



"""simple docstring"""






import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowercase					:					Optional[Any] 		=		logging.getLogger(__name__)






def 	snake_case__  (						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			random.seed(__lowerCamelCase      )
			np.random.seed(__lowerCamelCase      )
			torch.manual_seed(__lowerCamelCase      )
			torch.cuda.manual_seed_all(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						Union[str, Any]						      =next(model.parameters()      ).device
			lowerCamelCase__						:						Union[str, Any]						      =1
			lowerCamelCase__						:						int						      =torch.zeros((eval_batch_size, context_len)				,						dtype=torch.long				,						device=__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =torch.zeros(1				,						device=__lowerCamelCase      )
			lowerCamelCase__						:						Optional[Any]						      =0
			for batch in eval_dataloader:
						batch.to(__lowerCamelCase      )
						# pad
						context.zero_()
						for i in range(__lowerCamelCase      ):
									lowerCamelCase__						:						Optional[int]						      =batch[i]
						lowerCamelCase__						:						Tuple						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0      )
			lowerCamelCase__						:						Union[str, Any]						      =eval_loss / nb_eval_examples
			lowerCamelCase__						:						Union[str, Any]						      =torch.exp(__lowerCamelCase      )
			model.train()
			return perplexity






def 	snake_case__  (						__lowerCamelCase							:       List[Any]="gpt2"      ):
			"""simple docstring"""
			lowerCamelCase__						:						Tuple						      =GPTaLMHeadModel.from_pretrained(__lowerCamelCase				,						output_hidden_states=__lowerCamelCase      )
			torch.save(model.state_dict()				,						model_name + '''local.pt'''      )
			return model






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       int      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =copy.deepcopy(__lowerCamelCase      )
			model.to(__lowerCamelCase      )

			lowerCamelCase__						:						List[Any]						      =['''bias''', '''LayerNorm.weight''']
			lowerCamelCase__						:						Optional[int]						      =[
			    {
			        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
			        '''weight_decay''': 0.0,
			    },
			    {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )], '''weight_decay''': 0.0},
			]
			lowerCamelCase__						:						List[Any]						      =AdamW(__lowerCamelCase				,						lr=5e-5				,						eps=1e-8      )
			lowerCamelCase__						:						Union[str, Any]						      =get_linear_schedule_with_warmup(__lowerCamelCase				,						0				,						__lowerCamelCase      )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =np.array(__lowerCamelCase      ).mean()
			lowerCamelCase__						:						Optional[int]						      =np.array(__lowerCamelCase      ).std()
			lowerCamelCase__						:						List[str]						      =(real_perps - avg) / std
			lowerCamelCase__						:						Optional[int]						      =list(zip(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )      )
			joblib.dump(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       str="dev.jbl"				,						__lowerCamelCase							:       List[str]=recopy_gpta				,						):
			"""simple docstring"""
			lowerCamelCase__						:						List[Any]						      =[]
			lowerCamelCase__						:						Any						      =[]
			lowerCamelCase__						:						Tuple						      =[]

			# Initialize the transformer model
			lowerCamelCase__						:						List[Any]						      =copy.deepcopy(__lowerCamelCase      )
			orig_model.to(device='''cpu'''      )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						str						      =recopy_model(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			for step in tqdm(range(__lowerCamelCase      )      ):
						lowerCamelCase__						:						Dict						      =torch.zeros((1, context_len)				,						dtype=torch.long				,						device=__lowerCamelCase      )
						lowerCamelCase__						:						List[Any]						      =random.choice(__lowerCamelCase      )
						lowerCamelCase__						:						Optional[Any]						      =random.randint(0				,						len(story[0]      ) - context_len - 1      )
						lowerCamelCase__						:						int						      =story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						lowerCamelCase__						:						Dict						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						List[Any]						      =outputs[0]
						lowerCamelCase__						:						Dict						      =compute_perplexity(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()				,						3.0      )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						lowerCamelCase__						:						str						      =compute_perplexity(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1000 == 0 and step > 1:
									intermittent_save(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Union[str, Any]						      =recopy_model(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

						past_perps.append(past_perp.item()      )
						real_perps.append(orig_perp - real_perp.item()      )
						contexts.append(np.array(context.cpu()      )      )

			intermittent_save(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"				,						__lowerCamelCase							:       Tuple=100				,						__lowerCamelCase							:       Union[str, Any]=1026				,						__lowerCamelCase							:       Optional[Any]=True      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =joblib.load(__lowerCamelCase      )
			print('''data loaded'''      )
			lowerCamelCase__						:						Union[str, Any]						      =[]
			if trim:
						for i, example in enumerate(__lowerCamelCase      ):
									if len(example[0]      ) > min_len:
												lowerCamelCase__						:						str						      =random.randint(0				,						len(example[0]      ) - context_len - 1      )
												objective_set.append(example[0, start : start + context_len]      )
									if len(__lowerCamelCase      ) >= number:
												break
						lowerCamelCase__						:						Optional[Any]						      =[]
						for j in range(i + 1				,						len(__lowerCamelCase      )      ):
									if len(data[j][0]      ) > min_len:
												train_data.append(data[j]      )
			else:
						lowerCamelCase__						:						int						      =data[0:number]
						lowerCamelCase__						:						Union[str, Any]						      =data[number:]

