



"""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


A     =      logging.getLogger(__name__)

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Dict:
							random.seed(lowerCamelCase__      )
							np.random.seed(lowerCamelCase__      )
							torch.manual_seed(lowerCamelCase__      )
							torch.cuda.manual_seed_all(lowerCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				int:
							model.eval()
							A    =       next(model.parameters()      ).device
							A    =       1
							A    =       torch.zeros((eval_batch_size, context_len)					,   dtype=torch.long					,   device=lowerCamelCase__      )
							A    =       DataLoader(lowerCamelCase__					,   shuffle=lowerCamelCase__					,   batch_size=lowerCamelCase__      )
							A    =       torch.zeros(1					,   device=lowerCamelCase__      )
							A    =       0
							for batch in eval_dataloader:
														batch.to(lowerCamelCase__      )
														# pad
														context.zero_()
														for i in range(lowerCamelCase__      ):
																					A    =       batch[i]
														A    =       model(lowerCamelCase__					,   labels=lowerCamelCase__      )
														eval_loss += outputs[0].sum().item()
														nb_eval_examples += batch.size(0      )
							A    =       eval_loss / nb_eval_examples
							A    =       torch.exp(lowerCamelCase__      )
							model.train()
							return perplexity

def 	lowerCAmelCase__      (  lowerCamelCase__="gpt2"      )			->				Any:
							A    =       GPTaLMHeadModel.from_pretrained(lowerCamelCase__					,   output_hidden_states=lowerCamelCase__      )
							torch.save(model.state_dict()					,   model_name + 'local.pt'      )
							return model

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				List[Any]:
							A    =       copy.deepcopy(lowerCamelCase__      )
							model.to(lowerCamelCase__      )

							A    =       ['bias', 'LayerNorm.weight']
							A    =       [
							    {
							        '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},
							]
							A    =       AdamW(lowerCamelCase__					,   lr=5E-5					,   eps=1E-8      )
							A    =       get_linear_schedule_with_warmup(lowerCamelCase__					,   0					,   lowerCamelCase__      )
							torch.cuda.empty_cache()
							return model, lm_optimizer, lm_scheduler

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Optional[int]:
							A    =       np.array(lowerCamelCase__      ).mean()
							A    =       np.array(lowerCamelCase__      ).std()
							A    =       (real_perps - avg) / std
							A    =       list(zip(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )      )
							joblib.dump(lowerCamelCase__					,   lowerCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__="dev.jbl"					,   lowerCamelCase__=recopy_gpta					,   )			->				List[Any]:
							A    =       []
							A    =       []
							A    =       []

							# Initialize the transformer model
							A    =       copy.deepcopy(lowerCamelCase__      )
							orig_model.to(device='cpu'      )
							torch.cuda.empty_cache()

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

							for step in tqdm(range(lowerCamelCase__      )      ):
														A    =       torch.zeros((1, context_len)					,   dtype=torch.long					,   device=lowerCamelCase__      )
														A    =       random.choice(lowerCamelCase__      )
														A    =       random.randint(0					,   len(story[0]      ) - context_len - 1      )
														A    =       story[0][start : start + context_len]
														lm_optimizer.zero_grad()
														A    =       model(lowerCamelCase__					,   labels=lowerCamelCase__      )
														A    =       outputs[0]
														A    =       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
														A    =       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
														A   ,	A   ,	A    =       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 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__="data/tokenized_stories_train_wikitext103.jbl"					,   lowerCamelCase__=100					,   lowerCamelCase__=1026					,   lowerCamelCase__=True      )			->				Dict:
							A    =       joblib.load(lowerCamelCase__      )
							print('data loaded'      )
							A    =       []
							if trim:
														for i, example in enumerate(lowerCamelCase__      ):
																					if len(example[0]      ) > min_len:
																												A    =       random.randint(0					,   len(example[0]      ) - context_len - 1      )
																												objective_set.append(example[0, start : start + context_len]      )
																					if len(lowerCamelCase__      ) >= number:
																												break
														A    =       []
														for j in range(i + 1					,   len(lowerCamelCase__      )      ):
																					if len(data[j][0]      ) > min_len:
																												train_data.append(data[j]      )
							else:
														A    =       data[0:number]
														A    =       data[number:]

