


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_   (	_a=32     ,       _a=10     ,       _a=100     ,       _a=1_026     ,       _a=True     ,       _a="data/tokenized_stories_train_wikitext103.jbl"     ,       _a="igf_context_pairs.jbl"     ,       ):




		"""simple docstring"""






		set_seed(3	)
		# generate train_data and objective_set
		lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= generate_datasets(
		    _a     ,       _a     ,       number=_a     ,       min_len=1_026     ,       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?
		lowerCAmelCase__       :      Union[str, Any]   					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

		# load pretrained model
		lowerCAmelCase__       :      Optional[Any]   					= load_gpta('''gpt2'''	).to(_a	)
		print('''computing perplexity on objective set'''	)
		lowerCAmelCase__       :      Tuple   					= 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 					lowerCamelCase_   (	_a     ,       _a=15     ,       _a=128     ,       _a=100     ,       _a="igf_model.pt"     ,       ):




		"""simple docstring"""






		set_seed(42	)

		# Load pre-trained model
		lowerCAmelCase__       :      Optional[Any]   					= GPTaLMHeadModel.from_pretrained('''gpt2'''	)

		# Initialize secondary learner to use embedding weights of model
		lowerCAmelCase__       :      Optional[Any]   					= SecondaryLearner(_a	)

		# Train secondary learner
		lowerCAmelCase__       :      str   					= 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 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=32     ,       _a=1_000     ,       _a=16     ,       _a=1.0     ,       _a=recopy_gpta     ,       _a=None     ,       _a=10     ,       _a="gpt2_finetuned.pt"     ,       ):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
		lowerCAmelCase__       :      Tuple   					= RandomSampler(_a	)
		lowerCAmelCase__       :      Union[str, Any]   					= DataLoader(_a     ,       sampler=_a	)

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

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

		lowerCAmelCase__       :      Optional[Any]   					= []
		lowerCAmelCase__       :      List[str]   					= []

		# Compute the performance of the transformer model at the beginning
		lowerCAmelCase__       :      List[Any]   					= 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()
						lowerCAmelCase__       :      Optional[int]   					= random.randint(0     ,       example.size(2	) - context_len - 1	)
						lowerCAmelCase__       :      str   					= example[0, 0, start : start + context_len]
						lm_optimizer.zero_grad()
						lowerCAmelCase__       :      str   					= model(_a     ,       labels=_a	)
						lowerCAmelCase__       :      Optional[Any]   					= True

						if secondary_learner is not None:
								lowerCAmelCase__       :      Tuple   					= 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:
										lowerCAmelCase__       :      int   					= -1
								if predicted_q < threshold:
										lowerCAmelCase__       :      int   					= False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()	)	)
								lowerCAmelCase__       :      Dict   					= 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__       :      Optional[int]   					= 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__       :      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 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= 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=1_000     ,       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=1_026     ,       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=1_026     ,       trim=_a     ,       data_file='''data/tokenized_stories_train_wikitext103.jbl'''     ,       igf_data_file='''igf_context_pairs.jbl'''     ,       )

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

		# Train secondary learner
		lowerCAmelCase__       :      int   					= 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
		lowerCAmelCase__       :      Optional[int]   					= GPTaLMHeadModel.from_pretrained('''gpt2'''	)
		set_seed(42	)

		# Generate train and test data to train and evaluate gpt2 model
		lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= generate_datasets(
		    context_len=32     ,       file='''data/tokenized_stories_train_wikitext103.jbl'''     ,       number=100     ,       min_len=1_026     ,       trim=_a	)

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    _a     ,       _a     ,       _a     ,       context_len=32     ,       max_steps=1_000     ,       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()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}





class  _a							(    _lowercase):
	_a  :  List[Any]				   =							'''dpr'''




	def __init__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[str]=3_0522		, _SCREAMING_SNAKE_CASE						:		Optional[int]=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=512		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		int = 0		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	Optional[int]:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Union[str, Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      int   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      List[Any]   					= type_vocab_size
			lowerCAmelCase__       :      Optional[int]   					= initializer_range
			lowerCAmelCase__       :      List[str]   					= layer_norm_eps
			lowerCAmelCase__       :      Dict   					= projection_dim
			lowerCAmelCase__       :      int   					= position_embedding_type




