


'''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 							__magic_name__(						lowerCamelCase=3_2,				lowerCamelCase=1_0,				lowerCamelCase=1_0_0,				lowerCamelCase=1_0_2_6,				lowerCamelCase=True,				lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl",				lowerCamelCase="igf_context_pairs.jbl",				):
						set_seed(3)
						# generate train_data and objective_set
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				generate_datasets(
						    lowerCamelCase,				lowerCamelCase,				number=lowerCamelCase,				min_len=1_0_2_6,				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?
						__lowerCAmelCase							       =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')

						# load pretrained model
						__lowerCAmelCase							       =				load_gpta('''gpt2''').to(lowerCamelCase)
						print('''computing perplexity on objective set''')
						__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase,				lowerCamelCase=1_5,				lowerCamelCase=1_2_8,				lowerCamelCase=1_0_0,				lowerCamelCase="igf_model.pt",				):
						set_seed(4_2)

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

						# Initialize secondary learner to use embedding weights of model
						__lowerCAmelCase							       =				SecondaryLearner(lowerCamelCase)

						# Train secondary learner
						__lowerCAmelCase							       =				train_secondary_learner(
						    lowerCamelCase,				lowerCamelCase,				max_epochs=lowerCamelCase,				batch_size=lowerCamelCase,				eval_freq=1_0_0,				igf_model_path=lowerCamelCase,				)

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=3_2,				lowerCamelCase=1_0_0_0,				lowerCamelCase=1_6,				lowerCamelCase=1.0,				lowerCamelCase=recopy_gpta,				lowerCamelCase=None,				lowerCamelCase=1_0,				lowerCamelCase="gpt2_finetuned.pt",				):
						__lowerCAmelCase							       =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
						__lowerCAmelCase							       =				RandomSampler(lowerCamelCase)
						__lowerCAmelCase							       =				DataLoader(lowerCamelCase,				sampler=lowerCamelCase)

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

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

						__lowerCAmelCase							       =				[]
						__lowerCAmelCase							       =				[]

						# Compute the performance of the transformer model at the beginning
						__lowerCAmelCase							       =				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()
																		__lowerCAmelCase							       =				random.randint(0,				example.size(2) - context_len - 1)
																		__lowerCAmelCase							       =				example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		__lowerCAmelCase							       =				model(lowerCamelCase,				labels=lowerCamelCase)
																		__lowerCAmelCase							       =				True

																		if secondary_learner is not None:
																								__lowerCAmelCase							       =				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 == 1_0:
																														__lowerCAmelCase							       =				-1
																								if predicted_q < threshold:
																														__lowerCAmelCase							       =				False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()))
																								__lowerCAmelCase							       =				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()
																								__lowerCAmelCase							       =				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:
																														__lowerCAmelCase							       =				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 > 6_0:
																								break
												if max_steps > 0 and global_step > 6_0:
																		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 							__magic_name__(						):
						__lowerCAmelCase							       =				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=3_2,				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=1_0_0,				type=lowerCamelCase,				help='''number of articles that are long enough to be used as our objective set''',				)
						parser.add_argument(
						    '''--eval_freq''',				default=1_0_0,				type=lowerCamelCase,				help='''secondary model evaluation is triggered at eval_freq''')

						parser.add_argument('''--max_steps''',				default=1_0_0_0,				type=lowerCamelCase,				help='''To calculate training epochs''')

						parser.add_argument(
						    '''--secondary_learner_batch_size''',				default=1_2_8,				type=lowerCamelCase,				help='''batch size of training data for secondary learner''',				)

						parser.add_argument(
						    '''--batch_size''',				default=1_6,				type=lowerCamelCase,				help='''batch size of training data of language model(gpt2) ''')

						parser.add_argument(
						    '''--eval_interval''',				default=1_0,				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=1_0_0,				type=lowerCamelCase,				help='''The number of examples split to be used as objective_set/test_data''')

						parser.add_argument(
						    '''--min_len''',				default=1_0_2_6,				type=lowerCamelCase,				help='''The minimum length of the article to be used as objective set''')

						parser.add_argument(
						    '''--secondary_learner_max_epochs''',				default=1_5,				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=3_2,				max_steps=1_0,				size_objective_set=1_0_0,				min_len=1_0_2_6,				trim=lowerCamelCase,				data_file='''data/tokenized_stories_train_wikitext103.jbl''',				igf_data_file='''igf_context_pairs.jbl''',				)

