





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,
)


_UpperCAmelCase             =							logging.getLogger(__name__)






class        snake_case_    (		__lowercase						):






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Any       ,  _snake_case			:					Union[str, Any]=None       ,  _snake_case			:					Union[str, Any]=None      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.layer[current_layer](_snake_case       ,  _snake_case       ,  head_mask[current_layer]      )

   __lowerCAmelCase					:	List[Any]      =       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        snake_case_    (		__lowercase						):






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






   '''simple docstring'''

   super().__init__(_snake_case      )

   __lowerCAmelCase					:	str      =       BertEncoderWithPabee(_snake_case      )

   self.init_weights()
   __lowerCAmelCase					:	str      =       0
   __lowerCAmelCase					:	Dict      =       0
   __lowerCAmelCase					:	Union[str, Any]      =       0

   __lowerCAmelCase					:	int      =       0






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					str      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       threshold






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					List[str]      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       patience






  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       0
   __lowerCAmelCase					:	str      =       0






  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       self.inference_layers_num / self.inference_instances_num
   __lowerCAmelCase					:	str      =       (
       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			:					Union[str, Any]       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Union[str, Any]=None       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					Optional[Any]=None       ,  _snake_case			:					Dict=None       ,  _snake_case			:					int=None       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					Dict=None       ,  _snake_case			:					Any=None       ,  _snake_case			:					Tuple=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:
    __lowerCAmelCase					:	Any      =       input_ids.size()
   elif inputs_embeds is not None:
    __lowerCAmelCase					:	Optional[Any]      =       inputs_embeds.size()[:-1]
   else:
    raise ValueError("""You have to specify either input_ids or inputs_embeds"""      )

   __lowerCAmelCase					:	Dict      =       input_ids.device if input_ids is not None else inputs_embeds.device

   if attention_mask is None:
    __lowerCAmelCase					:	Dict      =       torch.ones(_snake_case       ,  device=_snake_case      )
   if token_type_ids is None:
    __lowerCAmelCase					:	List[Any]      =       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.
   __lowerCAmelCase					:	torch.Tensor      =       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:
    __lowerCAmelCase					:	Union[str, Any]      =       encoder_hidden_states.size()
    __lowerCAmelCase					:	List[str]      =       (encoder_batch_size, encoder_sequence_length)
    if encoder_attention_mask is None:
     __lowerCAmelCase					:	str      =       torch.ones(_snake_case       ,  device=_snake_case      )
    __lowerCAmelCase					:	List[Any]      =       self.invert_attention_mask(_snake_case      )
   else:
    __lowerCAmelCase					:	Tuple      =       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]
   __lowerCAmelCase					:	Dict      =       self.get_head_mask(_snake_case       ,  self.config.num_hidden_layers      )

   __lowerCAmelCase					:	List[Any]      =       self.embeddings(
       input_ids=_snake_case       ,  position_ids=_snake_case       ,  token_type_ids=_snake_case       ,  inputs_embeds=_snake_case      )
   __lowerCAmelCase					:	Any      =       embedding_output

   if self.training:
    __lowerCAmelCase					:	int      =       []
    for i in range(self.config.num_hidden_layers      ):
     __lowerCAmelCase					:	List[Any]      =       self.encoder.adaptive_forward(
         _snake_case       ,  current_layer=_snake_case       ,  attention_mask=_snake_case       ,  head_mask=_snake_case      )

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

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

     __lowerCAmelCase					:	int      =       logits
     if patient_counter == self.patience:
      break
    __lowerCAmelCase					:	Any      =       [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. '					,__lowercase					,)
class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Dict       ,  _snake_case			:					Tuple      )->Union[str, Any]:






   '''simple docstring'''

   super().__init__(_snake_case      )
   __lowerCAmelCase					:	Any      =       config.num_labels

   __lowerCAmelCase					:	Optional[Any]      =       BertModelWithPabee(_snake_case      )
   __lowerCAmelCase					:	Union[str, Any]      =       nn.Dropout(config.hidden_dropout_prob      )
   __lowerCAmelCase					:	Dict      =       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			:					Dict       ,  _snake_case			:					int=None       ,  _snake_case			:					List[str]=None       ,  _snake_case			:					str=None       ,  _snake_case			:					str=None       ,  _snake_case			:					List[str]=None       ,  _snake_case			:					Tuple=None       ,  _snake_case			:					Dict=None       ,  )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       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       ,  )

   __lowerCAmelCase					:	List[str]      =       (logits[-1],)

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

   return outputs






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

 return [i for i in range(2   ,   SCREAMING_SNAKE_CASE   ) if is_prime[i]]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_SNAKE_CASE   ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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