
'''simple docstring'''





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def  UpperCamelCase					(							lowerCAmelCase__=32				,    lowerCAmelCase__=10				,    lowerCAmelCase__=100				,    lowerCAmelCase__=1026				,    lowerCAmelCase__=True				,    lowerCAmelCase__="data/tokenized_stories_train_wikitext103.jbl"				,    lowerCAmelCase__="igf_context_pairs.jbl"				,    ):
					'''simple docstring'''
					set_seed(3	)
					# generate train_data and objective_set
					lowercase			,		lowercase					=		generate_datasets(
					    lowerCAmelCase__				,    lowerCAmelCase__				,    number=lowerCAmelCase__				,    min_len=1026				,    trim=lowerCAmelCase__	)
					# keeps model same across runs
					set_seed(4	)
					# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
					# can we train on GPU?
					lowercase					=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

					# load pretrained model
					lowercase					=		load_gpta('''gpt2'''	).to(lowerCAmelCase__	)
					print('''computing perplexity on objective set'''	)
					lowercase					=		compute_perplexity(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	).item()
					print('''perplexity on objective set:'''				,    lowerCAmelCase__	)

					# collect igf pairs and save to file demo.jbl
					collect_objective_set(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)

					# clean up, delete model and data we don't need anymore
					del model, train_data, objective_set
					torch.cuda.empty_cache()



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=15				,    lowerCAmelCase__=128				,    lowerCAmelCase__=100				,    lowerCAmelCase__="igf_model.pt"				,    ):
					'''simple docstring'''
					set_seed(42	)

					# Load pre-trained model
					lowercase					=		GPTaLMHeadModel.from_pretrained('''gpt2'''	)

					# Initialize secondary learner to use embedding weights of model
					lowercase					=		SecondaryLearner(lowerCAmelCase__	)

					# Train secondary learner
					lowercase					=		train_secondary_learner(
					    lowerCAmelCase__				,    lowerCAmelCase__				,    max_epochs=lowerCAmelCase__				,    batch_size=lowerCAmelCase__				,    eval_freq=100				,    igf_model_path=lowerCAmelCase__				,    )

					del model, secondary_learner_train_data
					torch.cuda.empty_cache()

					return secondary_learner



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=32				,    lowerCAmelCase__=1000				,    lowerCAmelCase__=16				,    lowerCAmelCase__=1.0				,    lowerCAmelCase__=recopy_gpta				,    lowerCAmelCase__=None				,    lowerCAmelCase__=10				,    lowerCAmelCase__="gpt2_finetuned.pt"				,    ):
					'''simple docstring'''
					lowercase					=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
					lowercase					=		RandomSampler(lowerCAmelCase__	)
					lowercase					=		DataLoader(lowerCAmelCase__				,    sampler=lowerCAmelCase__	)

					lowercase					=		max_steps // (len(lowerCAmelCase__	)) + 1
					lowercase					=		0
					lowercase					=		torch.zeros((1, context_len)				,    dtype=torch.long				,    device=lowerCAmelCase__	)
					lowercase			,		lowercase			,		lowercase					=		recopy_model(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)

					model.train()
					if secondary_learner is not None:
										secondary_learner.to(lowerCAmelCase__	)
										secondary_learner.eval()
					lowercase					=		[]
					lowercase					=		0

					lowercase					=		[]
					lowercase					=		[]

					# Compute the performance of the transformer model at the beginning
					lowercase					=		compute_perplexity(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					test_perps.append(lowerCAmelCase__	)
					print('''Test perplexity, step'''				,    lowerCAmelCase__				,    ''':'''				,    lowerCAmelCase__	)
					for epoch in range(int(lowerCAmelCase__	)	):
										for step, example in enumerate(lowerCAmelCase__	):
															torch.cuda.empty_cache()
															lowercase					=		random.randint(0				,    example.size(2	) - context_len - 1	)
															lowercase					=		example[0, 0, start : start + context_len]
															lm_optimizer.zero_grad()
															lowercase					=		model(lowerCAmelCase__				,    labels=lowerCAmelCase__	)
															lowercase					=		True

															if secondary_learner is not None:
																				lowercase					=		secondary_learner.forward(
																				    torch.tensor(lowerCAmelCase__				,    dtype=torch.long				,    device=lowerCAmelCase__	).unsqueeze(0	)	)[0].item()
																				observed_qs.append(float(lowerCAmelCase__	)	)

