


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase :    Tuple      =   {}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase :    List[Any]      =   ["""BartphoTokenizer"""]

if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bartpho import BartphoTokenizer

else:
   import sys

   UpperCAmelCase :    int      =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



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   (SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      snake_case_	=				torch.exp(SCREAMING_SNAKE_CASE )
      snake_case_	=				torch.sum(SCREAMING_SNAKE_CASE , 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(SCREAMING_SNAKE_CASE ) - B / A







class lowerCAmelCase_						(			nn.Module	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__()
       snake_case_	=				config.output_attentions
       snake_case_	=				config.output_hidden_states
       snake_case_	=				nn.ModuleList([BertLayer(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
       snake_case_	=				nn.ModuleList([BertHighway(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )

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



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       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(
                 _UpperCAmelCase		,				_UpperCAmelCase		,				head_mask[i]		,				_UpperCAmelCase		,				_UpperCAmelCase )
             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](_UpperCAmelCase )
             # logits, pooled_output

             if not self.training:
                   snake_case_	=				highway_exit[0]
                   snake_case_	=				entropy(_UpperCAmelCase )
                   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(_UpperCAmelCase		,				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). " ,						lowerCamelCase__ ,						)
class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__(_UpperCAmelCase )
       snake_case_	=				config

       snake_case_	=				BertEmbeddings(_UpperCAmelCase )
       snake_case_	=				DeeBertEncoder(_UpperCAmelCase )
       snake_case_	=				BertPooler(_UpperCAmelCase )

       self.init_weights()



 def 						UpperCamelCase__					(     self ):
       self.encoder.init_highway_pooler(self.pooler )



 def 						UpperCamelCase__					(     self ):
       return self.embeddings.word_embeddings



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				value



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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		,				):
       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(_UpperCAmelCase		,				device=_UpperCAmelCase )
       if encoder_attention_mask is None:
             snake_case_	=				torch.ones(_UpperCAmelCase		,				device=_UpperCAmelCase )
       if token_type_ids is None:
             snake_case_	=				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.
       snake_case_	=				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:
             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) * -10_000.0

       # 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(_UpperCAmelCase		,				self.config.num_hidden_layers )

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

       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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				message
       snake_case_	=				exit_layer  # start from 1!







class lowerCAmelCase_						(			nn.Module	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__()
       snake_case_	=				BertPooler(_UpperCAmelCase )
       snake_case_	=				nn.Dropout(config.hidden_dropout_prob )
       snake_case_	=				nn.Linear(config.hidden_size		,				config.num_labels )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       # Pooler
       snake_case_	=				encoder_outputs[0]
       snake_case_	=				self.pooler(_UpperCAmelCase )
       # "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(_UpperCAmelCase )
       snake_case_	=				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. " ,						lowerCamelCase__ ,						)
class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__(_UpperCAmelCase )
       snake_case_	=				config.num_labels
       snake_case_	=				config.num_hidden_layers

       snake_case_	=				DeeBertModel(_UpperCAmelCase )
       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(_UpperCAmelCase )
 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=-1		,				_UpperCAmelCase=False		,				):
       snake_case_	=				self.num_layers
       try:
             snake_case_	=				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

             snake_case_	=				outputs[1]

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

             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)