

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,
)


a_     :    Dict     		=   logging.getLogger(__name__)




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   Dict				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Dict=None				,     snake_case__			:   Optional[int]=None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.layer[current_layer](snake_case__				,     snake_case__				,     head_mask[current_layer]     )

     SCREAMING_SNAKE_CASE		    =      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."						,      SCREAMING_SNAKE_CASE						,      )
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def __init__(      self			:   str				,     snake_case__			:   Dict     ):

     """simple docstring"""






     super().__init__(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      BertEncoderWithPabee(snake_case__     )

     self.init_weights()
     SCREAMING_SNAKE_CASE		    =      0
     SCREAMING_SNAKE_CASE		    =      0
     SCREAMING_SNAKE_CASE		    =      0

     SCREAMING_SNAKE_CASE		    =      0

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      threshold

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      patience

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      0
     SCREAMING_SNAKE_CASE		    =      0

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.inference_layers_num / self.inference_instances_num
     SCREAMING_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(snake_case__     )




    @add_start_docstrings_to_model_forward(snake_case__     )
    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Union[str, Any]=None				,     snake_case__			:   str=None				,     snake_case__			:   Any=None				,     snake_case__			:   List[str]=None				,     snake_case__			:   Union[str, Any]=None				,     snake_case__			:   Tuple=None				,     snake_case__			:   int=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Any=None				,     snake_case__			:   Dict=None				,     snake_case__			:   Dict=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:
      SCREAMING_SNAKE_CASE		    =      input_ids.size()
     elif inputs_embeds is not None:
      SCREAMING_SNAKE_CASE		    =      inputs_embeds.size()[:-1]
     else:
      raise ValueError('You have to specify either input_ids or inputs_embeds'     )

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

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

     # 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.
     SCREAMING_SNAKE_CASE		    =      self.get_extended_attention_mask(snake_case__				,     snake_case__				,     snake_case__     )

     # 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:
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      encoder_hidden_states.size()
      SCREAMING_SNAKE_CASE		    =      (encoder_batch_size, encoder_sequence_length)
      if encoder_attention_mask is None:
       SCREAMING_SNAKE_CASE		    =      torch.ones(snake_case__				,     device=snake_case__     )
      SCREAMING_SNAKE_CASE		    =      self.invert_attention_mask(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      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]
     SCREAMING_SNAKE_CASE		    =      self.get_head_mask(snake_case__				,     self.config.num_hidden_layers     )

     SCREAMING_SNAKE_CASE		    =      self.embeddings(
         input_ids=snake_case__				,     position_ids=snake_case__				,     token_type_ids=snake_case__				,     inputs_embeds=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      embedding_output

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

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

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

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

    def __init__(      self			:   Optional[int]				,     snake_case__			:   List[Any]     ):

     """simple docstring"""






     super().__init__(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      config.num_labels

     SCREAMING_SNAKE_CASE		    =      BertModelWithPabee(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      nn.Dropout(config.hidden_dropout_prob     )
     SCREAMING_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(snake_case__     )
    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   str=None				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Tuple=None				,     snake_case__			:   str=None				,     snake_case__			:   int=None				,     snake_case__			:   Dict=None				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.bert(
         input_ids=snake_case__				,     attention_mask=snake_case__				,     token_type_ids=snake_case__				,     position_ids=snake_case__				,     head_mask=snake_case__				,     inputs_embeds=snake_case__				,     output_dropout=self.dropout				,     output_layers=self.classifiers				,     regression=self.num_labels == 1				,     )

     SCREAMING_SNAKE_CASE		    =      (logits[-1],)

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

     return outputs




import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

 while start <= end:
  if temp[start] is True:
   in_prime.append(_UpperCamelCase						)
   for i in range(start * start		,				end + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False
  start += 1
 prime += in_prime

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 while low <= n:
  SCREAMING_SNAKE_CASE		    =      [True] * (high - low + 1)
  for each in in_prime:
   SCREAMING_SNAKE_CASE		    =      math.floor(low / each						) * each
   if t < low:
    t += each

   for j in range(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

  for j in range(len(_UpperCamelCase						)						):
   if temp[j] is True:
    prime.append(j + low						)

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