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

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__=50					,   lowerCamelCase__="secondary_learner.pt"      )			->				Tuple:
							A    =       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
							A    =       train_dataset[:512]
							A    =       train_dataset[512:]
							A    =       DataLoader(lowerCamelCase__					,   shuffle=lowerCamelCase__					,   batch_size=lowerCamelCase__      )
							A    =       DataLoader(lowerCamelCase__					,   shuffle=lowerCamelCase__					,   batch_size=lowerCamelCase__      )

							# secondary learner model set up
							A    =       nn.MSELoss()
							A    =       nn.MSELoss(reduction='sum'      )
							secondary_learner.to(lowerCamelCase__      )
							A    =       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
							A    =       float('inf'      )
							# Iterate through batches until we've used max_steps batches
							for epoch in range(int(lowerCamelCase__      )      ):
														A    =       0.0
														secondary_learner.train()
														for step, batch in enumerate(lowerCamelCase__      ):
																					A    =       batch[0].to(lowerCamelCase__      )
																					A    =       batch[1].to(lowerCamelCase__      )
																					A    =       secondary_learner(lowerCamelCase__      )
																					q_optimizer.zero_grad()
																					A    =       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__      )):
																												A    =       tr_q_loss / (step + 1)

																												secondary_learner.eval()
																												A    =       0.0
																												A    =       0.0
																												A    =       []
																												A    =       []
																												# Compute performance of the secondary learner after this batch
																												for stepa, batcha in enumerate(lowerCamelCase__      ):
																																			A    =       batcha[0].to(lowerCamelCase__      )
																																			A    =       batcha[1].to(lowerCamelCase__      )
																																			A    =       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__      )
																																			A    =       q_lossa

																					secondary_learner.train()
							return secondary_learner






class   UpperCAmelCase__		(   nn.Module  ):



		def __init__(			self	:     List[Any]   ,				snake_case	:     Dict					)      ->    int:






									'''simple docstring'''

									super(snake_case   ,				self					).__init__()
									A    =       model.transformer.wte
									A    =       copy.deepcopy(model.transformer.wte.weight					)
									A    =       nn.Convad(self.embeddings.weight.size(1					)   ,				256   ,				3   ,				padding=1					)
									A    =       nn.Sequential(nn.Linear(256   ,				32					)   ,				nn.Dropout(p=0.1					)   ,				nn.Linear(32   ,				32					)   ,				nn.Linear(32   ,				1					)					)



		def        A_		(			self	:     Any   ,				snake_case	:     str					)      ->    List[Any]:






									'''simple docstring'''

									A    =       torch.max(self.conv(self.embeddings(snake_case					).squeeze(1					).transpose(1   ,				2					)					)   ,				2					)[0]
									A    =       self.fc(snake_case					)
									return qs.squeeze(1					)



		@classmethod
		def        A_		(			cls	:     List[str]   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     str					)      ->    Dict:






									'''simple docstring'''

									A    =       cls(snake_case					)  # this calls __init__
									A    =       torch.load(snake_case					)
									secondary_learner.load_state_dict(snake_case					)
									A    =       model.transformer.wte
									A    =       copy.deepcopy(model.transformer.wte.weight					)
									return secondary_learner








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[str]:
							A    =       [701, 301, 132, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(lowerCamelCase__					,   len(lowerCamelCase__      )      ):
																					A    =       collection[i]
																					A    =       i
																					while j >= gap and collection[j - gap] > insert_value:
																												A    =       collection[j - gap]
																												j -= gap
																					if j != i:
																												A    =       insert_value
							return collection


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							A     =      input('Enter numbers separated by a comma:\n').strip()
							A     =      [int(item) for item in user_input.split(',')]
							print(shell_sort(unsorted))



