




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,
)


__lowerCAmelCase		:				List[Any]       			=		logging.getLogger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     Optional[int]=None							)					->     List[Any]:
							a    =			self.layer[current_layer](__lowerCamelCase					,      __lowerCamelCase					,      head_mask[current_layer]							)

							a    =			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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     str					,      __lowerCamelCase				:     Tuple							)					->     str:
							super().__init__(__lowerCamelCase							)

							a    =			BertEncoderWithPabee(__lowerCamelCase							)

							self.init_weights()
							a    =			0
							a    =			0
							a    =			0

							a    =			0





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     List[str]							)					->     List[str]:
							a    =			threshold





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			patience





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Union[str, Any]:
							a    =			0
							a    =			0





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     List[str]:
							a    =			self.inference_layers_num / self.inference_instances_num
							a    =			(
							    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(__lowerCamelCase							)





				@add_start_docstrings_to_model_forward(__lowerCamelCase							)
				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     str=None					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Tuple=None					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     List[str]=None					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     Any=False					,      )					->     Optional[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:
										a    =			input_ids.size()
							elif inputs_embeds is not None:
										a    =			inputs_embeds.size()[:-1]
							else:
										raise ValueError("You have to specify either input_ids or inputs_embeds"							)

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

							if attention_mask is None:
										a    =			torch.ones(__lowerCamelCase					,      device=__lowerCamelCase							)
							if token_type_ids is None:
										a    =			torch.zeros(__lowerCamelCase					,      dtype=torch.long					,      device=__lowerCamelCase							)

							# 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.
							a    =			self.get_extended_attention_mask(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)

							# 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:
										a    ,		a    ,		a    =			encoder_hidden_states.size()
										a    =			(encoder_batch_size, encoder_sequence_length)
										if encoder_attention_mask is None:
													a    =			torch.ones(__lowerCamelCase					,      device=__lowerCamelCase							)
										a    =			self.invert_attention_mask(__lowerCamelCase							)
							else:
										a    =			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]
							a    =			self.get_head_mask(__lowerCamelCase					,      self.config.num_hidden_layers							)

							a    =			self.embeddings(
							    input_ids=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      inputs_embeds=__lowerCamelCase							)
							a    =			embedding_output

							if self.training:
										a    =			[]
										for i in range(self.config.num_hidden_layers							):
													a    =			self.encoder.adaptive_forward(
													    __lowerCamelCase					,      current_layer=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      head_mask=__lowerCamelCase							)

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

													a    =			self.pooler(__lowerCamelCase							)
													a    =			output_layers[i](__lowerCamelCase							)
													if regression:
																a    =			logits.detach()
																if patient_result is not None:
																			a    =			patient_result.detach()
																if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
																			patient_counter += 1
																else:
																			a    =			0
													else:
																a    =			logits.detach().argmax(dim=1							)
																if patient_result is not None:
																			a    =			patient_result.detach().argmax(dim=1							)
																if (patient_result is not None) and torch.all(labels.eq(__lowerCamelCase							)							):
																			patient_counter += 1
																else:
																			a    =			0

													a    =			logits
													if patient_counter == self.patience:
																break
										a    =			[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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     str					,      __lowerCamelCase				:     Any							)					->     Union[str, Any]:
							super().__init__(__lowerCamelCase							)
							a    =			config.num_labels

							a    =			BertModelWithPabee(__lowerCamelCase							)
							a    =			nn.Dropout(config.hidden_dropout_prob							)
							a    =			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(__lowerCamelCase							)
				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]=None					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     str=None					,      __lowerCamelCase				:     Dict=None					,      __lowerCamelCase				:     Any=None					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Any=None					,      )					->     Optional[int]:
							a    =			self.bert(
							    input_ids=__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      head_mask=__lowerCamelCase					,      inputs_embeds=__lowerCamelCase					,      output_dropout=self.dropout					,      output_layers=self.classifiers					,      regression=self.num_labels == 1					,      )

							a    =			(logits[-1],)

							if labels is not None:
										a    =			None
										a    =			0
										for ix, logits_item in enumerate(__lowerCamelCase							):
													if self.num_labels == 1:
																#  We are doing regression
																a    =			MSELoss()
																a    =			loss_fct(logits_item.view(-1							)					,      labels.view(-1							)							)
													else:
																a    =			CrossEntropyLoss()
																a    =			loss_fct(logits_item.view(-1					,      self.num_labels							)					,      labels.view(-1							)							)
													if total_loss is None:
																a    =			loss
													else:
																total_loss += loss * (ix + 1)
													total_weights += ix + 1
										a    =			(total_loss / total_weights,) + outputs

							return outputs












def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:List[str]							=  ""
  for word_or_phrase in separated:
    if not isinstance(lowerCamelCase,	lowerCamelCase      ):
      raise Exception("join() accepts only strings to be joined"      )
    joined += word_or_phrase + separator
  return joined.strip(lowerCamelCase      )


if __name__ == "__main__":
     from doctest import testmod

     testmod()



