"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs
"""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_(__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=1_026     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"     ,	__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"     ,	)->    Union[str, Any]:
							set_seed(3  )
							# generate train_data and objective_set
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    generate_datasets(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	number=__SCREAMING_SNAKE_CASE     ,	min_len=1_026     ,	trim=__SCREAMING_SNAKE_CASE  )
							# 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?
							_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )

							# load pretrained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    load_gpta("""gpt2"""  ).to(__SCREAMING_SNAKE_CASE  )
							print("""computing perplexity on objective set"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    compute_perplexity(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ).item()
							print("""perplexity on objective set:"""     ,	__SCREAMING_SNAKE_CASE  )

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

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=15     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE="igf_model.pt"     ,	)->    Optional[int]:
							set_seed(42  )

							# Load pre-trained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )

							# Initialize secondary learner to use embedding weights of model
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    SecondaryLearner(__SCREAMING_SNAKE_CASE  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						Any			  =    train_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	max_epochs=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	eval_freq=100     ,	igf_model_path=__SCREAMING_SNAKE_CASE     ,	)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=1_000     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=recopy_gpta     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"     ,	)->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RandomSampler(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(__SCREAMING_SNAKE_CASE     ,	sampler=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    max_steps // (len(__SCREAMING_SNAKE_CASE  )) + 1
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    0
							_SCREAMING_SNAKE_CASE		:						Any			  =    torch.zeros((1, context_len)     ,	dtype=torch.long     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    recopy_model(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(__SCREAMING_SNAKE_CASE  )
														secondary_learner.eval()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    []
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    0

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						int			  =    []

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

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

																												# 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:
																																			_SCREAMING_SNAKE_CASE		:						Dict			  =    -1
																												if predicted_q < threshold:
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    False

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

																																			print("""Test perplexity, step"""     ,	__SCREAMING_SNAKE_CASE     ,	""":"""     ,	__SCREAMING_SNAKE_CASE  )
            # 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()     ,	__SCREAMING_SNAKE_CASE  )
							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_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""  )

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

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

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

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

							parser.add_argument("""--max_steps"""     ,	default=1_000     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""To calculate training epochs"""  )

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

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

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

							parser.add_argument(
							    """--min_len"""     ,	default=1_026     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""The minimum length of the article to be used as objective set"""  )

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

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

							parser.add_argument(
							    """--threshold"""     ,	default=1.0     ,	type=__SCREAMING_SNAKE_CASE     ,	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=__SCREAMING_SNAKE_CASE     ,	help="""finetuned_model_name"""  )

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

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

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						int			  =    training_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	secondary_learner_max_epochs=15     ,	secondary_learner_batch_size=128     ,	eval_freq=100     ,	igf_model_path="""igf_model.pt"""     ,	)

							# load pretrained gpt2 model
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )
							set_seed(42  )

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

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	context_len=32     ,	max_steps=1_000     ,	batch_size=16     ,	threshold=1.0     ,	recopy_model=__SCREAMING_SNAKE_CASE     ,	secondary_learner=__SCREAMING_SNAKE_CASE     ,	eval_interval=10     ,	finetuned_model_name="""gpt2_finetuned.pt"""     ,	)


if __name__ == "__main__":
			main()


