





'''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


snake_case_  :							List[Any]            =	logging.getLogger(__name__)





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple="gpt2" )			->						Any:
						UpperCAmelCase_      :   List[str]      =	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_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						List[Any]:
						UpperCAmelCase_      :   Optional[Any]      =	copy.deepcopy(SCREAMING_SNAKE_CASE__ )
						model.to(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   List[str]      =	['''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_      :   Optional[int]      =	AdamW(SCREAMING_SNAKE_CASE__,      lr=5E-5,      eps=1E-8 )
						UpperCAmelCase_      :   Dict      =	get_linear_schedule_with_warmup(SCREAMING_SNAKE_CASE__,      0,      SCREAMING_SNAKE_CASE__ )
						torch.cuda.empty_cache()
						return model, lm_optimizer, lm_scheduler





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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      List[Any]="dev.jbl",      SCREAMING_SNAKE_CASE__   :      Optional[Any]=recopy_gpta,      )			->						List[Any]:
						UpperCAmelCase_      :   Union[str, Any]      =	[]
						UpperCAmelCase_      :   List[str]      =	[]
						UpperCAmelCase_      :   Dict      =	[]

						# Initialize the transformer model
						UpperCAmelCase_      :   Optional[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_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	recopy_model(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						for step in tqdm(range(SCREAMING_SNAKE_CASE__ ) ):
												UpperCAmelCase_      :   List[Any]      =	torch.zeros((1, context_len),      dtype=torch.long,      device=SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Optional[int]      =	random.choice(SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   Union[str, Any]      =	random.randint(0,      len(story[0] ) - context_len - 1 )
												UpperCAmelCase_      :   Optional[Any]      =	story[0][start : start + context_len]
												lm_optimizer.zero_grad()
												UpperCAmelCase_      :   Union[str, Any]      =	model(SCREAMING_SNAKE_CASE__,      labels=SCREAMING_SNAKE_CASE__ )
												UpperCAmelCase_      :   int      =	outputs[0]
												UpperCAmelCase_      :   Optional[int]      =	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_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   str      =	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_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      int="data/tokenized_stories_train_wikitext103.jbl",      SCREAMING_SNAKE_CASE__   :      Dict=100,      SCREAMING_SNAKE_CASE__   :      List[Any]=1026,      SCREAMING_SNAKE_CASE__   :      List[Any]=True )			->						str:
						UpperCAmelCase_      :   List[Any]      =	joblib.load(SCREAMING_SNAKE_CASE__ )
						print('''data loaded''' )
						UpperCAmelCase_      :   Tuple      =	[]
						if trim:
												for i, example in enumerate(SCREAMING_SNAKE_CASE__ ):
																		if len(example[0] ) > min_len:
																								UpperCAmelCase_      :   int      =	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_      :   Tuple      =	data[number:]

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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      int=50,      SCREAMING_SNAKE_CASE__   :      int="secondary_learner.pt" )			->						Tuple:
						UpperCAmelCase_      :   Union[str, 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
						UpperCAmelCase_      :   List[str]      =	train_dataset[:512]
						UpperCAmelCase_      :   List[str]      =	train_dataset[512:]
						UpperCAmelCase_      :   Tuple      =	DataLoader(SCREAMING_SNAKE_CASE__,      shuffle=SCREAMING_SNAKE_CASE__,      batch_size=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   List[str]      =	DataLoader(SCREAMING_SNAKE_CASE__,      shuffle=SCREAMING_SNAKE_CASE__,      batch_size=SCREAMING_SNAKE_CASE__ )

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

																								secondary_learner.eval()
																								UpperCAmelCase_      :   str      =	0.0
																								UpperCAmelCase_      :   Optional[Any]      =	0.0
																								UpperCAmelCase_      :   List[Any]      =	[]
																								UpperCAmelCase_      :   Optional[Any]      =	[]
																								# Compute performance of the secondary learner after this batch
																								for stepa, batcha in enumerate(SCREAMING_SNAKE_CASE__ ):
																														UpperCAmelCase_      :   Union[str, Any]      =	batcha[0].to(SCREAMING_SNAKE_CASE__ )
																														UpperCAmelCase_      :   List[str]      =	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_      :   str      =	q_lossa

																		secondary_learner.train()
						return secondary_learner


class        __a   (nn.Module  ):

							def __init__(				self				:  Tuple							,  __magic_name__				:  List[str] )       ->							Union[str, Any]:







													"""simple docstring"""
													super(__magic_name__							,  self ).__init__()
													UpperCAmelCase_      :   Optional[Any]      =	model.transformer.wte
													UpperCAmelCase_      :   int      =	copy.deepcopy(model.transformer.wte.weight )
													UpperCAmelCase_      :   int      =	nn.Convad(self.embeddings.weight.size(1 )							,  2_56							,  3							,  padding=1 )
													UpperCAmelCase_      :   int      =	nn.Sequential(nn.Linear(2_56							,  32 )							,  nn.Dropout(p=0.1 )							,  nn.Linear(32							,  32 )							,  nn.Linear(32							,  1 ) )

							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  Union[str, Any] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	torch.max(self.conv(self.embeddings(__magic_name__ ).squeeze(1 ).transpose(1							,  2 ) )							,  2 )[0]
													UpperCAmelCase_      :   List[Any]      =	self.fc(__magic_name__ )
													return qs.squeeze(1 )



							@classmethod
							def      UpperCAmelCase__						(				cls				:  Any							,  __magic_name__				:  int							,  __magic_name__				:  Dict )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	cls(__magic_name__ )  # this calls __init__
													UpperCAmelCase_      :   List[Any]      =	torch.load(__magic_name__ )
													secondary_learner.load_state_dict(__magic_name__ )
													UpperCAmelCase_      :   Dict      =	model.transformer.wte
													UpperCAmelCase_      :   List[str]      =	copy.deepcopy(model.transformer.wte.weight )
													return secondary_learner












'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

