


"""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 				A_       ( snake_case__=32       ,					snake_case__=10       ,					snake_case__=1_00       ,					snake_case__=10_26       ,					snake_case__=True       ,					snake_case__="data/tokenized_stories_train_wikitext103.jbl"       ,					snake_case__="igf_context_pairs.jbl"       ,					)					->  List[Any]:
			set_seed(3		)
			# generate train_data and objective_set
			_UpperCamelCase     ,      _UpperCamelCase					:str					     =	generate_datasets(
			    snake_case__       ,					snake_case__       ,					number=snake_case__       ,					min_len=10_26       ,					trim=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?
			_UpperCamelCase					:Tuple					     =	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)

			# load pretrained model
			_UpperCamelCase					:Optional[Any]					     =	load_gpta('''gpt2'''		).to(snake_case__		)
			print('''computing perplexity on objective set'''		)
			_UpperCamelCase					:Tuple					     =	compute_perplexity(snake_case__       ,					snake_case__       ,					snake_case__		).item()
			print('''perplexity on objective set:'''       ,					snake_case__		)

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

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





def 				A_       ( snake_case__       ,					snake_case__=15       ,					snake_case__=1_28       ,					snake_case__=1_00       ,					snake_case__="igf_model.pt"       ,					)					->  List[Any]:
			set_seed(42		)

			# Load pre-trained model
			_UpperCamelCase					:Union[str, Any]					     =	GPTaLMHeadModel.from_pretrained('''gpt2'''		)

			# Initialize secondary learner to use embedding weights of model
			_UpperCamelCase					:Optional[int]					     =	SecondaryLearner(snake_case__		)

			# Train secondary learner
			_UpperCamelCase					:Optional[int]					     =	train_secondary_learner(
			    snake_case__       ,					snake_case__       ,					max_epochs=snake_case__       ,					batch_size=snake_case__       ,					eval_freq=1_00       ,					igf_model_path=snake_case__       ,					)

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__=32       ,					snake_case__=10_00       ,					snake_case__=16       ,					snake_case__=1.0       ,					snake_case__=recopy_gpta       ,					snake_case__=None       ,					snake_case__=10       ,					snake_case__="gpt2_finetuned.pt"       ,					)					->  Optional[Any]:
			_UpperCamelCase					:Optional[Any]					     =	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
			_UpperCamelCase					:int					     =	RandomSampler(snake_case__		)
			_UpperCamelCase					:Union[str, Any]					     =	DataLoader(snake_case__       ,					sampler=snake_case__		)

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

			model.train()
			if secondary_learner is not None:
						secondary_learner.to(snake_case__		)
						secondary_learner.eval()
			_UpperCamelCase					:int					     =	[]
			_UpperCamelCase					:List[Any]					     =	0

			_UpperCamelCase					:int					     =	[]
			_UpperCamelCase					:Any					     =	[]

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

									if secondary_learner is not None:
												_UpperCamelCase					:Optional[Any]					     =	secondary_learner.forward(
												    torch.tensor(snake_case__       ,					dtype=torch.long       ,					device=snake_case__		).unsqueeze(0		)		)[0].item()
												observed_qs.append(float(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:
															_UpperCamelCase					:List[str]					     =	-1
												if predicted_q < threshold:
															_UpperCamelCase					:Optional[Any]					     =	False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()		)		)
												_UpperCamelCase					: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()
												_UpperCamelCase					:List[str]					     =	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:
															_UpperCamelCase					:Union[str, Any]					     =	compute_perplexity(snake_case__       ,					snake_case__       ,					snake_case__		)
															test_perps.append(snake_case__		)

															print('''Test perplexity, step'''       ,					snake_case__       ,					''':'''       ,					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()       ,					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 				A_       ( )					->  Union[str, Any]:
			_UpperCamelCase					:int					     =	argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''		)