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

						# Train secondary learner
						__lowerCAmelCase							       =				training_secondary_learner(
						    lowerCamelCase,				secondary_learner_max_epochs=1_5,				secondary_learner_batch_size=1_2_8,				eval_freq=1_0_0,				igf_model_path='''igf_model.pt''',				)

						# load pretrained gpt2 model
						__lowerCAmelCase							       =				GPTaLMHeadModel.from_pretrained('''gpt2''')
						set_seed(4_2)

						# Generate train and test data to train and evaluate gpt2 model
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				generate_datasets(
						    context_len=3_2,				file='''data/tokenized_stories_train_wikitext103.jbl''',				number=1_0_0,				min_len=1_0_2_6,				trim=lowerCamelCase)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				context_len=3_2,				max_steps=1_0_0_0,				batch_size=1_6,				threshold=1.0,				recopy_model=lowerCamelCase,				secondary_learner=lowerCamelCase,				eval_interval=1_0,				finetuned_model_name='''gpt2_finetuned.pt''',				)


if __name__ == "__main__":
	main()





'''simple docstring'''

from manim import *


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Rectangle(height=0.5       ,			width=0.5		)
										__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				VGroup(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''CPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										cpu.move_to([-2.5, -0.5, 0]		)
										self.add(__lowercase		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(1		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''GPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										gpu.align_to(__lowercase       ,			__lowercase		)
										gpu.set_x(gpu.get_x() - 1		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[mem.copy() for i in range(6		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)

										__lowerCAmelCase							       =				Text('''Model'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										model.move_to([3, -1.0, 0]		)

										self.play(
										    Create(__lowercase       ,			run_time=1		)       ,			Create(__lowercase       ,			run_time=1		)       ,			Create(__lowercase       ,			run_time=1		)       ,			)

										__lowerCAmelCase							       =				MarkupText(
										    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""       ,			font_size=24       ,			)

										__lowerCAmelCase							       =				Square(side_length=2.2		)
										key.move_to([-5, 2, 0]		)

										__lowerCAmelCase							       =				MarkupText(
										    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""       ,			font_size=18       ,			)

										key_text.move_to([-5, 2.4, 0]		)

										step_a.move_to([2, 2, 0]		)
										self.play(Write(__lowercase       ,			run_time=2.5		)       ,			Write(__lowercase		)       ,			Write(__lowercase		)		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										for i, rect in enumerate(__lowercase		):
																__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0.0		).set_fill(__lowercase       ,			opacity=0.7		)
																cpu_target.move_to(__lowercase		)
																cpu_target.generate_target()
																__lowerCAmelCase							       =				0.4_6 / 4
																__lowerCAmelCase							       =				0.4_6 / 3

																if i == 0:
																						cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)       ,			buff=0.0_2       ,			direction=__lowercase		)
																						cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
																elif i == 3:
																						cpu_target.target.next_to(cpu_targs[0].target       ,			direction=__lowercase       ,			buff=0.0		)
																else:
																						cpu_target.target.next_to(cpu_targs[i - 1].target       ,			direction=__lowercase       ,			buff=0.0		)
																cpu_targs.append(__lowercase		)

																first_animations.append(rect.animate(run_time=0.5		).set_stroke(__lowercase		)		)
																second_animations.append(MoveToTarget(__lowercase       ,			run_time=1.5		)		)

										self.play(*__lowercase		)
										self.play(*__lowercase		)

										self.wait()

