lowerCAmelCase								=					()
           __lowerCAmelCase								=					()
           __lowerCAmelCase								=					()
           for i, layer_module in enumerate(self.layer	):
                if self.output_hidden_states:
                     __lowerCAmelCase								=					all_hidden_states + (hidden_states,)

                __lowerCAmelCase								=					layer_module(
                    __UpperCamelCase					,						__UpperCamelCase					,						head_mask[i]					,						__UpperCamelCase					,						__UpperCamelCase	)
                __lowerCAmelCase								=					layer_outputs[0]

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

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

                __lowerCAmelCase								=					self.highway[i](__UpperCamelCase	)
                # logits, pooled_output

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

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

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

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

           __lowerCAmelCase								=					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). """							,		a_							,		)
class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	List[str]:
           super().__init__(__UpperCamelCase	)
           __lowerCAmelCase								=					config

           __lowerCAmelCase								=					BertEmbeddings(__UpperCamelCase	)
           __lowerCAmelCase								=					DeeBertEncoder(__UpperCamelCase	)
           __lowerCAmelCase								=					BertPooler(__UpperCamelCase	)

           self.init_weights()


      def  __UpperCAmelCase		(	self	)->	Any:
           self.encoder.init_highway_pooler(self.pooler	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           return self.embeddings.word_embeddings


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					value


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Union[str, Any]:
           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					,						)->	Any:
           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								=					input_ids.size()
           elif inputs_embeds is not None:
                __lowerCAmelCase								=					inputs_embeds.size()[:-1]
           else:
                raise ValueError("You have to specify either input_ids or inputs_embeds"	)

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

           if attention_mask is None:
                __lowerCAmelCase								=					torch.ones(__UpperCamelCase					,						device=__UpperCamelCase	)
           if encoder_attention_mask is None:
                __lowerCAmelCase								=					torch.ones(__UpperCamelCase					,						device=__UpperCamelCase	)
           if token_type_ids is None:
                __lowerCAmelCase								=					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								=					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								=					encoder_attention_mask[:, None, :, :]
           if encoder_attention_mask.dim() == 2:
                __lowerCAmelCase								=					encoder_attention_mask[:, None, None, :]

           __lowerCAmelCase								=					encoder_extended_attention_mask.to(
               dtype=next(self.parameters()	).dtype	)  # fp16 compatibility
           __lowerCAmelCase								=					(1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

           # 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								=					self.get_head_mask(__UpperCamelCase					,						self.config.num_hidden_layers	)

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

           __lowerCAmelCase								=					(
               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			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase	)->	Dict:
           __lowerCAmelCase								=					message
           __lowerCAmelCase								=					exit_layer  # start from 1!



class 							_UpperCamelCase			(nn.Module			):


      def __init__(	self					,						__UpperCamelCase	)->	Tuple:
           super().__init__()
           __lowerCAmelCase								=					BertPooler(__UpperCamelCase	)
           __lowerCAmelCase								=					nn.Dropout(config.hidden_dropout_prob	)
           __lowerCAmelCase								=					nn.Linear(config.hidden_size					,						config.num_labels	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	Dict:
           # Pooler
           __lowerCAmelCase								=					encoder_outputs[0]
           __lowerCAmelCase								=					self.pooler(__UpperCamelCase	)
           # "return" pooler_output

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

           # Dropout and classification
           __lowerCAmelCase								=					bmodel_output[1]

           __lowerCAmelCase								=					self.dropout(__UpperCamelCase	)
           __lowerCAmelCase								=					self.classifier(__UpperCamelCase	)

           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. """							,		a_							,		)
class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase	)->	Optional[int]:
           super().__init__(__UpperCamelCase	)
           __lowerCAmelCase								=					config.num_labels
           __lowerCAmelCase								=					config.num_hidden_layers

           __lowerCAmelCase								=					DeeBertModel(__UpperCamelCase	)
           __lowerCAmelCase								=					nn.Dropout(config.hidden_dropout_prob	)
           __lowerCAmelCase								=					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					,						)->	Any:
           __lowerCAmelCase								=					self.num_layers
           try:
                __lowerCAmelCase								=					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								=					outputs[1]

                __lowerCAmelCase								=					self.dropout(__UpperCamelCase	)
                __lowerCAmelCase								=					self.classifier(__UpperCamelCase	)
                __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(__UpperCamelCase	)
                __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(__UpperCamelCase	)
                          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(__UpperCamelCase	)

                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), (highway_exits)














lowerCamelCase			:					str         =	'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase			:					Tuple         =	[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase			:					Dict         =	{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






