


'''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     a   (    __a=32				,      __a=10				,      __a=100				,      __a=1026				,      __a=True				,      __a="data/tokenized_stories_train_wikitext103.jbl"				,      __a="igf_context_pairs.jbl"				,      )						->			List[str]:



					'''simple docstring'''






					set_seed(3     )
					# generate train_data and objective_set
					UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				generate_datasets(
					    __a				,      __a				,      number=__a				,      min_len=1026				,      trim=__a     )
					# 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__     :str							      =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''     )

					# load pretrained model
					UpperCamelCase__     :Optional[Any]							      =				load_gpta('''gpt2'''     ).to(__a     )
					print('''computing perplexity on objective set'''     )
					UpperCamelCase__     :int							      =				compute_perplexity(__a				,      __a				,      __a     ).item()
					print('''perplexity on objective set:'''				,      __a     )

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

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


def     a   (    __a				,      __a=15				,      __a=128				,      __a=100				,      __a="igf_model.pt"				,      )						->			Dict:



					'''simple docstring'''






					set_seed(42     )

					# Load pre-trained model
					UpperCamelCase__     :Tuple							      =				GPTaLMHeadModel.from_pretrained('''gpt2'''     )

					# Initialize secondary learner to use embedding weights of model
					UpperCamelCase__     :List[str]							      =				SecondaryLearner(__a     )

					# Train secondary learner
					UpperCamelCase__     :Optional[int]							      =				train_secondary_learner(
					    __a				,      __a				,      max_epochs=__a				,      batch_size=__a				,      eval_freq=100				,      igf_model_path=__a				,      )

					del model, secondary_learner_train_data
					torch.cuda.empty_cache()

					return secondary_learner


def     a   (    __a				,      __a				,      __a				,      __a=32				,      __a=1000				,      __a=16				,      __a=1.0				,      __a=recopy_gpta				,      __a=None				,      __a=10				,      __a="gpt2_finetuned.pt"				,      )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''     )
					UpperCamelCase__     :List[Any]							      =				RandomSampler(__a     )
					UpperCamelCase__     :Union[str, Any]							      =				DataLoader(__a				,      sampler=__a     )

					UpperCamelCase__     :Optional[int]							      =				max_steps // (len(__a     )) + 1
					UpperCamelCase__     :Union[str, Any]							      =				0
					UpperCamelCase__     :int							      =				torch.zeros((1, context_len)				,      dtype=torch.long				,      device=__a     )
					UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				recopy_model(__a				,      __a				,      __a     )

					model.train()
					if secondary_learner is not None:
										secondary_learner.to(__a     )
										secondary_learner.eval()
					UpperCamelCase__     :List[str]							      =				[]
					UpperCamelCase__     :List[str]							      =				0

					UpperCamelCase__     :List[str]							      =				[]
					UpperCamelCase__     :Union[str, Any]							      =				[]

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

															if secondary_learner is not None:
																				UpperCamelCase__     :Union[str, Any]							      =				secondary_learner.forward(
																				    torch.tensor(__a				,      dtype=torch.long				,      device=__a     ).unsqueeze(0     )     )[0].item()
																				observed_qs.append(float(__a     )     )

																				# 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__     :Dict							      =				-1
																				if predicted_q < threshold:
																									UpperCamelCase__     :Optional[Any]							      =				False

            # If we passed the filter, add the context to the batch!
															if do_backprop:
																				contexts.append(np.array(context.cpu()     )     )
																				UpperCamelCase__     :Union[str, Any]							      =				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__     :List[Any]							      =				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__     :List[Any]							      =				compute_perplexity(__a				,      __a				,      __a     )
																									test_perps.append(__a     )

																									print('''Test perplexity, step'''				,      __a				,      ''':'''				,      __a     )
            # 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()				,      __a     )
					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     a   (    )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''     )

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

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

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

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

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

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

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

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

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

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

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

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

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

					# Load train data for secondary learner
					UpperCamelCase__     :Union[str, Any]							      =				joblib.load('''data/IGF_values.jbl'''     )

					# Train secondary learner
					UpperCamelCase__     :Any							      =				training_secondary_learner(
					    __a				,      secondary_learner_max_epochs=15				,      secondary_learner_batch_size=128				,      eval_freq=100				,      igf_model_path='''igf_model.pt'''				,      )

					# load pretrained gpt2 model
					UpperCamelCase__     :Union[str, Any]							      =				GPTaLMHeadModel.from_pretrained('''gpt2'''     )
					set_seed(42     )

					# Generate train and test data to train and evaluate gpt2 model
					UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				generate_datasets(
					    context_len=32				,      file='''data/tokenized_stories_train_wikitext103.jbl'''				,      number=100				,      min_len=1026				,      trim=__a     )

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


if __name__ == "__main__":
		main()



'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps