




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__    :					Dict					=						logging.getLogger(__name__)


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	random.seed(_A							)
	np.random.seed(_A							)
	torch.manual_seed(_A							)
	torch.cuda.manual_seed_all(_A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		_A="gpt2"							):







	'''simple docstring'''





	snake_case_		   =					GPTaLMHeadModel.from_pretrained(_A					,     output_hidden_states=_A							)
	torch.save(model.state_dict()					,     model_name + "local.pt"							)
	return model


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					copy.deepcopy(_A							)
	model.to(_A							)

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


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					np.array(_A							).mean()
	snake_case_		   =					np.array(_A							).std()
	snake_case_		   =					(real_perps - avg) / std
	snake_case_		   =					list(zip(_A					,     _A					,     _A							)							)
	joblib.dump(_A					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A					,     _A					,     _A					,     _A					,     _A="dev.jbl"					,     _A=recopy_gpta					,     ):







	'''simple docstring'''





	snake_case_		   =					[]
	snake_case_		   =					[]
	snake_case_		   =					[]

	# Initialize the transformer model
	snake_case_		   =					copy.deepcopy(_A							)
	orig_model.to(device="cpu"							)
	torch.cuda.empty_cache()

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

	for step in tqdm(range(_A							)							):
		snake_case_		   =					torch.zeros((1, context_len)					,     dtype=torch.long					,     device=_A							)
		snake_case_		   =					random.choice(_A							)
		snake_case_		   =					random.randint(0					,     len(story[0]							) - context_len - 1							)
		snake_case_		   =					story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		snake_case_		   =					model(_A					,     labels=_A							)
		snake_case_		   =					outputs[0]
		snake_case_		   =					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
		snake_case_		   =					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
		snake_case_   ,				snake_case_   ,				snake_case_		   =					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 						lowerCamelCase__   (		_A					,     _A="data/tokenized_stories_train_wikitext103.jbl"					,     _A=100					,     _A=1026					,     _A=True							):







	'''simple docstring'''





	snake_case_		   =					joblib.load(_A							)
	print("data loaded"							)
	snake_case_		   =					[]
	if trim:
		for i, example in enumerate(_A							):
			if len(example[0]							) > min_len:
				snake_case_		   =					random.randint(0					,     len(example[0]							) - context_len - 1							)
				objective_set.append(example[0, start : start + context_len]							)
			if len(_A							) >= number:
				break
		snake_case_		   =					[]
		for j in range(i + 1					,     len(_A							)							):
			if len(data[j][0]							) > min_len:
				train_data.append(data[j]							)
	else:
		snake_case_		   =					data[0:number]
		snake_case_		   =					data[number:]

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


def 						lowerCamelCase__   (		_A					,     _A					,     _A					,     _A					,     _A=50					,     _A="secondary_learner.pt"							):







	'''simple docstring'''





	snake_case_		   =					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
	snake_case_		   =					train_dataset[:512]
	snake_case_		   =					train_dataset[512:]
	snake_case_		   =					DataLoader(_A					,     shuffle=_A					,     batch_size=_A							)
	snake_case_		   =					DataLoader(_A					,     shuffle=_A					,     batch_size=_A							)

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

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

			secondary_learner.train()
	return secondary_learner






class 		UpperCAmelCase       (    nn.Module      ):







						'''simple docstring'''





						def __init__(						self							:					Tuple	,     __lowercase							:					int						):






							"""simple docstring"""






							super(__lowercase	,     self						).__init__()
							snake_case_		   =					model.transformer.wte
							snake_case_		   =					copy.deepcopy(model.transformer.wte.weight						)
							snake_case_		   =					nn.Convad(self.embeddings.weight.size(1						)	,     2_56	,     3	,     padding=1						)
							snake_case_		   =					nn.Sequential(nn.Linear(2_56	,     32						)	,     nn.Dropout(p=0.1						)	,     nn.Linear(32	,     32						)	,     nn.Linear(32	,     1						)						)






						def 	snake_case__						(						self							:					int	,     __lowercase							:					List[Any]						):






							"""simple docstring"""






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






						@classmethod
						def 	snake_case__						(						cls							:					Tuple	,     __lowercase							:					Any	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






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






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.OneCycleLR(_A					,     max_lr=0.01					,     steps_per_epoch=2					,     epochs=1							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	scheduler.step()
	if step_scheduler_with_optimizer or (num_processes == 1):
		assert (
		    scheduler.scheduler.last_epoch == num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
	else:
		assert (
		    scheduler.scheduler.last_epoch != num_processes
		), f"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"


def 						lowerCamelCase__   (		_A=2					,     _A=True					,     _A=False							):







	'''simple docstring'''





	snake_case_		   =					Accelerator(step_scheduler_with_optimizer=_A					,     split_batches=_A							)
	snake_case_		   =					torch.nn.Linear(2					,     4							)
	snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=1.0							)
	snake_case_		   =					torch.optim.lr_scheduler.LambdaLR(_A					,     lr_lambda=lambda _A							: 1 - n / 10							)
	snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

	# Optimizer has stepped
	snake_case_		   =					False
	scheduler.step()
	snake_case_		   =					1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

	# Optimizer has not stepped
	snake_case_		   =					True
	scheduler.step()
	if not step_scheduler_with_optimizer:
		snake_case_		   =					1 - 2 / 10
	assert (
	    scheduler.get_last_lr()[0] == expected_lr
	), f"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"


def 						lowerCamelCase__   (		_A = 2							):







	'''simple docstring'''





	from transformers import get_linear_schedule_with_warmup

	snake_case_		   =					[1, 2, 4]
	for num_steps in steps:
		snake_case_		   =					GradientAccumulationPlugin(num_steps=_A					,     adjust_scheduler=num_steps > 1							)
		snake_case_		   =					Accelerator(gradient_accumulation_plugin=_A							)
		snake_case_		   =					torch.nn.Linear(2					,     4							)
		snake_case_		   =					torch.optim.AdamW(model.parameters()					,     lr=10.0							)
		snake_case_		   =					get_linear_schedule_with_warmup(optimizer=_A					,     num_warmup_steps=0					,     num_training_steps=20							)

		snake_case_   ,				snake_case_   ,				snake_case_		   =					accelerator.prepare(_A					,     _A					,     _A							)

		for i in range(10 * num_steps							):
			with accelerator.accumulate(_A							):
				optimizer.step()
				scheduler.step()

			if i == (10 * num_steps - 2):
				assert (
				    scheduler.get_last_lr()[0] != 0
				), f"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
		assert (
		    scheduler.get_last_lr()[0] == 0
		), f"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
		GradientState._reset_state()






@require_cpu
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					int						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1						)	,     num_processes=1						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							debug_launcher(partial(__lowercase	,     num_processes=1	,     step_scheduler_with_optimizer=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1	,     split_batches=__lowercase						)	,     num_processes=1						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     step_scheduler_with_optimizer=__lowercase						)						)






						@require_huggingface_suite
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							AcceleratorState._reset_state(__lowercase						)
							debug_launcher(partial(__lowercase	,     num_processes=1						)						)
							debug_launcher(__lowercase						)