																				# Here we implement the simple non-constant threshold for the predicted IG(X) value
																				# We will decay the selectivity of our secondary learner filter from
																				# 1 standard deviation above average to 1 below average after 10 batches.

																				if global_step == 10:
																									lowercase					=		-1
																				if predicted_q < threshold:
																									lowercase					=		False

            # If we passed the filter, add the context to the batch!
															if do_backprop:
																				contexts.append(np.array(context.cpu()	)	)
																				lowercase					=		outputs[0]
																				lm_loss.backward()
																				examples += 1

															del outputs

															# Once the batch is filled with enough contexts, backprop on the batch.
															if examples == batch_size:
																				torch.cuda.empty_cache()
																				lowercase					=		0
																				# Do LM backprop
																				torch.nn.utils.clip_grad_norm_(model.parameters()				,    3.0	)
																				lm_optimizer.step()
																				lm_scheduler.step()  # Update learning rate schedule
																				global_step += 1
																				# Compute the performance of the transformer model at this batch
																				if global_step % eval_interval == 0:
																									lowercase					=		compute_perplexity(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
																									test_perps.append(lowerCAmelCase__	)

																									print('''Test perplexity, step'''				,    lowerCAmelCase__				,    ''':'''				,    lowerCAmelCase__	)
            # Break out of the loop after 60 batches
															if max_steps > 0 and global_step > 60:
																				break
										if max_steps > 0 and global_step > 60:
															break

    # save finetuned transformer model
					torch.save(model.state_dict()				,    lowerCAmelCase__	)
					torch.cuda.empty_cache()
					# Do some cleaning up so we can reinitialize for the next run of this function
					del lm_optimizer
					del lm_scheduler
					return model



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''	)

					# Required parameters
					parser.add_argument(
					    '''--data_dir'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    required=lowerCAmelCase__				,    help='''The input data dir. Should contain data files for WikiText.'''				,    )
					parser.add_argument(
					    '''--model_name_or_path'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    required=lowerCAmelCase__				,    help='''Path to pretrained model or model identifier from huggingface.co/models'''				,    )
					parser.add_argument(
					    '''--data_file'''				,    type=lowerCAmelCase__				,    default=lowerCAmelCase__				,    help=(
					        '''A jbl file containing tokenized data which can be split as objective dataset, '''
					        '''train_dataset and test_dataset.'''
					    )				,    )

					parser.add_argument(
					    '''--igf_data_file'''				,    type=lowerCAmelCase__				,    default=lowerCAmelCase__				,    help='''A jbl file containing the context and information gain pairs to train secondary learner.'''				,    )

					parser.add_argument(
					    '''--output_dir'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    required=lowerCAmelCase__				,    help='''The output directory where the final fine-tuned model is stored.'''				,    )

					parser.add_argument(
					    '''--tokenizer_name'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    help='''Pretrained tokenizer name or path if not the same as model_name'''				,    )
					parser.add_argument('''--seed'''				,    type=lowerCAmelCase__				,    default=lowerCAmelCase__				,    help='''A seed for reproducible training.'''	)

					parser.add_argument(
					    '''--context_len'''				,    default=32				,    type=lowerCAmelCase__				,    help=(
					        '''The maximum total input sequence length after tokenization. Sequences longer '''
					        '''than this will be truncated, sequences shorter will be padded.'''
					    )				,    )

					parser.add_argument(
					    '''--size_objective_set'''				,    default=100				,    type=lowerCAmelCase__				,    help='''number of articles that are long enough to be used as our objective set'''				,    )
					parser.add_argument(
					    '''--eval_freq'''				,    default=100				,    type=lowerCAmelCase__				,    help='''secondary model evaluation is triggered at eval_freq'''	)

					parser.add_argument('''--max_steps'''				,    default=1000				,    type=lowerCAmelCase__				,    help='''To calculate training epochs'''	)

					parser.add_argument(
					    '''--secondary_learner_batch_size'''				,    default=128				,    type=lowerCAmelCase__				,    help='''batch size of training data for secondary learner'''				,    )

					parser.add_argument(
					    '''--batch_size'''				,    default=16				,    type=lowerCAmelCase__				,    help='''batch size of training data of language model(gpt2) '''	)

