



"""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 			_lowerCAmelCase				(       lowercase_=32		,     lowercase_=10		,     lowercase_=100		,     lowercase_=1026		,     lowercase_=True		,     lowercase_="data/tokenized_stories_train_wikitext103.jbl"		,     lowercase_="igf_context_pairs.jbl"		,     ):
						set_seed(3						)
						# generate train_data and objective_set
						UpperCAmelCase				,   UpperCAmelCase					=			generate_datasets(
						    lowercase_		,     lowercase_		,     number=lowercase_		,     min_len=1026		,     trim=lowercase_						)
						# 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?
						UpperCAmelCase					=			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'						)

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

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

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_=15		,     lowercase_=128		,     lowercase_=100		,     lowercase_="igf_model.pt"		,     ):
						set_seed(42						)

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

						# Initialize secondary learner to use embedding weights of model
						UpperCAmelCase					=			SecondaryLearner(lowercase_						)

						# Train secondary learner
						UpperCAmelCase					=			train_secondary_learner(
						    lowercase_		,     lowercase_		,     max_epochs=lowercase_		,     batch_size=lowercase_		,     eval_freq=100		,     igf_model_path=lowercase_		,     )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_=32		,     lowercase_=1000		,     lowercase_=16		,     lowercase_=1.0		,     lowercase_=recopy_gpta		,     lowercase_=None		,     lowercase_=10		,     lowercase_="gpt2_finetuned.pt"		,     ):
						UpperCAmelCase					=			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'						)
						UpperCAmelCase					=			RandomSampler(lowercase_						)
						UpperCAmelCase					=			DataLoader(lowercase_		,     sampler=lowercase_						)

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

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

						UpperCAmelCase					=			[]
						UpperCAmelCase					=			[]

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

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

																								# 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:
																														UpperCAmelCase					=			-1
																								if predicted_q < threshold:
																														UpperCAmelCase					=			False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()						)						)
																								UpperCAmelCase					=			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()
																								UpperCAmelCase					=			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:
																														UpperCAmelCase					=			compute_perplexity(lowercase_		,     lowercase_		,     lowercase_						)
																														test_perps.append(lowercase_						)

																														print('Test perplexity, step'		,     lowercase_		,     ':'		,     lowercase_						)
            # 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()		,     lowercase_						)
						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 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'						)

						# Required parameters
						parser.add_argument(
						    '--data_dir'		,     default=lowercase_		,     type=lowercase_		,     required=lowercase_		,     help='The input data dir. Should contain data files for WikiText.'		,     )
						parser.add_argument(
						    '--model_name_or_path'		,     default=lowercase_		,     type=lowercase_		,     required=lowercase_		,     help='Path to pretrained model or model identifier from huggingface.co/models'		,     )
						parser.add_argument(
						    '--data_file'		,     type=lowercase_		,     default=lowercase_		,     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=lowercase_		,     default=lowercase_		,     help='A jbl file containing the context and information gain pairs to train secondary learner.'		,     )

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

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

						parser.add_argument(
						    '--context_len'		,     default=32		,     type=lowercase_		,     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=lowercase_		,     help='number of articles that are long enough to be used as our objective set'		,     )
						parser.add_argument(
						    '--eval_freq'		,     default=100		,     type=lowercase_		,     help='secondary model evaluation is triggered at eval_freq'						)

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

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

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

						parser.add_argument(
						    '--eval_interval'		,     default=10		,     type=lowercase_		,     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=lowercase_		,     help='The number of examples split to be used as objective_set/test_data'						)

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

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

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

						parser.add_argument(
						    '--threshold'		,     default=1.0		,     type=lowercase_		,     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=lowercase_		,     help='finetuned_model_name'						)

						parser.add_argument(
						    '--recopy_model'		,     default=lowercase_		,     type=lowercase_		,     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=lowercase_		,     data_file='data/tokenized_stories_train_wikitext103.jbl'		,     igf_data_file='igf_context_pairs.jbl'		,     )

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

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

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

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

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


if __name__ == "__main__":
				main()









"""simple docstring"""


from math import factorial, radians

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = 18		,     lowercase_ = 10						):
						UpperCAmelCase					=			angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

						# Converting from degrees to radians
						UpperCAmelCase					=			radians(lowercase_						)

						UpperCAmelCase					=			angle_in_radians
						UpperCAmelCase					=			3
						UpperCAmelCase					=			-1

						for _ in range(lowercase_						):
												result += (b * (angle_in_radians**a)) / factorial(lowercase_						)

												UpperCAmelCase					=			-b  # One positive term and the next will be negative and so on...
												a += 2  # Increased by 2 for every term.

						return round(lowercase_		,     lowercase_						)


if __name__ == "__main__":
				__import__("""doctest""").testmod()





