






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      _snake_case     (__lowercase=32     ,   __lowercase=10     ,   __lowercase=100     ,   __lowercase=1026     ,   __lowercase=True     ,   __lowercase="data/tokenized_stories_train_wikitext103.jbl"     ,   __lowercase="igf_context_pairs.jbl"     ,   ):
   set_seed(3)
   # generate train_data and objective_set
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       __lowercase     ,   __lowercase     ,   number=__lowercase     ,   min_len=1026     ,   trim=__lowercase)
   # 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_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

   # load pretrained model
   UpperCamelCase_						=  load_gpta('gpt2').to(__lowercase)
   print('computing perplexity on objective set')
   UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase).item()
   print('perplexity on objective set:'     ,   __lowercase)

   # collect igf pairs and save to file demo.jbl
   collect_objective_set(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()






def      _snake_case     (__lowercase     ,   __lowercase=15     ,   __lowercase=128     ,   __lowercase=100     ,   __lowercase="igf_model.pt"     ,   ):
   set_seed(42)

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase_						=  SecondaryLearner(__lowercase)

   # Train secondary learner
   UpperCamelCase_						=  train_secondary_learner(
       __lowercase     ,   __lowercase     ,   max_epochs=__lowercase     ,   batch_size=__lowercase     ,   eval_freq=100     ,   igf_model_path=__lowercase     ,   )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=32     ,   __lowercase=1000     ,   __lowercase=16     ,   __lowercase=1.0     ,   __lowercase=recopy_gpta     ,   __lowercase=None     ,   __lowercase=10     ,   __lowercase="gpt2_finetuned.pt"     ,   ):
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   UpperCamelCase_						=  RandomSampler(__lowercase)
   UpperCamelCase_						=  DataLoader(__lowercase     ,   sampler=__lowercase)

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

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

   UpperCamelCase_						=  []
   UpperCamelCase_						=  []

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

         if secondary_learner is not None:
            UpperCamelCase_						=  secondary_learner.forward(
                torch.tensor(__lowercase     ,   dtype=torch.long     ,   device=__lowercase).unsqueeze(0))[0].item()
            observed_qs.append(float(__lowercase))

            # 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_						=  -1
            if predicted_q < threshold:
               UpperCamelCase_						=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()))
            UpperCamelCase_						=  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_						=  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_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
               test_perps.append(__lowercase)

               print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
            # 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()     ,   __lowercase)
   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      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

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

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

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

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

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

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

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

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

   parser.add_argument(
       '--min_len'     ,   default=1026     ,   type=__lowercase     ,   help='The minimum length of the article to be used as objective set')

   parser.add_argument(
       '--secondary_learner_max_epochs'     ,   default=15     ,   type=__lowercase     ,   help='number of epochs to train secondary learner')

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

   parser.add_argument(
       '--threshold'     ,   default=1.0     ,   type=__lowercase     ,   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=__lowercase     ,   help='finetuned_model_name')

   parser.add_argument(
       '--recopy_model'     ,   default=__lowercase     ,   type=__lowercase     ,   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=__lowercase     ,   data_file='data/tokenized_stories_train_wikitext103.jbl'     ,   igf_data_file='igf_context_pairs.jbl'     ,   )

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

   # Train secondary learner
   UpperCamelCase_						=  training_secondary_learner(
       __lowercase     ,   secondary_learner_max_epochs=15     ,   secondary_learner_batch_size=128     ,   eval_freq=100     ,   igf_model_path='igf_model.pt'     ,   )

   # load pretrained gpt2 model
   UpperCamelCase_						=  GPTaLMHeadModel.from_pretrained('gpt2')
   set_seed(42)

   # Generate train and test data to train and evaluate gpt2 model
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       context_len=32     ,   file='data/tokenized_stories_train_wikitext103.jbl'     ,   number=100     ,   min_len=1026     ,   trim=__lowercase)

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


if __name__ == "__main__":
 main()










import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def __init__(  self    :				int       ,	snake_case    :				Optional[Any]       ,	snake_case    :				Optional[Any]=13       ,	snake_case    :				str=7       ,	snake_case    :				Optional[Any]=True       ,	snake_case    :				int=True       ,	snake_case    :				str=True       ,	snake_case    :				List[str]=True       ,	snake_case    :				Optional[Any]=99       ,	snake_case    :				Optional[int]=32       ,	snake_case    :				Optional[int]=5       ,	snake_case    :				Optional[Any]=4       ,	snake_case    :				Optional[Any]=37       ,	snake_case    :				str="gelu"       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				int=512       ,	snake_case    :				Union[str, Any]=16       ,	snake_case    :				Optional[Any]=2       ,	snake_case    :				int=0.02       ,	snake_case    :				str=4       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				parent
											SCREAMING_SNAKE_CASE							:     str						=				batch_size
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				seq_length
											SCREAMING_SNAKE_CASE							:     int						=				is_training
											SCREAMING_SNAKE_CASE							:     List[str]						=				use_attention_mask
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				use_token_type_ids
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				use_labels
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Any						=				num_hidden_layers
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     Any						=				intermediate_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				hidden_act
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE							:     str						=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE							:     Tuple						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Any						=				type_vocab_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				type_sequence_label_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				initializer_range
											SCREAMING_SNAKE_CASE							:     List[str]						=				num_choices







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				None
											if self.use_attention_mask:
																SCREAMING_SNAKE_CASE							:     Any						=				random_attention_mask([self.batch_size, self.seq_length]	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				DistilBertConfig(
											    vocab_size=self.vocab_size       ,	dim=self.hidden_size       ,	n_layers=self.num_hidden_layers       ,	n_heads=self.num_attention_heads       ,	hidden_dim=self.intermediate_size       ,	hidden_act=self.hidden_act       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	initializer_range=self.initializer_range       ,	tie_weights_=snake_case       ,	)

											return config, input_ids, attention_mask







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				config_and_inputs
											SCREAMING_SNAKE_CASE							:     int						=				{'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


@require_flax
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		int		=		(
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				FlaxDistilBertModelTester(self	)







						@slow
						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											for model_class_name in self.all_model_classes:
																SCREAMING_SNAKE_CASE							:     Dict						=				model_class_name.from_pretrained('distilbert-base-uncased'	)
																SCREAMING_SNAKE_CASE							:     Dict						=				model(np.ones((1, 1)	)	)
																self.assertIsNotNone(snake_case	)


@require_flax
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						@slow
						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
											SCREAMING_SNAKE_CASE							:     Dict						=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
											SCREAMING_SNAKE_CASE							:     str						=				model(snake_case       ,	attention_mask=snake_case	)[0]
											SCREAMING_SNAKE_CASE							:     int						=				(1, 11, 768)
											self.assertEqual(output.shape       ,	snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]	)

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,	snake_case       ,	atol=1E-4	)	)