





'''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,
)


_SCREAMING_SNAKE_CASE			=							logging.getLogger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None						)      ->							Union[str, Any]:
     if current_layer == 0:
         _lowerCAmelCase		     = self.embedding_hidden_mapping_in(_lowerCAmelCase						)
     else:
         _lowerCAmelCase		     = hidden_states[0]

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

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

     _lowerCAmelCase		     = self.albert_layer_groups[group_idx](
         _lowerCAmelCase     ,       _lowerCAmelCase     ,       head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,       )
     _lowerCAmelCase		     = 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."  ,__magic_name__  ,)
class 				lowerCAmelCase_						(						__magic_name__						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     super().__init__(_lowerCAmelCase						)

     _lowerCAmelCase		     = AlbertTransformerWithPabee(_lowerCAmelCase						)

     self.init_weights()
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = 0

     _lowerCAmelCase		     = 0




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[int]:
     _lowerCAmelCase		     = threshold




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							str:
     _lowerCAmelCase		     = patience




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = 0
     _lowerCAmelCase		     = 0




 def 							_snake_case				(		self						)      ->							int:
     _lowerCAmelCase		     = self.inference_layers_num / self.inference_instances_num
     _lowerCAmelCase		     = (
         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 							_snake_case				(		self     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=False     ,       )      ->							Union[str, 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:
         _lowerCAmelCase		     = input_ids.size()
     elif inputs_embeds is not None:
         _lowerCAmelCase		     = inputs_embeds.size()[:-1]
     else:
         raise ValueError("You have to specify either input_ids or inputs_embeds"						)

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

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

     _lowerCAmelCase		     = attention_mask.unsqueeze(1						).unsqueeze(2						)
     _lowerCAmelCase		     = extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
     _lowerCAmelCase		     = (1.0 - extended_attention_mask) * -10000.0
     _lowerCAmelCase		     = self.get_head_mask(_lowerCAmelCase     ,       self.config.num_hidden_layers						)

     _lowerCAmelCase		     = self.embeddings(
         _lowerCAmelCase     ,       position_ids=_lowerCAmelCase     ,       token_type_ids=_lowerCAmelCase     ,       inputs_embeds=_lowerCAmelCase						)
     _lowerCAmelCase		     = embedding_output

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

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

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

             _lowerCAmelCase		     = logits
             if patient_counter == self.patience:
                 break
         _lowerCAmelCase		     = [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\n    the pooled output) e.g. for GLUE tasks. "  ,__magic_name__  ,)
class 				lowerCAmelCase_						(						__magic_name__						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							Dict:
     super().__init__(_lowerCAmelCase						)
     _lowerCAmelCase		     = config.num_labels

     _lowerCAmelCase		     = AlbertModelWithPabee(_lowerCAmelCase						)
     _lowerCAmelCase		     = nn.Dropout(config.classifier_dropout_prob						)
     _lowerCAmelCase		     = 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 							_snake_case				(		self     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=None     ,       )      ->							Union[str, Any]:
     _lowerCAmelCase		     = self.albert(
         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     ,       )

     _lowerCAmelCase		     = (logits[-1],)

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

     return outputs









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   int = 1000      ):



    '''simple docstring'''





    return sum(i for i in range(SCREAMING_SNAKE_CASE_      ) if i % 3 == 0 or i % 5 == 0      )


if __name__ == "__main__":
       print(f'''{solution() = }''')


