




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


UpperCamelCase__              =						logging.getLogger(__name__)






def 					snake_case					(							A			)							->							Optional[int]:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A						,   A			)							->							Optional[int]:




	'''simple docstring'''


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






def 					snake_case					(							A="gpt2"			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							GPTaLMHeadModel.from_pretrained(A						,   output_hidden_states=A			)
	torch.save(model.state_dict()						,   model_name + 'local.pt'			)
	return model






def 					snake_case					(							A						,   A						,   A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							copy.deepcopy(A			)
	model.to(A			)

	lowerCamelCase_							:		int							   =							['bias', 'LayerNorm.weight']
	lowerCamelCase_							:		Optional[Any]							   =							[
	    {
	        '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_							:		Dict							   =							AdamW(A						,   lr=5e-5						,   eps=1e-8			)
	lowerCamelCase_							:		Dict							   =							get_linear_schedule_with_warmup(A						,   0						,   A			)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler






def 					snake_case					(							A						,   A						,   A						,   A			)							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							np.array(A			).mean()
	lowerCamelCase_							:		List[Any]							   =							np.array(A			).std()
	lowerCamelCase_							:		Optional[int]							   =							(real_perps - avg) / std
	lowerCamelCase_							:		int							   =							list(zip(A						,   A						,   A			)			)
	joblib.dump(A						,   A			)






def 					snake_case					(							A						,   A						,   A						,   A						,   A						,   A						,   A						,   A="dev.jbl"						,   A=recopy_gpta						,   )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							   =							[]
	lowerCamelCase_							:		Optional[Any]							   =							[]
	lowerCamelCase_							:		str							   =							[]

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

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

	for step in tqdm(range(A			)			):
		lowerCamelCase_							:		Tuple							   =							torch.zeros((1, context_len)						,   dtype=torch.long						,   device=A			)
		lowerCamelCase_							:		Optional[int]							   =							random.choice(A			)
		lowerCamelCase_							:		List[str]							   =							random.randint(0						,   len(story[0]			) - context_len - 1			)
		lowerCamelCase_							:		Any							   =							story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		lowerCamelCase_							:		Optional[Any]							   =							model(A						,   labels=A			)
		lowerCamelCase_							:		List[Any]							   =							outputs[0]
		lowerCamelCase_							:		Optional[Any]							   =							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
		lowerCamelCase_							:		int							   =							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
		lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Dict							   =							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 					snake_case					(							A						,   A="data/tokenized_stories_train_wikitext103.jbl"						,   A=100						,   A=1026						,   A=True			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[int]							   =							joblib.load(A			)
	print('data loaded'			)
	lowerCamelCase_							:		Tuple							   =							[]
	if trim:
		for i, example in enumerate(A			):
			if len(example[0]			) > min_len:
				lowerCamelCase_							:		List[Any]							   =							random.randint(0						,   len(example[0]			) - context_len - 1			)
				objective_set.append(example[0, start : start + context_len]			)
			if len(A			) >= number:
				break
		lowerCamelCase_							:		Any							   =							[]
		for j in range(i + 1						,   len(A			)			):
			if len(data[j][0]			) > min_len:
				train_data.append(data[j]			)
	else:
		lowerCamelCase_							:		Tuple							   =							data[0:number]
		lowerCamelCase_							:		Union[str, Any]							   =							data[number:]

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






def 					snake_case					(							A						,   A						,   A						,   A						,   A=50						,   A="secondary_learner.pt"			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[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_							:		Any							   =							train_dataset[:512]
	lowerCamelCase_							:		Optional[Any]							   =							train_dataset[512:]
	lowerCamelCase_							:		int							   =							DataLoader(A						,   shuffle=A						,   batch_size=A			)
	lowerCamelCase_							:		int							   =							DataLoader(A						,   shuffle=A						,   batch_size=A			)

	# secondary learner model set up
	lowerCamelCase_							:		Tuple							   =							nn.MSELoss()
	lowerCamelCase_							:		List[Any]							   =							nn.MSELoss(reduction='sum'			)
	secondary_learner.to(A			)
	lowerCamelCase_							:		str							   =							torch.optim.Adam(secondary_learner.parameters()						,   lr=0.0_0001			)
	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_							:		str							   =							float('inf'			)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(A			)			):
		lowerCamelCase_							:		Tuple							   =							0.0
		secondary_learner.train()
		for step, batch in enumerate(A			):
			lowerCamelCase_							:		Tuple							   =							batch[0].to(A			)
			lowerCamelCase_							:		List[Any]							   =							batch[1].to(A			)
			lowerCamelCase_							:		int							   =							secondary_learner(A			)
			q_optimizer.zero_grad()
			lowerCamelCase_							:		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			)):
				lowerCamelCase_							:		Optional[Any]							   =							tr_q_loss / (step + 1)

				secondary_learner.eval()
				lowerCamelCase_							:		str							   =							0.0
				lowerCamelCase_							:		Tuple							   =							0.0
				lowerCamelCase_							:		Optional[int]							   =							[]
				lowerCamelCase_							:		Dict							   =							[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(A			):
					lowerCamelCase_							:		Dict							   =							batcha[0].to(A			)
					lowerCamelCase_							:		str							   =							batcha[1].to(A			)
					lowerCamelCase_							:		str							   =							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			)
					lowerCamelCase_							:		Union[str, Any]							   =							q_lossa

			secondary_learner.train()
	return secondary_learner



class 				__SCREAMING_SNAKE_CASE							(		nn.Module  ):

					def __init__(				self ,       __lowercase					):







						'''simple docstring'''




						super(__lowercase ,       self					).__init__()
						lowerCamelCase_							:		Optional[Any]							   =							model.transformer.wte
						lowerCamelCase_							:		List[Any]							   =							copy.deepcopy(model.transformer.wte.weight					)
						lowerCamelCase_							:		Tuple							   =							nn.Convad(self.embeddings.weight.size(1					) ,       256 ,       3 ,       padding=1					)
						lowerCamelCase_							:		int							   =							nn.Sequential(nn.Linear(256 ,       32					) ,       nn.Dropout(p=0.1					) ,       nn.Linear(32 ,       32					) ,       nn.Linear(32 ,       1					)					)

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							torch.max(self.conv(self.embeddings(__lowercase					).squeeze(1					).transpose(1 ,       2					)					) ,       2					)[0]
						lowerCamelCase_							:		Optional[int]							   =							self.fc(__lowercase					)
						return qs.squeeze(1					)





					@classmethod
					def 	lowercase_							(				cls ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							cls(__lowercase					)  # this calls __init__
						lowerCamelCase_							:		Union[str, Any]							   =							torch.load(__lowercase					)
						secondary_learner.load_state_dict(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							model.transformer.wte
						lowerCamelCase_							:		List[str]							   =							copy.deepcopy(model.transformer.wte.weight					)
						return secondary_learner






import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=24 ,       __lowercase=32 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=16 ,       __lowercase=2 ,       __lowercase=0.02 ,       __lowercase=4 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							ElectraConfig(
						    vocab_size=self.vocab_size ,       hidden_size=self.hidden_size ,       embedding_size=self.embedding_size ,       num_hidden_layers=self.num_hidden_layers ,       num_attention_heads=self.num_attention_heads ,       intermediate_size=self.intermediate_size ,       hidden_act=self.hidden_act ,       hidden_dropout_prob=self.hidden_dropout_prob ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       initializer_range=self.initializer_range ,       )

						return config, input_ids, token_type_ids, attention_mask





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

					_lowerCamelCase		  = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
