



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


__SCREAMING_SNAKE_CASE   					=     logging.getLogger(__name__)




def 		UpperCAmelCase      (  _lowerCamelCase			):
			random.seed(_a			)
			np.random.seed(_a			)
			torch.manual_seed(_a			)
			torch.cuda.manual_seed_all(_a			)




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




def 		UpperCAmelCase      (  _lowerCamelCase="gpt2"			):
			A							:    Any								=  GPTaLMHeadModel.from_pretrained(_a       ,			output_hidden_states=_a			)
			torch.save(model.state_dict()       ,			model_name + "local.pt"			)
			return model




def 		UpperCAmelCase      (  _lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase			):
			A							:    Optional[Any]								=  copy.deepcopy(_a			)
			model.to(_a			)

			A							:    Tuple								=  ["bias", "LayerNorm.weight"]
			A							:    List[str]								=  [
			    {
			        "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							:    Any								=  AdamW(_a       ,			lr=5e-5       ,			eps=1e-8			)
			A							:    Optional[int]								=  get_linear_schedule_with_warmup(_a       ,			0       ,			_a			)
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler




def 		UpperCAmelCase      (  _lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase			):
			A							:    List[Any]								=  np.array(_a			).mean()
			A							:    str								=  np.array(_a			).std()
			A							:    int								=  (real_perps - avg) / std
			A							:    Dict								=  list(zip(_a       ,			_a       ,			_a			)			)
			joblib.dump(_a       ,			_a			)




def 		UpperCAmelCase      (  _lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase="dev.jbl"       ,			_lowerCamelCase=recopy_gpta       ,			):
			A							:    Tuple								=  []
			A							:    Dict								=  []
			A							:    List[str]								=  []

			# Initialize the transformer model
			A							:    List[str]								=  copy.deepcopy(_a			)
			orig_model.to(device="cpu"			)
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			A							:    List[str]								=  recopy_model(_a       ,			_a       ,			_a			)

			for step in tqdm(range(_a			)			):
						A							:    Dict								=  torch.zeros((1, context_len)       ,			dtype=torch.long       ,			device=_a			)
						A							:    List[Any]								=  random.choice(_a			)
						A							:    Union[str, Any]								=  random.randint(0       ,			len(story[0]			) - context_len - 1			)
						A							:    int								=  story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						A							:    List[Any]								=  model(_a       ,			labels=_a			)
						A							:    List[Any]								=  outputs[0]
						A							:    Tuple								=  compute_perplexity(_a       ,			_a       ,			_a			)
						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							:    Any								=  compute_perplexity(_a       ,			_a       ,			_a			)

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

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						A							:    Optional[Any]								=  recopy_model(_a       ,			_a       ,			_a			)

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

			intermittent_save(_a       ,			_a       ,			_a       ,			_a			)




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

			joblib.dump(_a       ,			"objective_set.jbl"			)
			print("objective set saved"			)
			return train_data, objective_set




def 		UpperCAmelCase      (  _lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase       ,			_lowerCamelCase=50       ,			_lowerCamelCase="secondary_learner.pt"			):
			A							:    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
			A							:    str								=  train_dataset[:512]
			A							:    Union[str, Any]								=  train_dataset[512:]
			A							:    Union[str, Any]								=  DataLoader(_a       ,			shuffle=_a       ,			batch_size=_a			)
			A							:    Any								=  DataLoader(_a       ,			shuffle=_a       ,			batch_size=_a			)

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

												secondary_learner.eval()
												A							:    str								=  0.0
												A							:    Tuple								=  0.0
												A							:    Dict								=  []
												A							:    List[Any]								=  []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(_a			):
															A							:    List[Any]								=  batcha[0].to(_a			)
															A							:    Optional[int]								=  batcha[1].to(_a			)
															A							:    Optional[Any]								=  secondary_learner(_a			)
															q_lossa += test_loss(_a       ,			_a			).item()
															sum_qa += torch.sum(_a			).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(_a			)
												print(
												    "Epoch: "       ,			_a       ,			"step: "       ,			_a       ,			"Avg. q:"       ,			sum_qa / len(_a			)       ,			"Train Loss: "       ,			_a       ,			"Test Loss: "       ,			_a       ,			)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)       ,			"pred_vs_actual.jbl"			)
															torch.save(secondary_learner.state_dict()       ,			_a			)
															A							:    List[Any]								=  q_lossa

									secondary_learner.train()
			return secondary_learner






class 		lowerCamelCase_			(       nn.Module  ):






						'''simple docstring'''







						def __init__(		self					:	List[str]		,		__lowerCamelCase					:	Tuple       )					-> Any:
									super(_SCREAMING_SNAKE_CASE		,		self       ).__init__()
									A							:    int								=  model.transformer.wte
									A							:    Tuple								=  copy.deepcopy(model.transformer.wte.weight       )
									A							:    List[Any]								=  nn.Convad(self.embeddings.weight.size(1       )		,		2_56		,		3		,		padding=1       )
									A							:    Dict								=  nn.Sequential(nn.Linear(2_56		,		32       )		,		nn.Dropout(p=0.1       )		,		nn.Linear(32		,		32       )		,		nn.Linear(32		,		1       )       )







						def 	SCREAMING_SNAKE_CASE__						(		self					:	Tuple		,		__lowerCamelCase					:	Union[str, Any]       )					-> Optional[int]:
									A							:    List[Any]								=  torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE       ).squeeze(1       ).transpose(1		,		2       )       )		,		2       )[0]
									A							:    Optional[int]								=  self.fc(_SCREAMING_SNAKE_CASE       )
									return qs.squeeze(1       )







						@classmethod
						def 	SCREAMING_SNAKE_CASE__						(		cls					:	Optional[int]		,		__lowerCamelCase					:	Dict		,		__lowerCamelCase					:	List[Any]       )					-> Tuple:
									A							:    Optional[int]								=  cls(_SCREAMING_SNAKE_CASE       )  # this calls __init__
									A							:    int								=  torch.load(_SCREAMING_SNAKE_CASE       )
									secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE       )
									A							:    Optional[int]								=  model.transformer.wte
									A							:    List[Any]								=  copy.deepcopy(model.transformer.wte.weight       )
									return secondary_learner







def    lowerCAmelCase__		(			_a				:	str     ,					_a				:	int = 13			):
		snake_case_ :							List[Any]         =				""
		for c in s:
				if "A" <= c <= "Z":
						out += chr(ord("A"			) + (ord(_a			) - ord("A"			) + n) % 26			)
				elif "a" <= c <= "z":
						out += chr(ord("a"			) + (ord(_a			) - ord("a"			) + n) % 26			)
				else:
						out += c
		return out

def    lowerCAmelCase__		(			):
		snake_case_ :							int         =				input("Enter message: "			)

		snake_case_ :							List[Any]         =				dencrypt(_a     ,					13			)
		print("Encryption:"     ,					_a			)

		snake_case_ :							int         =				dencrypt(_a     ,					13			)
		print("Decryption: "     ,					_a			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()
