



'''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


lowerCAmelCase						:		Tuple						      = logging.getLogger(__name__)



def 	__lowerCAmelCase       (				lowerCamelCase :    Union[str, Any]       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    int					,		lowerCamelCase :    str					,		lowerCamelCase :    Any       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    Optional[Any]="gpt2"       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    Any					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    Any       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	copy.deepcopy(lowerCamelCase       )
	model.to(lowerCamelCase       )

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



def 	__lowerCAmelCase       (				lowerCamelCase :    Any					,		lowerCamelCase :    Union[str, Any]					,		lowerCamelCase :    Optional[int]					,		lowerCamelCase :    Any       ):






	'''simple docstring'''




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



def 	__lowerCAmelCase       (				lowerCamelCase :    Any					,		lowerCamelCase :    Union[str, Any]					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    Any="dev.jbl"					,		lowerCamelCase :    List[str]=recopy_gpta					,		):






	'''simple docstring'''




	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	[]
	__lowerCAmelCase       				=	[]

	# Initialize the transformer model
	__lowerCAmelCase       				=	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       				=	recopy_model(lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )

	for step in tqdm(range(lowerCamelCase       )       ):
		__lowerCAmelCase       				=	torch.zeros((1, context_len)					,		dtype=torch.long					,		device=lowerCamelCase       )
		__lowerCAmelCase       				=	random.choice(lowerCamelCase       )
		__lowerCAmelCase       				=	random.randint(0					,		len(story[0]       ) - context_len - 1       )
		__lowerCAmelCase       				=	story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		__lowerCAmelCase       				=	model(lowerCamelCase					,		labels=lowerCamelCase       )
		__lowerCAmelCase       				=	outputs[0]
		__lowerCAmelCase       				=	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       				=	compute_perplexity(lowerCamelCase					,		lowerCamelCase					,		lowerCamelCase       )

		# Periodically save the stored (X, IG(X)) pairs
		if step % 10_00 == 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       				=	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 :    Union[str, Any]					,		lowerCamelCase :    Dict="data/tokenized_stories_train_wikitext103.jbl"					,		lowerCamelCase :    Dict=1_00					,		lowerCamelCase :    Tuple=10_26					,		lowerCamelCase :    List[str]=True       ):






	'''simple docstring'''




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

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



def 	__lowerCAmelCase       (				lowerCamelCase :    Optional[Any]					,		lowerCamelCase :    List[str]					,		lowerCamelCase :    List[Any]					,		lowerCamelCase :    int					,		lowerCamelCase :    Optional[int]=50					,		lowerCamelCase :    List[str]="secondary_learner.pt"       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	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       				=	train_dataset[:5_12]
	__lowerCAmelCase       				=	train_dataset[5_12:]
	__lowerCAmelCase       				=	DataLoader(lowerCamelCase					,		shuffle=lowerCamelCase					,		batch_size=lowerCamelCase       )
	__lowerCAmelCase       				=	DataLoader(lowerCamelCase					,		shuffle=lowerCamelCase					,		batch_size=lowerCamelCase       )

	# secondary learner model set up
	__lowerCAmelCase       				=	nn.MSELoss()
	__lowerCAmelCase       				=	nn.MSELoss(reduction="sum"       )
	secondary_learner.to(lowerCamelCase       )
	__lowerCAmelCase       				=	torch.optim.Adam(secondary_learner.parameters()					,		lr=0.0_0_0_0_1       )
	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       				=	float("inf"       )
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(lowerCamelCase       )       ):
		__lowerCAmelCase       				=	0.0
		secondary_learner.train()
		for step, batch in enumerate(lowerCamelCase       ):
			__lowerCAmelCase       				=	batch[0].to(lowerCamelCase       )
			__lowerCAmelCase       				=	batch[1].to(lowerCamelCase       )
			__lowerCAmelCase       				=	secondary_learner(lowerCamelCase       )
			q_optimizer.zero_grad()
			__lowerCAmelCase       				=	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       				=	tr_q_loss / (step + 1)

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

			secondary_learner.train()
	return secondary_learner






class        UpperCAmelCase__						(		nn.Module			):



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



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      int:
						__lowerCAmelCase       				=	torch.max(self.conv(self.embeddings(UpperCamelCase  ).squeeze(1  ).transpose(1      ,					2  )  )      ,					2  )[0]
						__lowerCAmelCase       				=	self.fc(UpperCamelCase  )
						return qs.squeeze(1  )



					@classmethod
					def 							UpperCAmelCase_			(     cls      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      List[Any]:
						__lowerCAmelCase       				=	cls(UpperCamelCase  )  # this calls __init__
						__lowerCAmelCase       				=	torch.load(UpperCamelCase  )
						secondary_learner.load_state_dict(UpperCamelCase  )
						__lowerCAmelCase       				=	model.transformer.wte
						__lowerCAmelCase       				=	copy.deepcopy(model.transformer.wte.weight  )
						return secondary_learner




'''simple docstring'''



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
	from PIL import Image






class        UpperCAmelCase__						(		unittest.TestCase				,							UpperCamelCase__			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	load_tool("image-question-answering"  )
						self.tool.setup()
						__lowerCAmelCase       				=	load_tool("image-question-answering"      ,					remote=UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[int]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(UpperCamelCase      ,					"How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict:
						__lowerCAmelCase       				=	Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  )
						__lowerCAmelCase       				=	self.remote_tool(image=UpperCamelCase      ,					question="How many cats are sleeping on the couch?"  )
						self.assertEqual(UpperCamelCase      ,					"2"  )