






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      a_ (      _A  )			->			str:







				"""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   __SCREAMING_SNAKE_CASE(	nn.Module	):
							def __init__(     self:      List[Any]       ,	UpperCamelCase:      Tuple						)		->		List[str]:
											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 lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      List[str]						)		->		Dict:
											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 lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Optional[Any]						)		->		Dict:
											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 lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Tuple=None       ,	UpperCamelCase:      Tuple=None       ,	UpperCamelCase:      Union[str, Any]=None       ,	UpperCamelCase:      Optional[int]=None       ,	)		->		Optional[Any]:
											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). "    ,  a_    ,  )
class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      str       ,	UpperCamelCase:      str						)		->		Union[str, Any]:
											super().__init__(UpperCamelCase						)
											snake_case__									=     config

											snake_case__									=     BertEmbeddings(UpperCamelCase						)
											snake_case__									=     DeeBertEncoder(UpperCamelCase						)
											snake_case__									=     BertPooler(UpperCamelCase						)

											self.init_weights()
							def lowerCAmelCase_ (     self:      int						)		->		Union[str, Any]:
											self.encoder.init_highway_pooler(self.pooler						)
							def lowerCAmelCase_ (     self:      Any						)		->		Union[str, Any]:
											return self.embeddings.word_embeddings
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      List[Any]						)		->		str:
											snake_case__									=     value
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      int						)		->		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 lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      int=None       ,	UpperCamelCase:      Any=None       ,	UpperCamelCase:      Tuple=None       ,	UpperCamelCase:      List[str]=None       ,	UpperCamelCase:      Optional[Any]=None       ,	UpperCamelCase:      Union[str, Any]=None       ,	UpperCamelCase:      Optional[int]=None       ,	UpperCamelCase:      Any=None       ,	)		->		Dict:
											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   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Union[str, Any]       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      Any						)		->		Optional[int]:
											snake_case__									=     message
											snake_case__									=     exit_layer  # start from 1!


class   __SCREAMING_SNAKE_CASE(	nn.Module	):
							def __init__(     self:      Optional[Any]       ,	UpperCamelCase:      str						)		->		Tuple:
											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 lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      int						)		->		int:
											# 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. "    ,  a_    ,  )
class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Optional[Any]       ,	UpperCamelCase:      Optional[Any]						)		->		Any:
											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 lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Any=None       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      Dict=None       ,	UpperCamelCase:      str=None       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      List[Any]=None       ,	UpperCamelCase:      Optional[Any]=-1       ,	UpperCamelCase:      List[Any]=False       ,	)		->		Union[str, Any]:
											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)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCamelCase					:		str	  =						{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""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					:		Any	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




