"""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,
)


__SCREAMING_SNAKE_CASE						:   Tuple         =			logging.getLogger(__name__)



class        lowercase_   (					lowerCamelCase__		):



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_=None				,    lowercase_=None    ):
													_snake_case						:					Optional[int]    =   self.layer[current_layer](lowercase__				,    lowercase__				,    head_mask[current_layer]    )

													_snake_case						:					Optional[int]    =   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.'						,			lowerCamelCase__						,			)
class        lowercase_   (					lowerCamelCase__		):



							def __init__(						self				,    lowercase_    ):
													super().__init__(lowercase__    )

													_snake_case						:					Tuple    =   BertEncoderWithPabee(lowercase__    )

													self.init_weights()
													_snake_case						:					Tuple    =   0
													_snake_case						:					int    =   0
													_snake_case						:					Optional[int]    =   0

													_snake_case						:					Any    =   0



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					str    =   threshold



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Any    =   patience



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   0
													_snake_case						:					str    =   0



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.inference_layers_num / self.inference_instances_num
													_snake_case						:					List[Any]    =   (
													    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(lowercase__    )



							@add_start_docstrings_to_model_forward(lowercase__    )
							def 	UpperCamelCase	(						self				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=False				,    ):

													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:
																			_snake_case						:					Union[str, Any]    =   input_ids.size()
													elif inputs_embeds is not None:
																			_snake_case						:					List[str]    =   inputs_embeds.size()[:-1]
													else:
																			raise ValueError("You have to specify either input_ids or inputs_embeds"    )

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

													if attention_mask is None:
																			_snake_case						:					int    =   torch.ones(lowercase__				,    device=lowercase__    )
													if token_type_ids is None:
																			_snake_case						:					Union[str, Any]    =   torch.zeros(lowercase__				,    dtype=torch.long				,    device=lowercase__    )

													# 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.
													_snake_case						:					Union[str, Any]    =   self.get_extended_attention_mask(lowercase__				,    lowercase__				,    lowercase__    )

													# 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:
																			_snake_case    ,_snake_case    ,_snake_case						:					List[Any]    =   encoder_hidden_states.size()
																			_snake_case						:					Optional[Any]    =   (encoder_batch_size, encoder_sequence_length)
																			if encoder_attention_mask is None:
																									_snake_case						:					Optional[Any]    =   torch.ones(lowercase__				,    device=lowercase__    )
																			_snake_case						:					List[Any]    =   self.invert_attention_mask(lowercase__    )
													else:
																			_snake_case						:					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]
													_snake_case						:					List[str]    =   self.get_head_mask(lowercase__				,    self.config.num_hidden_layers    )

													_snake_case						:					Tuple    =   self.embeddings(
													    input_ids=lowercase__				,    position_ids=lowercase__				,    token_type_ids=lowercase__				,    inputs_embeds=lowercase__    )
													_snake_case						:					Optional[int]    =   embedding_output

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

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

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

																									_snake_case						:					Tuple    =   logits
																									if patient_counter == self.patience:
																															break
																			_snake_case						:					Tuple    =   [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. '						,			lowerCamelCase__						,			)
class        lowercase_   (					lowerCamelCase__		):



							def __init__(						self				,    lowercase_    ):
													super().__init__(lowercase__    )
													_snake_case						:					List[str]    =   config.num_labels

													_snake_case						:					List[str]    =   BertModelWithPabee(lowercase__    )
													_snake_case						:					Union[str, Any]    =   nn.Dropout(config.hidden_dropout_prob    )
													_snake_case						:					str    =   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(lowercase__    )
							def 	UpperCamelCase	(						self				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    ):
													_snake_case						:					Optional[Any]    =   self.bert(
													    input_ids=lowercase__				,    attention_mask=lowercase__				,    token_type_ids=lowercase__				,    position_ids=lowercase__				,    head_mask=lowercase__				,    inputs_embeds=lowercase__				,    output_dropout=self.dropout				,    output_layers=self.classifiers				,    regression=self.num_labels == 1				,    )

													_snake_case						:					Dict    =   (logits[-1],)

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

													return outputs
def  snake_case						(__lowercase						)	->	list[int]:

						'''simple docstring'''



						if num <= 0:
												raise ValueError("Input must be a positive integer"						)

						_snake_case						:					Any    =   [True] * (num + 1)

						_snake_case						:					str    =   2
						while p * p <= num:
												if primes[p]:
																		for i in range(p * p ,      num + 1 ,      __lowercase						):
																								_snake_case						:					Optional[int]    =   False
												p += 1

						return [prime for prime in range(2 ,      num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__SCREAMING_SNAKE_CASE						:   Any         =			int(input('Enter a positive integer: ').strip())
				print(prime_sieve_eratosthenes(user_num))