



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





							'''simple docstring'''




							set_seed(3  )
							# generate train_data and objective_set
							_lowerCAmelCase     :							Optional[Any]    =    generate_datasets(
							    _lowerCamelCase				, _lowerCamelCase				, number=_lowerCamelCase				, min_len=1_026				, 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     :							List[Any]    =    torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )

							# load pretrained model
							_lowerCAmelCase     :							str    =    load_gpta("gpt2"  ).to(_lowerCamelCase  )
							print("computing perplexity on objective set"  )
							_lowerCAmelCase     :							List[str]    =    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 				A				(					_lowerCamelCase				, _lowerCamelCase=15				, _lowerCamelCase=128				, _lowerCamelCase=100				, _lowerCamelCase="igf_model.pt"				, ):





							'''simple docstring'''




							set_seed(42  )

							# Load pre-trained model
							_lowerCAmelCase     :							str    =    GPTaLMHeadModel.from_pretrained("gpt2"  )

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

							# Train secondary learner
							_lowerCAmelCase     :							Tuple    =    train_secondary_learner(
							    _lowerCamelCase				, _lowerCamelCase				, max_epochs=_lowerCamelCase				, batch_size=_lowerCamelCase				, eval_freq=100				, igf_model_path=_lowerCamelCase				, )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=32				, _lowerCamelCase=1_000				, _lowerCamelCase=16				, _lowerCamelCase=1.0				, _lowerCamelCase=recopy_gpta				, _lowerCamelCase=None				, _lowerCamelCase=10				, _lowerCamelCase="gpt2_finetuned.pt"				, ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    torch.device("cuda:0" if torch.cuda.is_available() else "cpu"  )
							_lowerCAmelCase     :							Tuple    =    RandomSampler(_lowerCamelCase  )
							_lowerCAmelCase     :							Union[str, Any]    =    DataLoader(_lowerCamelCase				, sampler=_lowerCamelCase  )

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

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

							_lowerCAmelCase     :							int    =    []
							_lowerCAmelCase     :							int    =    []

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

																					if secondary_learner is not None:
																												_lowerCAmelCase     :							List[Any]    =    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 == 10:
																																			_lowerCAmelCase     :							List[Any]    =    -1
																												if predicted_q < threshold:
																																			_lowerCAmelCase     :							List[str]    =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()  )  )
																												_lowerCAmelCase     :							int    =    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     :							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:
																																			_lowerCAmelCase     :							Tuple    =    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 > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					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 				A				(					):





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

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

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

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

							parser.add_argument(
							    "--eval_interval"				, default=10				, 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=100				, type=_lowerCamelCase				, help="The number of examples split to be used as objective_set/test_data"  )

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

							parser.add_argument(
							    "--secondary_learner_max_epochs"				, default=15				, 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=32				, max_steps=10				, size_objective_set=100				, min_len=1_026				, trim=_lowerCamelCase				, data_file="data/tokenized_stories_train_wikitext103.jbl"				, igf_data_file="igf_context_pairs.jbl"				, )

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

							# Train secondary learner
							_lowerCAmelCase     :							str    =    training_secondary_learner(
							    _lowerCamelCase				, secondary_learner_max_epochs=15				, secondary_learner_batch_size=128				, eval_freq=100				, igf_model_path="igf_model.pt"				, )

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

							# Generate train and test data to train and evaluate gpt2 model
							_lowerCAmelCase     :							Dict    =    generate_datasets(
							    context_len=32				, file="data/tokenized_stories_train_wikitext103.jbl"				, number=100				, min_len=1_026				, trim=_lowerCamelCase  )

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


if __name__ == "__main__":
						main()

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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						str						=      """ctrl"""
    a__	:						Dict						=      ["""past_key_values"""]
    a__	:						Tuple						=      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(	self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :Optional[Any]			  =		n_positions
        __UpperCamelCase     :Dict			  =		n_embd
        __UpperCamelCase     :Dict			  =		n_layer
        __UpperCamelCase     :List[Any]			  =		n_head
        __UpperCamelCase     :int			  =		dff
        __UpperCamelCase     :Union[str, Any]			  =		resid_pdrop
        __UpperCamelCase     :Optional[int]			  =		embd_pdrop
        __UpperCamelCase     :List[Any]			  =		layer_norm_epsilon
        __UpperCamelCase     :Dict			  =		initializer_range

        __UpperCamelCase     :Any			  =		use_cache

        super().__init__(**__lowercase)





