






'''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  SCREAMING_SNAKE_CASE_       (UpperCamelCase=32				,			UpperCamelCase=10				,			UpperCamelCase=100				,			UpperCamelCase=1026				,			UpperCamelCase=True				,			UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"				,			UpperCamelCase="igf_context_pairs.jbl"				,			)      ->       List[str]:
							set_seed(3      )
							# generate train_data and objective_set
							lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       generate_datasets(
							    UpperCamelCase				,			UpperCamelCase				,			number=UpperCamelCase				,			min_len=1026				,			trim=UpperCamelCase      )
							# 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__					:	Any         =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )

							# load pretrained model
							lowerCamelCase__					:	Optional[int]         =       load_gpta("""gpt2"""      ).to(UpperCamelCase      )
							print("""computing perplexity on objective set"""      )
							lowerCamelCase__					:	Dict         =       compute_perplexity(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      ).item()
							print("""perplexity on objective set:"""				,			UpperCamelCase      )

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

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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=15				,			UpperCamelCase=128				,			UpperCamelCase=100				,			UpperCamelCase="igf_model.pt"				,			)      ->       str:
							set_seed(42      )

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

							# Initialize secondary learner to use embedding weights of model
							lowerCamelCase__					:	List[Any]         =       SecondaryLearner(UpperCamelCase      )

							# Train secondary learner
							lowerCamelCase__					:	List[str]         =       train_secondary_learner(
							    UpperCamelCase				,			UpperCamelCase				,			max_epochs=UpperCamelCase				,			batch_size=UpperCamelCase				,			eval_freq=100				,			igf_model_path=UpperCamelCase				,			)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=32				,			UpperCamelCase=1000				,			UpperCamelCase=16				,			UpperCamelCase=1.0				,			UpperCamelCase=recopy_gpta				,			UpperCamelCase=None				,			UpperCamelCase=10				,			UpperCamelCase="gpt2_finetuned.pt"				,			)      ->       List[Any]:
							lowerCamelCase__					:	List[str]         =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )
							lowerCamelCase__					:	Union[str, Any]         =       RandomSampler(UpperCamelCase      )
							lowerCamelCase__					:	str         =       DataLoader(UpperCamelCase				,			sampler=UpperCamelCase      )

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(UpperCamelCase      )
														secondary_learner.eval()
							lowerCamelCase__					:	List[str]         =       []
							lowerCamelCase__					:	Any         =       0

							lowerCamelCase__					:	Optional[int]         =       []
							lowerCamelCase__					:	List[Any]         =       []

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

																					if secondary_learner is not None:
																												lowerCamelCase__					:	str         =       secondary_learner.forward(
																												    torch.tensor(UpperCamelCase				,			dtype=torch.long				,			device=UpperCamelCase      ).unsqueeze(0      )      )[0].item()
																												observed_qs.append(float(UpperCamelCase      )      )

																												# 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[str]         =       -1
																												if predicted_q < threshold:
																																			lowerCamelCase__					:	str         =       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()      )      )
																												lowerCamelCase__					:	Optional[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__					:	Optional[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__					:	Any         =       compute_perplexity(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
																																			test_perps.append(UpperCamelCase      )

																																			print("""Test perplexity, step"""				,			UpperCamelCase				,			""":"""				,			UpperCamelCase      )
            # 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()				,			UpperCamelCase      )
							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  SCREAMING_SNAKE_CASE_       ()      ->       List[Any]:
							lowerCamelCase__					:	Union[str, Any]         =       argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""      )

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

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

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

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

							parser.add_argument("""--max_steps"""				,			default=1000				,			type=UpperCamelCase				,			help="""To calculate training epochs"""      )

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

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

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

							parser.add_argument(
							    """--min_len"""				,			default=1026				,			type=UpperCamelCase				,			help="""The minimum length of the article to be used as objective set"""      )

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

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

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

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

							# Load train data for secondary learner
							lowerCamelCase__					:	int         =       joblib.load("""data/IGF_values.jbl"""      )

							# Train secondary learner
							lowerCamelCase__					:	List[Any]         =       training_secondary_learner(
							    UpperCamelCase				,			secondary_learner_max_epochs=15				,			secondary_learner_batch_size=128				,			eval_freq=100				,			igf_model_path="""igf_model.pt"""				,			)

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

							# Generate train and test data to train and evaluate gpt2 model
							lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       generate_datasets(
							    context_len=32				,			file="""data/tokenized_stories_train_wikitext103.jbl"""				,			number=100				,			min_len=1026				,			trim=UpperCamelCase      )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			context_len=32				,			max_steps=1000				,			batch_size=16				,			threshold=1.0				,			recopy_model=UpperCamelCase				,			secondary_learner=UpperCamelCase				,			eval_interval=10				,			finetuned_model_name="""gpt2_finetuned.pt"""				,			)


if __name__ == "__main__":
			main()


"""simple docstring"""






import functools



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							len(lowerCamelCase       )
				UpperCAmelCase__             =							len(lowerCamelCase       )

				@functools.cache
				def min_distance(lowerCamelCase					,			lowerCamelCase       ) -> int:
								# if first word index is overflow - delete all from the second word
								if indexa >= len_worda:
												return len_worda - indexa
								# if second word index is overflow - delete all from the first word
								if indexa >= len_worda:
												return len_worda - indexa
								UpperCAmelCase__             =							int(worda[indexa] != worda[indexa]       )  # current letters not identical
								return min(
								    1 + min_distance(indexa + 1					,			lowerCamelCase       )					,			1 + min_distance(lowerCamelCase					,			indexa + 1       )					,			diff + min_distance(indexa + 1					,			indexa + 1       )					,			)

				return min_distance(0					,			0       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