					parser.add_argument(
					    '''--eval_interval'''				,    default=10				,    type=lowerCAmelCase__				,    help=(
					        '''decay the selectivity of our secondary learner filter from'''
					        '''1 standard deviation above average to 1 below average after 10 batches'''
					    )				,    )

					parser.add_argument(
					    '''--number'''				,    default=100				,    type=lowerCAmelCase__				,    help='''The number of examples split to be used as objective_set/test_data'''	)

					parser.add_argument(
					    '''--min_len'''				,    default=1026				,    type=lowerCAmelCase__				,    help='''The minimum length of the article to be used as objective set'''	)

					parser.add_argument(
					    '''--secondary_learner_max_epochs'''				,    default=15				,    type=lowerCAmelCase__				,    help='''number of epochs to train secondary learner'''	)

					parser.add_argument('''--trim'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    help='''truncate the example if it exceeds context length'''	)

					parser.add_argument(
					    '''--threshold'''				,    default=1.0				,    type=lowerCAmelCase__				,    help=(
					        '''The threshold value used by secondary learner to filter the train_data and allow only'''
					        ''' informative data as input to the model'''
					    )				,    )

					parser.add_argument('''--finetuned_model_name'''				,    default='''gpt2_finetuned.pt'''				,    type=lowerCAmelCase__				,    help='''finetuned_model_name'''	)

					parser.add_argument(
					    '''--recopy_model'''				,    default=lowerCAmelCase__				,    type=lowerCAmelCase__				,    help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''				,    )

					# function calls
					# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
					generate_n_pairs(
					    context_len=32				,    max_steps=10				,    size_objective_set=100				,    min_len=1026				,    trim=lowerCAmelCase__				,    data_file='''data/tokenized_stories_train_wikitext103.jbl'''				,    igf_data_file='''igf_context_pairs.jbl'''				,    )

					# Load train data for secondary learner
					lowercase					=		joblib.load('''data/IGF_values.jbl'''	)

					# Train secondary learner
					lowercase					=		training_secondary_learner(
					    lowerCAmelCase__				,    secondary_learner_max_epochs=15				,    secondary_learner_batch_size=128				,    eval_freq=100				,    igf_model_path='''igf_model.pt'''				,    )

					# load pretrained gpt2 model
					lowercase					=		GPTaLMHeadModel.from_pretrained('''gpt2'''	)
					set_seed(42	)

					# Generate train and test data to train and evaluate gpt2 model
					lowercase			,		lowercase					=		generate_datasets(
					    context_len=32				,    file='''data/tokenized_stories_train_wikitext103.jbl'''				,    number=100				,    min_len=1026				,    trim=lowerCAmelCase__	)

					# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
					finetune(
					    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    context_len=32				,    max_steps=1000				,    batch_size=16				,    threshold=1.0				,    recopy_model=lowerCAmelCase__				,    secondary_learner=lowerCAmelCase__				,    eval_interval=10				,    finetuned_model_name='''gpt2_finetuned.pt'''				,    )


if __name__ == "__main__":
							main()






class        lowercase	:




							def __init__(       self     ,A__):
												lowercase					=		val
												lowercase					=		None
												lowercase					=		None





							def 				A__   (       self     ,A__):
												if self.val:
																	if val < self.val:
																						if self.left is None:
																											lowercase					=		Node(A__)
																						else:
																											self.left.insert(A__)
																	elif val > self.val:
																						if self.right is None:
																											lowercase					=		Node(A__)
																						else:
																											self.right.insert(A__)
												else:
																	lowercase					=		val



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					# Recursive traversal
					if root:
										inorder(root.left				,    lowerCAmelCase__	)
										res.append(root.val	)
										inorder(root.right				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					# Build BST
					if len(lowerCAmelCase__	) == 0:
										return arr
					lowercase					=		Node(arr[0]	)
					for i in range(1				,    len(lowerCAmelCase__	)	):
										root.insert(arr[i]	)
					# Traverse BST in order.
					lowercase					=		[]
					inorder(lowerCAmelCase__				,    lowerCAmelCase__	)
					return res


if __name__ == "__main__":
							print(tree_sort([10, 1, 3, 2, 9, 14, 13]))




