


'''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        a__     (					UpperCamelCase_						:      Optional[Any]=32,   UpperCamelCase_						:      Optional[int]=10,   UpperCamelCase_						:      Optional[int]=100,   UpperCamelCase_						:      Union[str, Any]=1_026,   UpperCamelCase_						:      Any=True,   UpperCamelCase_						:      List[str]="data/tokenized_stories_train_wikitext103.jbl",   UpperCamelCase_						:      Tuple="igf_context_pairs.jbl",   ):
   set_seed(3						)
   # generate train_data and objective_set
   UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      generate_datasets(
       UpperCamelCase_,   UpperCamelCase_,   number=UpperCamelCase_,   min_len=1_026,   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?
   UpperCAmelCase__	:Optional[Any]      =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

   # load pretrained model
   UpperCAmelCase__	:Tuple      =      load_gpta('''gpt2'''						).to(UpperCamelCase_						)
   print('''computing perplexity on objective set'''						)
   UpperCAmelCase__	:List[str]      =      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        a__     (					UpperCamelCase_						:      List[Any],   UpperCamelCase_						:      Dict=15,   UpperCamelCase_						:      Tuple=128,   UpperCamelCase_						:      List[str]=100,   UpperCamelCase_						:      Any="igf_model.pt",   ):
   set_seed(42						)

   # Load pre-trained model
   UpperCAmelCase__	:Tuple      =      GPTaLMHeadModel.from_pretrained('''gpt2'''						)

   # Initialize secondary learner to use embedding weights of model
   UpperCAmelCase__	:str      =      SecondaryLearner(UpperCamelCase_						)

   # Train secondary learner
   UpperCAmelCase__	:Any      =      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        a__     (					UpperCamelCase_						:      List[Any],   UpperCamelCase_						:      Any,   UpperCamelCase_						:      List[str],   UpperCamelCase_						:      Optional[Any]=32,   UpperCamelCase_						:      List[Any]=1_000,   UpperCamelCase_						:      List[Any]=16,   UpperCamelCase_						:      Tuple=1.0,   UpperCamelCase_						:      List[Any]=recopy_gpta,   UpperCamelCase_						:      str=None,   UpperCamelCase_						:      str=10,   UpperCamelCase_						:      Any="gpt2_finetuned.pt",   ):
   UpperCAmelCase__	:List[str]      =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
   UpperCAmelCase__	:Optional[Any]      =      RandomSampler(UpperCamelCase_						)
   UpperCAmelCase__	:int      =      DataLoader(UpperCamelCase_,   sampler=UpperCamelCase_						)

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(UpperCamelCase_						)
      secondary_learner.eval()
   UpperCAmelCase__	:Union[str, Any]      =      []
   UpperCAmelCase__	:List[Any]      =      0

   UpperCAmelCase__	:List[str]      =      []
   UpperCAmelCase__	:Union[str, Any]      =      []

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

         if secondary_learner is not None:
            UpperCAmelCase__	:Union[str, Any]      =      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:
               UpperCAmelCase__	:Optional[int]      =      -1
            if predicted_q < threshold:
               UpperCAmelCase__	:Any      =      False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()						)						)
            UpperCAmelCase__	:Dict      =      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()
            UpperCAmelCase__	: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:
               UpperCAmelCase__	:int      =      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        a__     (					):
   UpperCAmelCase__	: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=1_000,   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=1_026,   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=1_026,   trim=UpperCamelCase_,   data_file='''data/tokenized_stories_train_wikitext103.jbl''',   igf_data_file='''igf_context_pairs.jbl''',   )

   # Load train data for secondary learner
   UpperCAmelCase__	:Tuple      =      joblib.load('''data/IGF_values.jbl'''						)

   # Train secondary learner
   UpperCAmelCase__	:Union[str, 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
   UpperCAmelCase__	:Optional[Any]      =      GPTaLMHeadModel.from_pretrained('''gpt2'''						)
   set_seed(42						)

   # Generate train and test data to train and evaluate gpt2 model
   UpperCAmelCase__   ,						UpperCAmelCase__	:Dict      =      generate_datasets(
       context_len=32,   file='''data/tokenized_stories_train_wikitext103.jbl''',   number=100,   min_len=1_026,   trim=UpperCamelCase_						)

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





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 				UpperCAmelCase			(			unittest.TestCase						):


 @slow
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:List[str]      =      TFCamembertModel.from_pretrained('''jplu/tf-camembert-base''' )

    UpperCAmelCase__	:Optional[Any]      =      tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]			,       dtype=tf.intaa			,       )  # J'aime le camembert !"

    UpperCAmelCase__	:Any      =      model(__lowerCamelCase )['''last_hidden_state''']
    UpperCAmelCase__	:Dict      =      tf.TensorShape((1, 1_0, 7_6_8) )
    self.assertEqual(output.shape			,       __lowerCamelCase )
    # compare the actual values for a slice.
    UpperCAmelCase__	:Any      =      tf.convert_to_tensor(
        [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]			,       dtype=tf.floataa			,       )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,       expected_slice.numpy()			,       atol=1e-4 ) )



