




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

       # load pretrained model
       lowerCamelCase   :      Any			 =					load_gpta("gpt2"    ).to(_SCREAMING_SNAKE_CASE    )
       print("computing perplexity on objective set"    )
       lowerCamelCase   :      str			 =					compute_perplexity(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ).item()
       print("perplexity on objective set:" ,_SCREAMING_SNAKE_CASE    )

       # collect igf pairs and save to file demo.jbl
       collect_objective_set(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=15 ,_SCREAMING_SNAKE_CASE=128 ,_SCREAMING_SNAKE_CASE=100 ,_SCREAMING_SNAKE_CASE="igf_model.pt" ,)	-> List[Any]:
       set_seed(42    )

       # Load pre-trained model
       lowerCamelCase   :      str			 =					GPTaLMHeadModel.from_pretrained("gpt2"    )

       # Initialize secondary learner to use embedding weights of model
       lowerCamelCase   :      Union[str, Any]			 =					SecondaryLearner(_SCREAMING_SNAKE_CASE    )

       # Train secondary learner
       lowerCamelCase   :      Dict			 =					train_secondary_learner(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,max_epochs=_SCREAMING_SNAKE_CASE ,batch_size=_SCREAMING_SNAKE_CASE ,eval_freq=100 ,igf_model_path=_SCREAMING_SNAKE_CASE ,)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=1000 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=1.0 ,_SCREAMING_SNAKE_CASE=recopy_gpta ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=10 ,_SCREAMING_SNAKE_CASE="gpt2_finetuned.pt" ,)	-> str:
       lowerCamelCase   :      Optional[int]			 =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"    )
       lowerCamelCase   :      Optional[Any]			 =					RandomSampler(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					DataLoader(_SCREAMING_SNAKE_CASE ,sampler=_SCREAMING_SNAKE_CASE    )

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

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(_SCREAMING_SNAKE_CASE    )
              secondary_learner.eval()
       lowerCamelCase   :      Union[str, Any]			 =					[]
       lowerCamelCase   :      Optional[Any]			 =					0

       lowerCamelCase   :      Union[str, Any]			 =					[]
       lowerCamelCase   :      Union[str, Any]			 =					[]

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

                     if secondary_learner is not None:
                            lowerCamelCase   :      Dict			 =					secondary_learner.forward(
                                torch.tensor(_SCREAMING_SNAKE_CASE ,dtype=torch.long ,device=_SCREAMING_SNAKE_CASE    ).unsqueeze(0    )    )[0].item()
                            observed_qs.append(float(_SCREAMING_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:
                                   lowerCamelCase   :      Tuple			 =					-1
                            if predicted_q < threshold:
                                   lowerCamelCase   :      List[str]			 =					False

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

                                   print("Test perplexity, step" ,_SCREAMING_SNAKE_CASE ,":" ,_SCREAMING_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() ,_SCREAMING_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      (      )	-> Optional[Any]:
       lowerCamelCase   :      List[str]			 =					argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"    )

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

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

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

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

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

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

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

       parser.add_argument(
           "--eval_interval" ,default=10 ,type=_SCREAMING_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=100 ,type=_SCREAMING_SNAKE_CASE ,help="The number of examples split to be used as objective_set/test_data"    )

       parser.add_argument(
           "--min_len" ,default=1026 ,type=_SCREAMING_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=_SCREAMING_SNAKE_CASE ,help="number of epochs to train secondary learner"    )

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

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

       parser.add_argument(
           "--recopy_model" ,default=_SCREAMING_SNAKE_CASE ,type=_SCREAMING_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=100 ,min_len=1026 ,trim=_SCREAMING_SNAKE_CASE ,data_file="data/tokenized_stories_train_wikitext103.jbl" ,igf_data_file="igf_context_pairs.jbl" ,)

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

       # Train secondary learner
       lowerCamelCase   :      Tuple			 =					training_secondary_learner(
           _SCREAMING_SNAKE_CASE ,secondary_learner_max_epochs=15 ,secondary_learner_batch_size=128 ,eval_freq=100 ,igf_model_path="igf_model.pt" ,)

       # load pretrained gpt2 model
       lowerCamelCase   :      Union[str, Any]			 =					GPTaLMHeadModel.from_pretrained("gpt2"    )
       set_seed(42    )

       # Generate train and test data to train and evaluate gpt2 model
       lowerCamelCase ,      lowerCamelCase   :      int			 =					generate_datasets(
           context_len=32 ,file="data/tokenized_stories_train_wikitext103.jbl" ,number=100 ,min_len=1026 ,trim=_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
       main()












import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(lowerCAmelCase__     )    ,				"""Tatoeba directory does not exist."""     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           self.resolver.convert_models(["heb-eng"]     )







    @slow
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.resolver.write_model_card("opus-mt-he-en"     ,  dry_run=UpperCamelCase__     )
           assert mmeta["long_pair"] == "heb-eng"