			joblib.dump(__lowerCamelCase				,						'''objective_set.jbl'''      )
			print('''objective set saved'''      )
			return train_data, objective_set






def 	snake_case__  (						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Tuple=50				,						__lowerCamelCase							:       List[Any]="secondary_learner.pt"      ):
			"""simple docstring"""
			lowerCamelCase__						:						Union[str, Any]						      =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			lowerCamelCase__						:						Optional[int]						      =train_dataset[:512]
			lowerCamelCase__						:						str						      =train_dataset[512:]
			lowerCamelCase__						:						Optional[Any]						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )
			lowerCamelCase__						:						Tuple						      =DataLoader(__lowerCamelCase				,						shuffle=__lowerCamelCase				,						batch_size=__lowerCamelCase      )

			# secondary learner model set up
			lowerCamelCase__						:						Optional[int]						      =nn.MSELoss()
			lowerCamelCase__						:						Any						      =nn.MSELoss(reduction='''sum'''      )
			secondary_learner.to(__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =torch.optim.Adam(secondary_learner.parameters()				,						lr=0.0_00_01      )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			lowerCamelCase__						:						Dict						      =float('''inf'''      )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(__lowerCamelCase      )      ):
						lowerCamelCase__						:						Union[str, Any]						      =0.0
						secondary_learner.train()
						for step, batch in enumerate(__lowerCamelCase      ):
									lowerCamelCase__						:						Dict						      =batch[0].to(__lowerCamelCase      )
									lowerCamelCase__						:						str						      =batch[1].to(__lowerCamelCase      )
									lowerCamelCase__						:						Any						      =secondary_learner(__lowerCamelCase      )
									q_optimizer.zero_grad()
									lowerCamelCase__						:						Optional[Any]						      =loss(__lowerCamelCase				,						real_q.float()      )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__lowerCamelCase      )):
												lowerCamelCase__						:						List[str]						      =tr_q_loss / (step + 1)

												secondary_learner.eval()
												lowerCamelCase__						:						Tuple						      =0.0
												lowerCamelCase__						:						Union[str, Any]						      =0.0
												lowerCamelCase__						:						Tuple						      =[]
												lowerCamelCase__						:						List[str]						      =[]
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(__lowerCamelCase      ):
															lowerCamelCase__						:						Tuple						      =batcha[0].to(__lowerCamelCase      )
															lowerCamelCase__						:						str						      =batcha[1].to(__lowerCamelCase      )
															lowerCamelCase__						:						Dict						      =secondary_learner(__lowerCamelCase      )
															q_lossa += test_loss(__lowerCamelCase				,						__lowerCamelCase      ).item()
															sum_qa += torch.sum(__lowerCamelCase      ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy()      ):
																		predicted.append(i.item()      )
															for ei, i in enumerate(real_qa.cpu().detach().numpy()      ):
																		actual.append(i.item()      )

												q_lossa /= len(__lowerCamelCase      )
												print(
												    '''Epoch: '''				,						__lowerCamelCase				,						'''step: '''				,						__lowerCamelCase				,						'''Avg. q:'''				,						sum_qa / len(__lowerCamelCase      )				,						'''Train Loss: '''				,						__lowerCamelCase				,						'''Test Loss: '''				,						__lowerCamelCase				,						)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)				,						'''pred_vs_actual.jbl'''      )
															torch.save(secondary_learner.state_dict()				,						__lowerCamelCase      )
															lowerCamelCase__						:						Union[str, Any]						      =q_lossa

									secondary_learner.train()
			return secondary_learner

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    str,    lowerCamelCase				:    Union[str, Any]	)->   Dict:
								super(lowerCamelCase,    self	).__init__()
								lowerCamelCase__						:						str						      =model.transformer.wte
								lowerCamelCase__						:						Tuple						      =copy.deepcopy(model.transformer.wte.weight	)
								lowerCamelCase__						:						Union[str, Any]						      =nn.Convad(self.embeddings.weight.size(1	),    256,    3,    padding=1	)
								lowerCamelCase__						:						Any						      =nn.Sequential(nn.Linear(256,    32	),    nn.Dropout(p=0.1	),    nn.Linear(32,    32	),    nn.Linear(32,    1	)	)



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    int	)->   Dict:
								lowerCamelCase__						:						Tuple						      =torch.max(self.conv(self.embeddings(lowerCamelCase	).squeeze(1	).transpose(1,    2	)	),    2	)[0]
								lowerCamelCase__						:						List[str]						      =self.fc(lowerCamelCase	)
								return qs.squeeze(1	)




					@classmethod
					def 						snake_case			(			cls				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    int	)->   Optional[int]:
								lowerCamelCase__						:						List[str]						      =cls(lowerCamelCase	)  # this calls __init__
								lowerCamelCase__						:						Union[str, Any]						      =torch.load(lowerCamelCase	)
								secondary_learner.load_state_dict(lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =model.transformer.wte
								lowerCamelCase__						:						Optional[int]						      =copy.deepcopy(model.transformer.wte.weight	)
								return secondary_learner









"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




