






'''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 (				__A				: Optional[int]=32    ,		__A				: Union[str, Any]=10    ,		__A				: Union[str, Any]=1_00    ,		__A				: Optional[Any]=10_26    ,		__A				: Dict=True    ,		__A				: int="data/tokenized_stories_train_wikitext103.jbl"    ,		__A				: str="igf_context_pairs.jbl"    ,		):
				set_seed(3   )
				# generate train_data and objective_set
				a_					,      a_		:	Optional[int]									=   generate_datasets(
				    __A    ,		__A    ,		number=__A    ,		min_len=10_26    ,		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?
				a_		:	Dict									=   torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )

				# load pretrained model
				a_		:	str									=   load_gpta('''gpt2'''   ).to(__A   )
				print('''computing perplexity on objective set'''   )
				a_		:	Union[str, Any]									=   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 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: str=15    ,		__A				: Optional[int]=1_28    ,		__A				: Union[str, Any]=1_00    ,		__A				: Dict="igf_model.pt"    ,		):
				set_seed(42   )

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

				# Initialize secondary learner to use embedding weights of model
				a_		:	Tuple									=   SecondaryLearner(__A   )

				# Train secondary learner
				a_		:	Optional[Any]									=   train_secondary_learner(
				    __A    ,		__A    ,		max_epochs=__A    ,		batch_size=__A    ,		eval_freq=1_00    ,		igf_model_path=__A    ,		)

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: Dict    ,		__A				: Any    ,		__A				: Optional[int]=32    ,		__A				: int=10_00    ,		__A				: Any=16    ,		__A				: Tuple=1.0    ,		__A				: Optional[int]=recopy_gpta    ,		__A				: Optional[int]=None    ,		__A				: List[Any]=10    ,		__A				: List[str]="gpt2_finetuned.pt"    ,		):
				a_		:	Tuple									=   torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
				a_		:	Dict									=   RandomSampler(__A   )
				a_		:	int									=   DataLoader(__A    ,		sampler=__A   )

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

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

				a_		:	int									=   []
				a_		:	Union[str, Any]									=   []

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

												if secondary_learner is not None:
																a_		:	Dict									=   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:
																				a_		:	List[Any]									=   -1
																if predicted_q < threshold:
																				a_		:	Optional[int]									=   False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()   )   )
																a_		:	List[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()
																a_		:	Dict									=   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:
																				a_		:	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 		_UpperCAmelCase (				):
				a_		:	Optional[int]									=   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=1_00    ,		type=__A    ,		help='''number of articles that are long enough to be used as our objective set'''    ,		)
				parser.add_argument(
				    '''--eval_freq'''    ,		default=1_00    ,		type=__A    ,		help='''secondary model evaluation is triggered at eval_freq'''   )

				parser.add_argument('''--max_steps'''    ,		default=10_00    ,		type=__A    ,		help='''To calculate training epochs'''   )

				parser.add_argument(
				    '''--secondary_learner_batch_size'''    ,		default=1_28    ,		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=1_00    ,		type=__A    ,		help='''The number of examples split to be used as objective_set/test_data'''   )

				parser.add_argument(
				    '''--min_len'''    ,		default=10_26    ,		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=1_00    ,		min_len=10_26    ,		trim=__A    ,		data_file='''data/tokenized_stories_train_wikitext103.jbl'''    ,		igf_data_file='''igf_context_pairs.jbl'''    ,		)

				# Load train data for secondary learner
				a_		:	Optional[int]									=   joblib.load('''data/IGF_values.jbl'''   )

				# Train secondary learner
				a_		:	str									=   training_secondary_learner(
				    __A    ,		secondary_learner_max_epochs=15    ,		secondary_learner_batch_size=1_28    ,		eval_freq=1_00    ,		igf_model_path='''igf_model.pt'''    ,		)

				# load pretrained gpt2 model
				a_		:	Dict									=   GPTaLMHeadModel.from_pretrained('''gpt2'''   )
				set_seed(42   )

				# Generate train and test data to train and evaluate gpt2 model
				a_					,      a_		:	List[str]									=   generate_datasets(
				    context_len=32    ,		file='''data/tokenized_stories_train_wikitext103.jbl'''    ,		number=1_00    ,		min_len=10_26    ,		trim=__A   )

				# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
				finetune(
				    __A    ,		__A    ,		__A    ,		context_len=32    ,		max_steps=10_00    ,		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'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