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

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

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

			parser.add_argument(
			    '''--context_len'''       ,					default=32       ,					type=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=1_00       ,					type=snake_case__       ,					help='''number of articles that are long enough to be used as our objective set'''       ,					)
			parser.add_argument(
			    '''--eval_freq'''       ,					default=1_00       ,					type=snake_case__       ,					help='''secondary model evaluation is triggered at eval_freq'''		)

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

			parser.add_argument(
			    '''--secondary_learner_batch_size'''       ,					default=1_28       ,					type=snake_case__       ,					help='''batch size of training data for secondary learner'''       ,					)

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

			parser.add_argument(
			    '''--eval_interval'''       ,					default=10       ,					type=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=1_00       ,					type=snake_case__       ,					help='''The number of examples split to be used as objective_set/test_data'''		)

			parser.add_argument(
			    '''--min_len'''       ,					default=10_26       ,					type=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=snake_case__       ,					help='''number of epochs to train secondary learner'''		)

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

			parser.add_argument(
			    '''--threshold'''       ,					default=1.0       ,					type=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=snake_case__       ,					help='''finetuned_model_name'''		)

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

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

			# Train secondary learner
			_UpperCamelCase					:List[str]					     =	training_secondary_learner(
			    snake_case__       ,					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
			_UpperCamelCase					:Optional[int]					     =	GPTaLMHeadModel.from_pretrained('''gpt2'''		)
			set_seed(42		)

			# Generate train and test data to train and evaluate gpt2 model
			_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	generate_datasets(
			    context_len=32       ,					file='''data/tokenized_stories_train_wikitext103.jbl'''       ,					number=1_00       ,					min_len=10_26       ,					trim=snake_case__		)

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    snake_case__       ,					snake_case__       ,					snake_case__       ,					context_len=32       ,					max_steps=10_00       ,					batch_size=16       ,					threshold=1.0       ,					recopy_model=snake_case__       ,					secondary_learner=snake_case__       ,					eval_interval=10       ,					finetuned_model_name='''gpt2_finetuned.pt'''       ,					)


if __name__ == "__main__":
					main()





"""simple docstring"""






UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """prompt""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""prompt""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset([])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""image"""])

UpperCamelCase__				:str			   =	frozenset(
    [
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""image"""])

UpperCamelCase__				:Optional[int]			   =	frozenset(
    [
        """prompt""",
        """image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:int			   =	frozenset(["""prompt""", """image""", """negative_prompt"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        # Text guided image variation with an image mask
        """prompt""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
    ]
)

UpperCamelCase__				:Any			   =	frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])

UpperCamelCase__				:Dict			   =	frozenset(
    [
        # image variation with an image mask
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:Dict			   =	frozenset(["""image""", """mask_image"""])

UpperCamelCase__				:Tuple			   =	frozenset(
    [
        """example_image""",
        """image""",
        """mask_image""",
        """height""",
        """width""",
        """guidance_scale""",
    ]
)

UpperCamelCase__				:List[str]			   =	frozenset(["""example_image""", """image""", """mask_image"""])

UpperCamelCase__				:List[str]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""class_labels"""])

UpperCamelCase__				:Any			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[Any]			   =	frozenset([])

UpperCamelCase__				:Optional[Any]			   =	frozenset(["""batch_size"""])

UpperCamelCase__				:Optional[int]			   =	frozenset([])

UpperCamelCase__				:str			   =	frozenset(
    [
        """prompt""",
        """audio_length_in_s""",
        """guidance_scale""",
        """negative_prompt""",
        """prompt_embeds""",
        """negative_prompt_embeds""",
        """cross_attention_kwargs""",
    ]
)

UpperCamelCase__				:Union[str, Any]			   =	frozenset(["""prompt""", """negative_prompt"""])
UpperCamelCase__				:Tuple			   =	frozenset(["""input_tokens"""])

UpperCamelCase__				:int			   =	frozenset(["""input_tokens"""])

