

"""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


__UpperCAmelCase	=			logging.getLogger(__name__)
def 					lowerCAmelCase  (   __UpperCamelCase ):
	'''simple docstring'''





	random.seed(SCREAMING_SNAKE_CASE_ )
	np.random.seed(SCREAMING_SNAKE_CASE_ )
	torch.manual_seed(SCREAMING_SNAKE_CASE_ )
	torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_ )
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
	'''simple docstring'''





	model.eval()
	UpperCAmelCase__   :					Any   				=   next(model.parameters() ).device
	UpperCAmelCase__   :					int   				=   1
	UpperCAmelCase__   :					Optional[int]   				=   torch.zeros((eval_batch_size, context_len)							,      dtype=torch.long							,      device=SCREAMING_SNAKE_CASE_ )
	UpperCAmelCase__   :					Optional[Any]   				=   DataLoader(SCREAMING_SNAKE_CASE_							,      shuffle=SCREAMING_SNAKE_CASE_							,      batch_size=SCREAMING_SNAKE_CASE_ )
	UpperCAmelCase__   :					Tuple   				=   torch.zeros(1							,      device=SCREAMING_SNAKE_CASE_ )
	UpperCAmelCase__   :					Tuple   				=   0
	for batch in eval_dataloader:
		batch.to(SCREAMING_SNAKE_CASE_ )
		# pad
		context.zero_()
		for i in range(SCREAMING_SNAKE_CASE_ ):
			UpperCAmelCase__   :					Any   				=   batch[i]
		UpperCAmelCase__   :					Union[str, Any]   				=   model(SCREAMING_SNAKE_CASE_							,      labels=SCREAMING_SNAKE_CASE_ )
		eval_loss += outputs[0].sum().item()
		nb_eval_examples += batch.size(0 )
	UpperCAmelCase__   :					Dict   				=   eval_loss / nb_eval_examples
	UpperCAmelCase__   :					Union[str, Any]   				=   torch.exp(SCREAMING_SNAKE_CASE_ )
	model.train()
	return perplexity
def 					lowerCAmelCase  (   __UpperCamelCase="gpt2" ):
	'''simple docstring'''





	UpperCAmelCase__   :					List[Any]   				=   GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_							,      output_hidden_states=SCREAMING_SNAKE_CASE_ )
	torch.save(model.state_dict()							,      model_name + """local.pt""" )
	return model
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
	'''simple docstring'''





	UpperCAmelCase__   :					List[Any]   				=   copy.deepcopy(SCREAMING_SNAKE_CASE_ )
	model.to(SCREAMING_SNAKE_CASE_ )

	UpperCAmelCase__   :					int   				=   ["bias", "LayerNorm.weight"]
	UpperCAmelCase__   :					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},
	]
	UpperCAmelCase__   :					Any   				=   AdamW(SCREAMING_SNAKE_CASE_							,      lr=5e-5							,      eps=1e-8 )
	UpperCAmelCase__   :					List[str]   				=   get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE_							,      0							,      SCREAMING_SNAKE_CASE_ )
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase ):
	'''simple docstring'''





	UpperCAmelCase__   :					List[str]   				=   np.array(SCREAMING_SNAKE_CASE_ ).mean()
	UpperCAmelCase__   :					Tuple   				=   np.array(SCREAMING_SNAKE_CASE_ ).std()
	UpperCAmelCase__   :					Tuple   				=   (real_perps - avg) / std
	UpperCAmelCase__   :					int   				=   list(zip(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_ ) )
	joblib.dump(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_ )
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase="dev.jbl"							,      __UpperCamelCase=recopy_gpta							,      ):
	'''simple docstring'''





	UpperCAmelCase__   :					Union[str, Any]   				=   []
	UpperCAmelCase__   :					Optional[int]   				=   []
	UpperCAmelCase__   :					List[Any]   				=   []

	# Initialize the transformer model
	UpperCAmelCase__   :					List[Any]   				=   copy.deepcopy(SCREAMING_SNAKE_CASE_ )
	orig_model.to(device="""cpu""" )
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	UpperCAmelCase__   :					Optional[int]   				=   recopy_model(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_ )

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

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

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		UpperCAmelCase__   :					Optional[int]   				=   recopy_model(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_ )

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

	intermittent_save(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_ )
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"							,      __UpperCamelCase=100							,      __UpperCamelCase=1026							,      __UpperCamelCase=True ):
	'''simple docstring'''





