



'''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,
)


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

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def  A       (  self      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=None ):


       """simple docstring"""







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

       UpperCamelCase					=  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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Any  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       super().__init__(UpperCamelCase__ )

       UpperCamelCase					=  BertEncoderWithPabee(UpperCamelCase__ )

       self.init_weights()
       UpperCamelCase					=  0
       UpperCamelCase					=  0
       UpperCamelCase					=  0

       UpperCamelCase					=  0






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  threshold






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  patience






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  0
       UpperCamelCase					=  0






    def  A       (  self      :					List[Any] ):


       """simple docstring"""







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


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

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

       if attention_mask is None:
          UpperCamelCase					=  torch.ones(UpperCamelCase__  ,						device=UpperCamelCase__ )
       if token_type_ids is None:
          UpperCamelCase					=  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.
       UpperCamelCase					=  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:
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  encoder_hidden_states.size()
          UpperCamelCase					=  (encoder_batch_size, encoder_sequence_length)
          if encoder_attention_mask is None:
             UpperCamelCase					=  torch.ones(UpperCamelCase__  ,						device=UpperCamelCase__ )
          UpperCamelCase					=  self.invert_attention_mask(UpperCamelCase__ )
       else:
          UpperCamelCase					=  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]
       UpperCamelCase					=  self.get_head_mask(UpperCamelCase__  ,						self.config.num_hidden_layers )

       UpperCamelCase					=  self.embeddings(
           input_ids=UpperCamelCase__  ,						position_ids=UpperCamelCase__  ,						token_type_ids=UpperCamelCase__  ,						inputs_embeds=UpperCamelCase__ )
       UpperCamelCase					=  embedding_output

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

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

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

             UpperCamelCase					=  logits
             if patient_counter == self.patience:
                break
          UpperCamelCase					=  [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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Tuple  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







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

       UpperCamelCase					=  BertModelWithPabee(UpperCamelCase__ )
       UpperCamelCase					=  nn.Dropout(config.hidden_dropout_prob )
       UpperCamelCase					=  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  A       (  self      :					str  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Any=None  ,						UpperCamelCase__      :					Tuple=None  ,						UpperCamelCase__      :					Dict=None  ,						UpperCamelCase__      :					Optional[Any]=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  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  ,						)

       UpperCamelCase					=  (logits[-1],)

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

       return outputs






'''simple docstring'''


def 				__lowerCamelCase				(   A__ = 10**9    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  1
   UpperCamelCase					=  2

   UpperCamelCase					=  0
   UpperCamelCase					=  0
   UpperCamelCase					=  0
   while perimeter <= max_perimeter:
      perimeters_sum += perimeter

      prev_value += 2 * value
      value += prev_value

      UpperCamelCase					=  2 * value + 2 if i % 2 == 0 else 2 * value - 2
      i += 1

   return perimeters_sum


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

