


'''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 							__magic_name__(						lowerCamelCase=3_2,				lowerCamelCase=1_0,				lowerCamelCase=1_0_0,				lowerCamelCase=1_0_2_6,				lowerCamelCase=True,				lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl",				lowerCamelCase="igf_context_pairs.jbl",				):
      set_seed(3)
      # generate train_data and objective_set
      __lowerCAmelCase       ,					__lowerCAmelCase							       =				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?
      __lowerCAmelCase							       =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')

      # load pretrained model
      __lowerCAmelCase							       =				load_gpta('''gpt2''').to(lowerCamelCase)
      print('''computing perplexity on objective set''')
      __lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase,				lowerCamelCase=1_5,				lowerCamelCase=1_2_8,				lowerCamelCase=1_0_0,				lowerCamelCase="igf_model.pt",				):
      set_seed(4_2)

      # Load pre-trained model
      __lowerCAmelCase							       =				GPTaLMHeadModel.from_pretrained('''gpt2''')

      # Initialize secondary learner to use embedding weights of model
      __lowerCAmelCase							       =				SecondaryLearner(lowerCamelCase)

      # Train secondary learner
      __lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase=3_2,				lowerCamelCase=1_0_0_0,				lowerCamelCase=1_6,				lowerCamelCase=1.0,				lowerCamelCase=recopy_gpta,				lowerCamelCase=None,				lowerCamelCase=1_0,				lowerCamelCase="gpt2_finetuned.pt",				):
      __lowerCAmelCase							       =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
      __lowerCAmelCase							       =				RandomSampler(lowerCamelCase)
      __lowerCAmelCase							       =				DataLoader(lowerCamelCase,				sampler=lowerCamelCase)

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

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

      __lowerCAmelCase							       =				[]
      __lowerCAmelCase							       =				[]

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

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

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

      # Train secondary learner
      __lowerCAmelCase							       =				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
      __lowerCAmelCase							       =				GPTaLMHeadModel.from_pretrained('''gpt2''')
      set_seed(4_2)

      # Generate train and test data to train and evaluate gpt2 model
      __lowerCAmelCase       ,					__lowerCAmelCase							       =				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'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