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

	joblib.dump(SCREAMING_SNAKE_CASE_							,      """objective_set.jbl""" )
	print("""objective set saved""" )
	return train_data, objective_set
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase=50							,      __UpperCamelCase="secondary_learner.pt" ):
	'''simple docstring'''





	UpperCAmelCase__   :					int   				=   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
	UpperCAmelCase__   :					Any   				=   train_dataset[:512]
	UpperCAmelCase__   :					int   				=   train_dataset[512:]
	UpperCAmelCase__   :					Optional[Any]   				=   DataLoader(SCREAMING_SNAKE_CASE_							,      shuffle=SCREAMING_SNAKE_CASE_							,      batch_size=SCREAMING_SNAKE_CASE_ )
	UpperCAmelCase__   :					Any   				=   DataLoader(SCREAMING_SNAKE_CASE_							,      shuffle=SCREAMING_SNAKE_CASE_							,      batch_size=SCREAMING_SNAKE_CASE_ )

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

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

			secondary_learner.train()
	return secondary_learner







class        __lowercase     (      nn.Module   ):

		def __init__(					self						:     int   ,A						:     Optional[Any]      ):






			'''simple docstring'''
			super(A   ,self      ).__init__()
			UpperCAmelCase__   :					Any   				=   model.transformer.wte
			UpperCAmelCase__   :					str   				=   copy.deepcopy(model.transformer.wte.weight      )
			UpperCAmelCase__   :					Dict   				=   nn.Convad(self.embeddings.weight.size(1      )   ,256   ,3   ,padding=1      )
			UpperCAmelCase__   :					Optional[int]   				=   nn.Sequential(nn.Linear(256   ,32      )   ,nn.Dropout(p=0.1      )   ,nn.Linear(32   ,32      )   ,nn.Linear(32   ,1      )      )

		def 				__lowercase    (					self						:     Dict   ,A						:     Dict      ):






			'''simple docstring'''
			UpperCAmelCase__   :					Optional[Any]   				=   torch.max(self.conv(self.embeddings(A      ).squeeze(1      ).transpose(1   ,2      )      )   ,2      )[0]
			UpperCAmelCase__   :					Dict   				=   self.fc(A      )
			return qs.squeeze(1      )




		@classmethod
		def 				__lowercase    (					cls						:     Tuple   ,A						:     str   ,A						:     Any      ):






			'''simple docstring'''
			UpperCAmelCase__   :					List[Any]   				=   cls(A      )  # this calls __init__
			UpperCAmelCase__   :					int   				=   torch.load(A      )
			secondary_learner.load_state_dict(A      )
			UpperCAmelCase__   :					List[str]   				=   model.transformer.wte
			UpperCAmelCase__   :					Union[str, Any]   				=   copy.deepcopy(model.transformer.wte.weight      )
			return secondary_learner








"""simple docstring"""




def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   1
 UpperCAmelCase__   :					Union[str, Any]   				=   0
 UpperCAmelCase__   :					List[str]   				=   1
 UpperCAmelCase__   :					Any   				=   1
 UpperCAmelCase__   :					Optional[int]   				=   3
 UpperCAmelCase__   :					Any   				=   3
 UpperCAmelCase__   :					Optional[Any]   				=   limit
 UpperCAmelCase__   :					Dict   				=   0

 UpperCAmelCase__   :					int   				=   """"""
 while counter != decimal + 1:
  if 4 * q + r - t < n * t:
   result += str(__UpperCamelCase )
   if counter == 0:
    result += "."

   if decimal == counter:
    break

   counter += 1
   UpperCAmelCase__   :					Union[str, Any]   				=   10 * (r - n * t)
   UpperCAmelCase__   :					int   				=   ((10 * (3 * q + r)) // t) - 10 * n
   q *= 10
   UpperCAmelCase__   :					str   				=   nr
  else:
   UpperCAmelCase__   :					List[str]   				=   (2 * q + r) * l
   UpperCAmelCase__   :					Union[str, Any]   				=   (q * (7 * k) + 2 + (r * l)) // (t * l)
   q *= k
   t *= l
   l += 2
   k += 1
   UpperCAmelCase__   :					Optional[Any]   				=   nn
   UpperCAmelCase__   :					str   				=   nr
 return result
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 print(F"{calculate_pi(50 ) = }" )
 import doctest

 doctest.testmod()


if __name__ == "__main__":
      main()





