




'''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.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


UpperCAmelCase_				:     List[str]     			=						logging.getLogger(__name__)





class 			lowerCAmelCase      (       __lowerCAmelCase):


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None     )					->     Tuple:



            '''simple docstring'''




            if current_layer == 0:
                 __snake_case			    =     self.embedding_hidden_mapping_in(__SCREAMING_SNAKE_CASE     )
            else:
                 __snake_case			    =     hidden_states[0]

            __snake_case			    =     int(self.config.num_hidden_layers / self.config.num_hidden_groups     )

            # Index of the hidden group
            __snake_case			    =     int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)     )

            __snake_case			    =     self.albert_layer_groups[group_idx](
                __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]			,     )
            __snake_case			    =     layer_group_output[0]

            return (hidden_states,)





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


       def __init__(      self			,     __SCREAMING_SNAKE_CASE     )					->     Tuple:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     AlbertTransformerWithPabee(__SCREAMING_SNAKE_CASE     )

            self.init_weights()
            __snake_case			    =     0
            __snake_case			    =     0
            __snake_case			    =     0

            __snake_case			    =     0


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     threshold


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     patience


       def 					lowerCAmelCase					(      self     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     0
            __snake_case			    =     0


       def 					lowerCAmelCase					(      self     )					->     Union[str, 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(__SCREAMING_SNAKE_CASE     )




       @add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE     )
       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=False			,     )					->     Dict:



            '''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(__SCREAMING_SNAKE_CASE			,     device=__SCREAMING_SNAKE_CASE     )
            if token_type_ids is None:
                 __snake_case			    =     torch.zeros(__SCREAMING_SNAKE_CASE			,     dtype=torch.long			,     device=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     attention_mask.unsqueeze(1     ).unsqueeze(2     )
            __snake_case			    =     extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
            __snake_case			    =     (1.0 - extended_attention_mask) * -10_000.0
            __snake_case			    =     self.get_head_mask(__SCREAMING_SNAKE_CASE			,     self.config.num_hidden_layers     )

            __snake_case			    =     self.embeddings(
                __SCREAMING_SNAKE_CASE			,     position_ids=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     inputs_embeds=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     embedding_output

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

                      __snake_case			    =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
                      __snake_case			    =     output_layers[i](output_dropout(__SCREAMING_SNAKE_CASE     )     )
                      res.append(__SCREAMING_SNAKE_CASE     )
            elif self.patience == 0:  # Use all layers for inference
                 __snake_case			    =     self.encoder(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     head_mask=__SCREAMING_SNAKE_CASE     )
                 __snake_case			    =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
                 __snake_case			    =     [output_layers[self.config.num_hidden_layers - 1](__SCREAMING_SNAKE_CASE     )]
            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(
                          __SCREAMING_SNAKE_CASE			,     current_layer=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     head_mask=__SCREAMING_SNAKE_CASE			,     )

                      __snake_case			    =     self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]     )     )
                      __snake_case			    =     output_layers[i](__SCREAMING_SNAKE_CASE     )
                      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(__SCREAMING_SNAKE_CASE     )     ):
                                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(
    '''Albert 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. ''' ,      __lowerCAmelCase ,      )
class 			lowerCAmelCase      (       __lowerCAmelCase):


       def __init__(      self			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            super().__init__(__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     config.num_labels

            __snake_case			    =     AlbertModelWithPabee(__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     nn.Dropout(config.classifier_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(__SCREAMING_SNAKE_CASE     )
       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=None			,     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     self.albert(
                input_ids=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     position_ids=__SCREAMING_SNAKE_CASE			,     head_mask=__SCREAMING_SNAKE_CASE			,     inputs_embeds=__SCREAMING_SNAKE_CASE			,     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(__SCREAMING_SNAKE_CASE     ):
                      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'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






