


"""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,
)


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.getLogger(__name__)





class      __A     (_A):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Tuple=None		,       UpperCAmelCase_							:      str=None       )					->int:



														"""simple docstring"""



														snake_case_           =		self.layer[current_layer](UpperCamelCase__		,       UpperCamelCase__		,       head_mask[current_layer]       )

														snake_case_           =		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."""						,		_A						,		)
class      __A     (_A):





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Dict       )					->Dict:



														"""simple docstring"""



														super().__init__(UpperCamelCase__       )

														snake_case_           =		BertEncoderWithPabee(UpperCamelCase__       )

														self.init_weights()
														snake_case_           =		0
														snake_case_           =		0
														snake_case_           =		0

														snake_case_           =		0



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      List[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		threshold



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Dict       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		patience



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		0
														snake_case_           =		0



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.inference_layers_num / self.inference_instances_num
														snake_case_           =		(
														    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(UpperCamelCase__       )



							@add_start_docstrings_to_model_forward(UpperCamelCase__       )
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      Tuple=None		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Union[str, Any]=None		,       UpperCAmelCase_							:      Tuple=None		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      Any=None		,       UpperCAmelCase_							:      Optional[Any]=None		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      Tuple=False		,       )					->List[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:
																					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 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 self.config.is_decoder and encoder_hidden_states is not None:
																					snake_case_       ,						snake_case_       ,						snake_case_           =		encoder_hidden_states.size()
																					snake_case_           =		(encoder_batch_size, encoder_sequence_length)
																					if encoder_attention_mask is None:
																												snake_case_           =		torch.ones(UpperCamelCase__		,       device=UpperCamelCase__       )
																					snake_case_           =		self.invert_attention_mask(UpperCamelCase__       )
														else:
																					snake_case_           =		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]
														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_           =		embedding_output

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

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

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

																												snake_case_           =		logits
																												if patient_counter == self.patience:
																																			break
																					snake_case_           =		[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. """						,		_A						,		)
class      __A     (_A):





							'''simple docstring'''

							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      List[str]       )					->Dict:



														"""simple docstring"""



														super().__init__(UpperCamelCase__       )
														snake_case_           =		config.num_labels

														snake_case_           =		BertModelWithPabee(UpperCamelCase__       )
														snake_case_           =		nn.Dropout(config.hidden_dropout_prob       )
														snake_case_           =		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(UpperCamelCase__       )
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      int=None		,       UpperCAmelCase_							:      str=None		,       UpperCAmelCase_							:      str=None		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Tuple=None		,       UpperCAmelCase_							:      Dict=None		,       UpperCAmelCase_							:      Any=None		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.bert(
														    input_ids=UpperCamelCase__		,       attention_mask=UpperCamelCase__		,       token_type_ids=UpperCamelCase__		,       position_ids=UpperCamelCase__		,       head_mask=UpperCamelCase__		,       inputs_embeds=UpperCamelCase__		,       output_dropout=self.dropout		,       output_layers=self.classifiers		,       regression=self.num_labels == 1		,       )

														snake_case_           =		(logits[-1],)

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

														return outputs







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if index == number_of_items:
														return 0
							snake_case_           =		0
							snake_case_           =		0
							snake_case_           =		knapsack(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, index + 1				)
							if weights[index] <= max_weight:
														snake_case_           =		values[index] + knapsack(
														    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, max_weight - weights[index]				, index + 1				)
							return max(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



