


"""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      lowercase			(				A_=32  ,   A_=10  ,   A_=100  ,   A_=1_026  ,   A_=True  ,   A_="data/tokenized_stories_train_wikitext103.jbl"  ,   A_="igf_context_pairs.jbl"  ,   )->      List[str]:
  '''simple docstring'''







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

  # load pretrained model
  a						:		Union[str, Any]      							=			load_gpta("gpt2"       ).to(A_       )
  print("computing perplexity on objective set"       )
  a						:		int      							=			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      lowercase			(				A_  ,   A_=15  ,   A_=128  ,   A_=100  ,   A_="igf_model.pt"  ,   )->      Any:
  '''simple docstring'''







  set_seed(42       )

  # Load pre-trained model
  a						:		int      							=			GPTaLMHeadModel.from_pretrained("gpt2"       )

  # Initialize secondary learner to use embedding weights of model
  a						:		int      							=			SecondaryLearner(A_       )

  # Train secondary learner
  a						:		Optional[Any]      							=			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      lowercase			(				A_  ,   A_  ,   A_  ,   A_=32  ,   A_=1_000  ,   A_=16  ,   A_=1.0  ,   A_=recopy_gpta  ,   A_=None  ,   A_=10  ,   A_="gpt2_finetuned.pt"  ,   )->      Union[str, Any]:
  '''simple docstring'''







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

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

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(A_       )
    secondary_learner.eval()
  a						:		Tuple      							=			[]
  a						:		List[Any]      							=			0

  a						:		str      							=			[]
  a						:		Optional[Any]      							=			[]

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

      if secondary_learner is not None:
        a						:		Tuple      							=			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:
          a						:		List[Any]      							=			-1
        if predicted_q < threshold:
          a						:		Tuple      							=			False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()       )       )
        a						:		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()
        a						:		Dict      							=			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:
          a						:		Dict      							=			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      lowercase			(				)->      Optional[int]:
  '''simple docstring'''







  a						:		Any      							=			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=1_000  ,   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=1_026  ,   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=1_026  ,   trim=A_  ,   data_file="data/tokenized_stories_train_wikitext103.jbl"  ,   igf_data_file="igf_context_pairs.jbl"  ,   )

  # Load train data for secondary learner
  a						:		Union[str, Any]      							=			joblib.load("data/IGF_values.jbl"       )

  # Train secondary learner
  a						:		Optional[Any]      							=			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
  a						:		List[str]      							=			GPTaLMHeadModel.from_pretrained("gpt2"       )
  set_seed(42       )

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

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      A_  ,   A_  ,   A_  ,   context_len=32  ,   max_steps=1_000  ,   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()








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  *lowercase						,  lowercase=None						,  lowercase=None						,  **lowercase )				->       List[str]:
					super().__init__(*lowercase						,  **lowercase )
					lowerCamelCase_						=			eval_examples
					lowerCamelCase_						=			post_process_function



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase = None						,  lowercase=None						,  lowercase = None						,  lowercase = "eval"						,  **lowercase						,  )				->       Dict[str, float]:
					lowerCamelCase_						=			gen_kwargs.copy()
					lowerCamelCase_						=			(
					    gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
					)
					lowerCamelCase_						=			(
					    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
					)
					lowerCamelCase_						=			gen_kwargs

					lowerCamelCase_						=			self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCamelCase_						=			self.get_eval_dataloader(lowercase )
					lowerCamelCase_						=			self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Evaluation"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics
					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )

					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
									# Only the main node write the results by default
									lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase )
									lowerCamelCase_						=			self.compute_metrics(lowercase )

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys() ):
													if not key.startswith(f'{metric_key_prefix}_' ):
																	lowerCamelCase_						=			metrics.pop(lowercase )

									metrics.update(output.metrics )
					else:
									lowerCamelCase_						=			output.metrics

					if self.args.should_log:
									# Only the main node log the results by default
									self.log(lowercase )

					if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report() )

					lowerCamelCase_						=			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase )
					return metrics



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase=None						,  lowercase = "test"						,  **lowercase )				->       Union[str, Any]:
					lowerCamelCase_						=			gen_kwargs.copy()

					lowerCamelCase_						=			self.get_test_dataloader(lowercase )

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCamelCase_						=			self.compute_metrics
					lowerCamelCase_						=			None
					lowerCamelCase_						=			time.time()
					lowerCamelCase_						=			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					try:
									lowerCamelCase_						=			eval_loop(
									    lowercase						,  description="Prediction"						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase						,  metric_key_prefix=lowercase						,  )
					finally:
									lowerCamelCase_						=			compute_metrics

					lowerCamelCase_						=			self.args.eval_batch_size * self.args.world_size
					if f'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[f'{metric_key_prefix}_jit_compilation_time']
					output.metrics.update(
					    speed_metrics(
					        lowercase						,  lowercase						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size )						,  ) )
					if self.post_process_function is None or self.compute_metrics is None:
									return output

					lowerCamelCase_						=			self.post_process_function(lowercase						,  lowercase						,  lowercase						,  "predict" )
					lowerCamelCase_						=			self.compute_metrics(lowercase )

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys() ):
									if not key.startswith(f'{metric_key_prefix}_' ):
													lowerCamelCase_						=			metrics.pop(lowercase )
					metrics.update(output.metrics )
					return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase )


