


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							:							Optional[int]    							=					logging.getLogger(__name__)

class    __magic_name__						(  __lowerCAmelCase):


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





      '''simple docstring'''






      if current_layer == 0:
       UpperCamelCase__       :     Any 							= self.embedding_hidden_mapping_in(lowerCamelCase__      )
      else:
       UpperCamelCase__       :     Optional[int] 							= hidden_states[0]

      UpperCamelCase__       :     Optional[Any] 							= int(self.config.num_hidden_layers / self.config.num_hidden_groups      )

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

      UpperCamelCase__       :     Optional[int] 							= self.albert_layer_groups[group_idx](
          lowerCamelCase__				,						lowerCamelCase__				,						head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]				,						)
      UpperCamelCase__       :     List[str] 							= 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    __magic_name__						(  __lowerCAmelCase):


     def __init__(   self				:  List[Any]				,						lowerCamelCase__				:  Tuple      )	->   int:





      '''simple docstring'''






      super().__init__(lowerCamelCase__      )

      UpperCamelCase__       :     Any 							= AlbertTransformerWithPabee(lowerCamelCase__      )

      self.init_weights()
      UpperCamelCase__       :     int 							= 0
      UpperCamelCase__       :     Optional[int] 							= 0
      UpperCamelCase__       :     List[str] 							= 0

      UpperCamelCase__       :     Optional[Any] 							= 0


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[Any]      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= threshold


     def   UpperCAmelCase__						(   self				:  Tuple				,						lowerCamelCase__				:  str      )	->   Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= patience


     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= 0
      UpperCamelCase__       :     Any 							= 0


     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= self.inference_layers_num / self.inference_instances_num
      UpperCamelCase__       :     Dict 							= (
          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				:  List[str]				,						lowerCamelCase__				:  List[str]=None				,						lowerCamelCase__				:  List[str]=None				,						lowerCamelCase__				:  List[Any]=None				,						lowerCamelCase__				:  Union[str, Any]=None				,						lowerCamelCase__				:  Dict=None				,						lowerCamelCase__				:  str=None				,						lowerCamelCase__				:  int=None				,						lowerCamelCase__				:  str=None				,						lowerCamelCase__				:  Optional[Any]=False				,						)	->   int:





      '''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:
       UpperCamelCase__       :     Tuple 							= input_ids.size()
      elif inputs_embeds is not None:
       UpperCamelCase__       :     Any 							= inputs_embeds.size()[:-1]
      else:
       raise ValueError('''You have to specify either input_ids or inputs_embeds'''      )

      UpperCamelCase__       :     Optional[Any] 							= input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
       UpperCamelCase__       :     Optional[int] 							= torch.ones(lowerCamelCase__				,						device=lowerCamelCase__      )
      if token_type_ids is None:
       UpperCamelCase__       :     int 							= torch.zeros(lowerCamelCase__				,						dtype=torch.long				,						device=lowerCamelCase__      )

      UpperCamelCase__       :     List[str] 							= attention_mask.unsqueeze(1      ).unsqueeze(2      )
      UpperCamelCase__       :     Any 							= extended_attention_mask.to(dtype=self.dtype      )  # fp16 compatibility
      UpperCamelCase__       :     int 							= (1.0 - extended_attention_mask) * -1_0000.0
      UpperCamelCase__       :     Optional[Any] 							= self.get_head_mask(lowerCamelCase__				,						self.config.num_hidden_layers      )

      UpperCamelCase__       :     List[Any] 							= self.embeddings(
          lowerCamelCase__				,						position_ids=lowerCamelCase__				,						token_type_ids=lowerCamelCase__				,						inputs_embeds=lowerCamelCase__      )
      UpperCamelCase__       :     List[str] 							= embedding_output

      if self.training:
       UpperCamelCase__       :     Union[str, Any] 							= []
       for i in range(self.config.num_hidden_layers      ):
        UpperCamelCase__       :     int 							= self.encoder.adaptive_forward(
            lowerCamelCase__				,						current_layer=lowerCamelCase__				,						attention_mask=lowerCamelCase__				,						head_mask=lowerCamelCase__				,						)

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

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

        UpperCamelCase__       :     str 							= logits
        if patient_counter == self.patience:
         break
       UpperCamelCase__       :     Dict 							= [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. "       ,					__lowerCAmelCase       ,					)
class    __magic_name__						(  __lowerCAmelCase):


     def __init__(   self				:  int				,						lowerCamelCase__				:  Dict      )	->   str:





      '''simple docstring'''






      super().__init__(lowerCamelCase__      )
      UpperCamelCase__       :     Union[str, Any] 							= config.num_labels

      UpperCamelCase__       :     Tuple 							= AlbertModelWithPabee(lowerCamelCase__      )
      UpperCamelCase__       :     Dict 							= nn.Dropout(config.classifier_dropout_prob      )
      UpperCamelCase__       :     Optional[int] 							= 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				:  str				,						lowerCamelCase__				:  List[Any]=None				,						lowerCamelCase__				:  Optional[int]=None				,						lowerCamelCase__				:  List[str]=None				,						lowerCamelCase__				:  List[str]=None				,						lowerCamelCase__				:  Optional[Any]=None				,						lowerCamelCase__				:  str=None				,						lowerCamelCase__				:  Optional[int]=None				,						)	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Any 							= 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				,						)

      UpperCamelCase__       :     Optional[Any] 							= (logits[-1],)

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

      return outputs






import numpy as np

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple  ):







 """simple docstring"""



 UpperCamelCase__       :     str 							= np.exp(SCREAMING_SNAKE_CASE  )

 # Add up the all the exponentials
 UpperCamelCase__       :     Optional[int] 							= np.sum(SCREAMING_SNAKE_CASE  )

 # Divide every exponent by the sum of all exponents
 UpperCamelCase__       :     Optional[Any] 							= exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
       print(softmax((0,)))


