"""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 			UpperCAmelCase__       (				lowerCAmelCase__     :str=3_2					,		lowerCAmelCase__     :Dict=1_0					,		lowerCAmelCase__     :List[str]=1_0_0					,		lowerCAmelCase__     :Dict=1_0_2_6					,		lowerCAmelCase__     :Any=True					,		lowerCAmelCase__     :Optional[int]="data/tokenized_stories_train_wikitext103.jbl"					,		lowerCAmelCase__     :Tuple="igf_context_pairs.jbl"					,		) ->    str:
      '''simple docstring'''


      set_seed(3		)
      # generate train_data and objective_set
      lowercase														=	generate_datasets(
          lowerCAmelCase__					,		lowerCAmelCase__					,		number=lowerCAmelCase__					,		min_len=1_0_2_6					,		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?
      lowercase														=	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""		)

      # load pretrained model
      lowercase														=	load_gpta("""gpt2"""		).to(lowerCAmelCase__		)
      print("""computing perplexity on objective set"""		)
      lowercase														=	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 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[str]=1_5					,		lowerCAmelCase__     :List[str]=1_2_8					,		lowerCAmelCase__     :Optional[int]=1_0_0					,		lowerCAmelCase__     :str="igf_model.pt"					,		) ->    List[Any]:
      '''simple docstring'''


      set_seed(4_2		)

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

      # Initialize secondary learner to use embedding weights of model
      lowercase														=	SecondaryLearner(lowerCAmelCase__		)

      # Train secondary learner
      lowercase														=	train_secondary_learner(
          lowerCAmelCase__					,		lowerCAmelCase__					,		max_epochs=lowerCAmelCase__					,		batch_size=lowerCAmelCase__					,		eval_freq=1_0_0					,		igf_model_path=lowerCAmelCase__					,		)

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any=3_2					,		lowerCAmelCase__     :int=1_0_0_0					,		lowerCAmelCase__     :Any=1_6					,		lowerCAmelCase__     :Optional[Any]=1.0					,		lowerCAmelCase__     :Optional[int]=recopy_gpta					,		lowerCAmelCase__     :List[str]=None					,		lowerCAmelCase__     :int=1_0					,		lowerCAmelCase__     :Any="gpt2_finetuned.pt"					,		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""		)
      lowercase														=	RandomSampler(lowerCAmelCase__		)
      lowercase														=	DataLoader(lowerCAmelCase__					,		sampler=lowerCAmelCase__		)

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

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

      lowercase														=	[]
      lowercase														=	[]

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

                  if secondary_learner is not None:
                        lowercase														=	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 == 1_0:
                              lowercase														=	-1
                        if predicted_q < threshold:
                              lowercase														=	False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()		)		)
                        lowercase														=	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()
                        lowercase														=	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:
                              lowercase														=	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 > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  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 			UpperCAmelCase__       (				) ->    str:
      '''simple docstring'''


      lowercase														=	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=3_2					,		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=1_0_0					,		type=lowerCAmelCase__					,		help="""number of articles that are long enough to be used as our objective set"""					,		)
      parser.add_argument(
          """--eval_freq"""					,		default=1_0_0					,		type=lowerCAmelCase__					,		help="""secondary model evaluation is triggered at eval_freq"""		)

      parser.add_argument("""--max_steps"""					,		default=1_0_0_0					,		type=lowerCAmelCase__					,		help="""To calculate training epochs"""		)

      parser.add_argument(
          """--secondary_learner_batch_size"""					,		default=1_2_8					,		type=lowerCAmelCase__					,		help="""batch size of training data for secondary learner"""					,		)

      parser.add_argument(
          """--batch_size"""					,		default=1_6					,		type=lowerCAmelCase__					,		help="""batch size of training data of language model(gpt2) """		)

      parser.add_argument(
          """--eval_interval"""					,		default=1_0					,		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=1_0_0					,		type=lowerCAmelCase__					,		help="""The number of examples split to be used as objective_set/test_data"""		)

      parser.add_argument(
          """--min_len"""					,		default=1_0_2_6					,		type=lowerCAmelCase__					,		help="""The minimum length of the article to be used as objective set"""		)

      parser.add_argument(
          """--secondary_learner_max_epochs"""					,		default=1_5					,		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=3_2					,		max_steps=1_0					,		size_objective_set=1_0_0					,		min_len=1_0_2_6					,		trim=lowerCAmelCase__					,		data_file="""data/tokenized_stories_train_wikitext103.jbl"""					,		igf_data_file="""igf_context_pairs.jbl"""					,		)

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

      # Train secondary learner
      lowercase														=	training_secondary_learner(
          lowerCAmelCase__					,		secondary_learner_max_epochs=1_5					,		secondary_learner_batch_size=1_2_8					,		eval_freq=1_0_0					,		igf_model_path="""igf_model.pt"""					,		)

      # load pretrained gpt2 model
      lowercase														=	GPTaLMHeadModel.from_pretrained("""gpt2"""		)
      set_seed(4_2		)

      # Generate train and test data to train and evaluate gpt2 model
      lowercase														=	generate_datasets(
          context_len=3_2					,		file="""data/tokenized_stories_train_wikitext103.jbl"""					,		number=1_0_0					,		min_len=1_0_2_6					,		trim=lowerCAmelCase__		)

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		context_len=3_2					,		max_steps=1_0_0_0					,		batch_size=1_6					,		threshold=1.0					,		recopy_model=lowerCAmelCase__					,		secondary_learner=lowerCAmelCase__					,		eval_interval=1_0					,		finetuned_model_name="""gpt2_finetuned.pt"""					,		)


if __name__ == "__main__":
       main()







'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__    					=      namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    return (abs(source - target						) / target) < 0.0_1







@pytest.mark.integration
def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		str													=				_TestCommandArgs(dataset=lowerCAmelCase__  ,					all_configs=lowerCAmelCase__  ,					save_infos=lowerCAmelCase__						)
    UpperCAmelCase__			:		Tuple													=				TestCommand(*lowerCAmelCase__						)
    test_command.run()
    UpperCAmelCase__			:		List[Any]													=				os.path.join(lowerCAmelCase__  ,					'''README.md'''						)
    assert os.path.exists(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DatasetInfosDict.from_directory(lowerCAmelCase__						)
    UpperCAmelCase__			:		Any													=				DatasetInfosDict(
        {
            '''default''': DatasetInfo(
                features=Features(
                    {
                        '''tokens''': Sequence(Value('''string'''						)						),
                        '''ner_tags''': Sequence(
                            ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
                        '''langs''': Sequence(Value('''string'''						)						),
                        '''spans''': Sequence(Value('''string'''						)						),
                    }						)  ,					splits=[
                    {
                        '''name''': '''train''',
                        '''num_bytes''': 2_35_15_63,
                        '''num_examples''': 1_00_00,
                    },
                    {
                        '''name''': '''validation''',
                        '''num_bytes''': 23_84_18,
                        '''num_examples''': 10_00,
                    },
                ]  ,					download_size=3_94_06_80  ,					dataset_size=2_58_99_81  ,					)
        }						)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				getattr(dataset_infos['''default''']  ,					lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']  ,					lowerCAmelCase__						)
        if key == "num_bytes":
            assert is_apercent_close(lowerCAmelCase__  ,					lowerCAmelCase__						)
        elif key == "splits":
            assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
            for split in result:
                assert result[split].name == expected[split].name
                assert result[split].num_examples == expected[split].num_examples
                assert is_apercent_close(result[split].num_bytes  ,					expected[split].num_bytes						)
        else:
            result == expected
