




"""simple docstring"""






import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCamelCase__						=			logging.getLogger(__name__)




class   a__			(       UpperCamelCase_  ):





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Optional[int]     ,a__      :	Optional[int]     ,a__      :	int=None     ,a__      :	Any=None)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.layer[current_layer](a__     ,a__     ,head_mask[current_layer])

											_lowerCAmelCase:Tuple									=  layer_outputs[0]

											return hidden_states




@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,a__      :	Any)						->       Tuple:




											"""simple docstring"""







											super().__init__(a__)

											_lowerCAmelCase:List[Any]									=  BertEncoderWithPabee(a__)

											self.init_weights()
											_lowerCAmelCase:Dict									=  0
											_lowerCAmelCase:Dict									=  0
											_lowerCAmelCase:Tuple									=  0

											_lowerCAmelCase:Tuple									=  0





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:int									=  threshold





						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	Dict)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  patience





						def       __UpperCamelCase     (							self      :	List[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  0
											_lowerCAmelCase:Dict									=  0





						def       __UpperCamelCase     (							self      :	Optional[int])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.inference_layers_num / self.inference_instances_num
											_lowerCAmelCase:Tuple									=  (
											    F'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
											    F' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
											)
											print(a__)







						@add_start_docstrings_to_model_forward(a__)
						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	List[str]=None     ,a__      :	Union[str, Any]=None     ,a__      :	Tuple=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[int]=None     ,a__      :	List[str]=None     ,a__      :	List[str]=None     ,a__      :	Tuple=None     ,a__      :	str=None     ,a__      :	Optional[Any]=None     ,a__      :	Dict=False     ,)						->       str:




											"""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:
																_lowerCAmelCase:Optional[Any]									=  input_ids.size()
											elif inputs_embeds is not None:
																_lowerCAmelCase:Union[str, Any]									=  inputs_embeds.size()[:-1]
											else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds''')

											_lowerCAmelCase:int									=  input_ids.device if input_ids is not None else inputs_embeds.device

											if attention_mask is None:
																_lowerCAmelCase:Union[str, Any]									=  torch.ones(a__     ,device=a__)
											if token_type_ids is None:
																_lowerCAmelCase:Optional[Any]									=  torch.zeros(a__     ,dtype=torch.long     ,device=a__)

											# 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(a__     ,a__     ,a__)

											# 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 self.config.is_decoder and encoder_hidden_states is not None:
																_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[Any]									=  encoder_hidden_states.size()
																_lowerCAmelCase:Any									=  (encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																					_lowerCAmelCase:Optional[Any]									=  torch.ones(a__     ,device=a__)
																_lowerCAmelCase:int									=  self.invert_attention_mask(a__)
											else:
																_lowerCAmelCase:Dict									=  None

											# 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:Dict									=  self.get_head_mask(a__     ,self.config.num_hidden_layers)

											_lowerCAmelCase:str									=  self.embeddings(
											    input_ids=a__     ,position_ids=a__     ,token_type_ids=a__     ,inputs_embeds=a__)
											_lowerCAmelCase:List[Any]									=  embedding_output

											if self.training:
																_lowerCAmelCase:str									=  []
																for i in range(self.config.num_hidden_layers):
																					_lowerCAmelCase:List[str]									=  self.encoder.adaptive_forward(
																					    a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__)

																					_lowerCAmelCase:Optional[int]									=  self.pooler(a__)
																					_lowerCAmelCase:Optional[int]									=  output_layers[i](output_dropout(a__))
																					res.append(a__)
											elif self.patience == 0:  # Use all layers for inference
																_lowerCAmelCase:Optional[int]									=  self.encoder(
																    a__     ,attention_mask=a__     ,head_mask=a__     ,encoder_hidden_states=a__     ,encoder_attention_mask=a__     ,)
																_lowerCAmelCase:str									=  self.pooler(encoder_outputs[0])
																_lowerCAmelCase:int									=  [output_layers[self.config.num_hidden_layers - 1](a__)]
											else:
																_lowerCAmelCase:int									=  0
																_lowerCAmelCase:Optional[Any]									=  None
																_lowerCAmelCase:Optional[Any]									=  0
																for i in range(self.config.num_hidden_layers):
																					calculated_layer_num += 1
																					_lowerCAmelCase:Optional[int]									=  self.encoder.adaptive_forward(
																					    a__     ,current_layer=a__     ,attention_mask=a__     ,head_mask=a__)

																					_lowerCAmelCase:Tuple									=  self.pooler(a__)
																					_lowerCAmelCase:Dict									=  output_layers[i](a__)
																					if regression:
																										_lowerCAmelCase:List[str]									=  logits.detach()
																										if patient_result is not None:
																															_lowerCAmelCase:Union[str, Any]									=  patient_result.detach()
																										if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
																															patient_counter += 1
																										else:
																															_lowerCAmelCase:Optional[int]									=  0
																					else:
																										_lowerCAmelCase:Optional[int]									=  logits.detach().argmax(dim=1)
																										if patient_result is not None:
																															_lowerCAmelCase:List[str]									=  patient_result.detach().argmax(dim=1)
																										if (patient_result is not None) and torch.all(labels.eq(a__)):
																															patient_counter += 1
																										else:
																															_lowerCAmelCase:Union[str, Any]									=  0

																					_lowerCAmelCase:Optional[Any]									=  logits
																					if patient_counter == self.patience:
																										break
																_lowerCAmelCase:str									=  [patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

											return res




@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''					,	UpperCamelCase_					,	)
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Optional[int]     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											super().__init__(a__)
											_lowerCAmelCase:str									=  config.num_labels

											_lowerCAmelCase:str									=  BertModelWithPabee(a__)
											_lowerCAmelCase:str									=  nn.Dropout(config.hidden_dropout_prob)
											_lowerCAmelCase:Any									=  nn.ModuleList(
											    [nn.Linear(config.hidden_size     ,self.config.num_labels) for _ in range(config.num_hidden_layers)])

											self.init_weights()







						@add_start_docstrings_to_model_forward(a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[str]=None     ,a__      :	Dict=None     ,a__      :	str=None     ,a__      :	List[str]=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[Any]=None     ,a__      :	Optional[int]=None     ,)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.bert(
											    input_ids=a__     ,attention_mask=a__     ,token_type_ids=a__     ,position_ids=a__     ,head_mask=a__     ,inputs_embeds=a__     ,output_dropout=self.dropout     ,output_layers=self.classifiers     ,regression=self.num_labels == 1     ,)

											_lowerCAmelCase:int									=  (logits[-1],)

											if labels is not None:
																_lowerCAmelCase:Any									=  None
																_lowerCAmelCase:int									=  0
																for ix, logits_item in enumerate(a__):
																					if self.num_labels == 1:
																										#  We are doing regression
																										_lowerCAmelCase:Tuple									=  MSELoss()
																										_lowerCAmelCase:List[Any]									=  loss_fct(logits_item.view(-1)     ,labels.view(-1))
																					else:
																										_lowerCAmelCase:Union[str, Any]									=  CrossEntropyLoss()
																										_lowerCAmelCase:Dict									=  loss_fct(logits_item.view(-1     ,self.num_labels)     ,labels.view(-1))
																					if total_loss is None:
																										_lowerCAmelCase:Tuple									=  loss
																					else:
																										total_loss += loss * (ix + 1)
																					total_weights += ix + 1
																_lowerCAmelCase:List[Any]									=  (total_loss / total_weights,) + outputs

											return outputs












"""simple docstring"""






import baseaa





def 							UpperCAmelCase		(		snake_case			:					str    ):
					return baseaa.aaaencode(string.encode('''utf-8'''    )    )





def 							UpperCAmelCase		(		snake_case			:					bytes    ):
					return baseaa.aaadecode(snake_case    ).decode('''utf-8'''    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






