
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			:     Tuple           =   logging.getLogger(__name__)




class        UpperCAmelCase_			( __lowercase     ):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__				     =       self.layer[current_layer](UpperCAmelCase__			,							UpperCAmelCase__			,							head_mask[current_layer]    )

    __magic_name__				     =       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."""  ,       __lowercase  ,       )
class        UpperCAmelCase_			( __lowercase     ):


  '''simple docstring'''







  def __init__( self			: int			,							UpperCamelCase__			: Tuple    )			->	Dict:







    """simple docstring"""






    super().__init__(UpperCAmelCase__    )

    __magic_name__				     =       BertEncoderWithPabee(UpperCAmelCase__    )

    self.init_weights()
    __magic_name__				     =       0
    __magic_name__				     =       0
    __magic_name__				     =       0

    __magic_name__				     =       0
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       threshold
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       patience
  def 							_lowercase							( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       0
    __magic_name__				     =       0
  def 							_lowercase							( self			: int    )			->	str:







    """simple docstring"""






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







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

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

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

    __magic_name__				     =       self.embeddings(
        input_ids=UpperCAmelCase__			,							position_ids=UpperCAmelCase__			,							token_type_ids=UpperCAmelCase__			,							inputs_embeds=UpperCAmelCase__    )
    __magic_name__				     =       embedding_output

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

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

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

        __magic_name__				     =       logits
        if patient_counter == self.patience:
          break
      __magic_name__				     =       [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. """  ,       __lowercase  ,       )
class        UpperCAmelCase_			( __lowercase     ):


  '''simple docstring'''







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







    """simple docstring"""






    super().__init__(UpperCAmelCase__    )
    __magic_name__				     =       config.num_labels

    __magic_name__				     =       BertModelWithPabee(UpperCAmelCase__    )
    __magic_name__				     =       nn.Dropout(config.hidden_dropout_prob    )
    __magic_name__				     =       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 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: List[Any]=None			,							UpperCamelCase__			: List[str]=None			,							UpperCamelCase__			: Optional[int]=None			,							UpperCamelCase__			: Tuple=None			,							UpperCamelCase__			: List[str]=None			,							UpperCamelCase__			: int=None			,							UpperCamelCase__			: Any=None			,							)			->	Any:







    """simple docstring"""






    __magic_name__				     =       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			,							)

    __magic_name__				     =       (logits[-1],)

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

    return outputs





'''simple docstring'''




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		UpperCAmelCase_      (  __lowercase		):





     def __lt__( self       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[Any]:
        return self[-1] < other[-1]





     def __eq__( self       :      str    ,      UpperCAmelCase__       :      List[str]    )				->  Tuple:
        return self[-1] == other[-1]





def 	a_					( lowerCamelCase	:     list				):
   lowerCAmelCase     =			[]
   # sort into stacks
   for element in collection:
      lowerCAmelCase     =			Stack([element]				)
      lowerCAmelCase     =			bisect_left(lowerCamelCase	, lowerCamelCase				)
      if i != len(lowerCamelCase				):
         stacks[i].append(lowerCamelCase				)
      else:
         stacks.append(lowerCamelCase				)

    # use a heap-based merge to merge stack efficiently
   lowerCAmelCase     =			merge(*(reversed(lowerCamelCase				) for stack in stacks)				)
   return collection


if __name__ == "__main__":
       __snake_case    						=input("""Enter numbers separated by a comma:\n""").strip()
       __snake_case    						=[int(item) for item in user_input.split(""",""")]
       print(patience_sort(unsorted))





