






import os
from datetime import datetime as dt

from github import Github


a__       =     [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]

def _UpperCAmelCase   (	):
							snake_case__	  =       Github(os.environ["""GITHUB_TOKEN"""] )
							snake_case__	  =       g.get_repo("""huggingface/diffusers""" )
							snake_case__	  =       repo.get_issues(state="""open""" )

							for issue in open_issues:
														snake_case__	  =       sorted(issue.get_comments()  ,					key=lambda a : i.created_at  ,					reverse=a )
														snake_case__	  =       comments[0] if len(a ) > 0 else None
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and (dt.utcnow() - issue.updated_at).days > 7
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
														):
																					# Closes the issue after 7 days of inactivity since the Stalebot notification.
																					issue.edit(state="""closed""" )
														elif (
														    "stale" in issue.get_labels()
														    and last_comment is not None
														    and last_comment.user.login != "github-actions[bot]"
														):
																					# Opens the issue if someone other than Stalebot commented.
																					issue.edit(state="""open""" )
																					issue.remove_from_labels("""stale""" )
														elif (
														    (dt.utcnow() - issue.updated_at).days > 23
														    and (dt.utcnow() - issue.created_at).days >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
														):
																					# Post a Stalebot notification after 23 days of inactivity.
																					issue.create_comment(
																					    """This issue has been automatically marked as stale because it has not had """
																					    """recent activity. If you think this still needs to be addressed """
																					    """please comment on this thread.\n\nPlease note that issues that do not follow the """
																					    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored.""" )
																					issue.add_to_labels("""stale""" )


if __name__ == "__main__":
	main()








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						:	int=32  ,					a						:	int=10  ,					a						:	Tuple=100  ,					a						:	str=1026  ,					a						:	Union[str, Any]=True  ,					a						:	Optional[int]="data/tokenized_stories_train_wikitext103.jbl"  ,					a						:	Tuple="igf_context_pairs.jbl"  ,					):
							set_seed(3 )
							# generate train_data and objective_set
							snake_case__  , snake_case__	  =       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?
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

							# load pretrained model
							snake_case__	  =       load_gpta("""gpt2""" ).to(a )
							print("""computing perplexity on objective set""" )
							snake_case__	  =       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						:	int  ,					a						:	Dict=15  ,					a						:	Optional[int]=128  ,					a						:	str=100  ,					a						:	Optional[int]="igf_model.pt"  ,					):
							set_seed(42 )

							# Load pre-trained model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )

							# Initialize secondary learner to use embedding weights of model
							snake_case__	  =       SecondaryLearner(a )

							# Train secondary learner
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str  ,					a						:	List[Any]=32  ,					a						:	Any=1000  ,					a						:	str=16  ,					a						:	Tuple=1.0  ,					a						:	str=recopy_gpta  ,					a						:	Any=None  ,					a						:	Optional[int]=10  ,					a						:	Tuple="gpt2_finetuned.pt"  ,					):
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
							snake_case__	  =       RandomSampler(a )
							snake_case__	  =       DataLoader(a  ,					sampler=a )

							snake_case__	  =       max_steps // (len(a )) + 1
							snake_case__	  =       0
							snake_case__	  =       torch.zeros((1, context_len)  ,					dtype=torch.long  ,					device=a )
							snake_case__  , snake_case__  , snake_case__	  =       recopy_model(a  ,					a  ,					a )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(a )
														secondary_learner.eval()
							snake_case__	  =       []
							snake_case__	  =       0

							snake_case__	  =       []
							snake_case__	  =       []

							# Compute the performance of the transformer model at the beginning
							snake_case__	  =       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()
																					snake_case__	  =       random.randint(0  ,					example.size(2 ) - context_len - 1 )
																					snake_case__	  =       example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					snake_case__	  =       model(a  ,					labels=a )
																					snake_case__	  =       True

																					if secondary_learner is not None:
																												snake_case__	  =       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:
																																			snake_case__	  =       -1
																												if predicted_q < threshold:
																																			snake_case__	  =       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												snake_case__	  =       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()
																												snake_case__	  =       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:
																																			snake_case__	  =       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   (	):
							snake_case__	  =       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
							snake_case__	  =       joblib.load("""data/IGF_values.jbl""" )

							# Train secondary learner
							snake_case__	  =       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
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )
							set_seed(42 )

							# Generate train and test data to train and evaluate gpt2 model
							snake_case__  , snake_case__	  =       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()
