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    lowerCAmelCase_  (		lowercase:				Optional[int]=32	,       lowercase:				Dict=10	,       lowercase:				str=100	,       lowercase:				Optional[Any]=1_026	,       lowercase:				Union[str, Any]=True	,       lowercase:				Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"	,       lowercase:				List[Any]="igf_context_pairs.jbl"	,       )     ->					List[Any]:







 '''simple docstring'''
 set_seed(3		)
 # generate train_data and objective_set
 _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			generate_datasets(
     lowercase	,       lowercase	,       number=lowercase	,       min_len=1_026	,       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:  Optional[int]    						=			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)

 # load pretrained model
 _UpperCamelCase:  Dict    						=			load_gpta('''gpt2'''		).to(lowercase		)
 print('''computing perplexity on objective set'''		)
 _UpperCamelCase:  Optional[Any]    						=			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    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Tuple=15	,       lowercase:				int=128	,       lowercase:				Optional[Any]=100	,       lowercase:				str="igf_model.pt"	,       )     ->					List[Any]:







 '''simple docstring'''
 set_seed(42		)

 # Load pre-trained model
 _UpperCamelCase:  Any    						=			GPTaLMHeadModel.from_pretrained('''gpt2'''		)

 # Initialize secondary learner to use embedding weights of model
 _UpperCamelCase:  Tuple    						=			SecondaryLearner(lowercase		)

 # Train secondary learner
 _UpperCamelCase:  Tuple    						=			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    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				List[str]	,       lowercase:				Optional[int]	,       lowercase:				Optional[int]=32	,       lowercase:				Union[str, Any]=1_000	,       lowercase:				List[str]=16	,       lowercase:				Tuple=1.0	,       lowercase:				List[str]=recopy_gpta	,       lowercase:				Union[str, Any]=None	,       lowercase:				Any=10	,       lowercase:				List[Any]="gpt2_finetuned.pt"	,       )     ->					Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
 _UpperCamelCase:  Dict    						=			RandomSampler(lowercase		)
 _UpperCamelCase:  Any    						=			DataLoader(lowercase	,       sampler=lowercase		)

 _UpperCamelCase:  Tuple    						=			max_steps // (len(lowercase		)) + 1
 _UpperCamelCase:  str    						=			0
 _UpperCamelCase:  int    						=			torch.zeros((1, context_len)	,       dtype=torch.long	,       device=lowercase		)
 _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			recopy_model(lowercase	,       lowercase	,       lowercase		)

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(lowercase		)
  secondary_learner.eval()
 _UpperCamelCase:  List[str]    						=			[]
 _UpperCamelCase:  List[Any]    						=			0

 _UpperCamelCase:  int    						=			[]
 _UpperCamelCase:  List[Any]    						=			[]

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

   if secondary_learner is not None:
    _UpperCamelCase:  Optional[Any]    						=			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:  Dict    						=			-1
    if predicted_q < threshold:
     _UpperCamelCase:  List[Any]    						=			False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()		)		)
    _UpperCamelCase:  Union[str, Any]    						=			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:  int    						=			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(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    lowerCAmelCase_  (		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			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=1_000	,       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=1_026	,       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=1_026	,       trim=lowercase	,       data_file='''data/tokenized_stories_train_wikitext103.jbl'''	,       igf_data_file='''igf_context_pairs.jbl'''	,       )

 # Load train data for secondary learner
 _UpperCamelCase:  Optional[Any]    						=			joblib.load('''data/IGF_values.jbl'''		)

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

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

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     lowercase	,       lowercase	,       lowercase	,       context_len=32	,       max_steps=1_000	,       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 BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




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



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Tuple    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  str    						=			BertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Any    						=			config_and_inputs
    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			config_and_inputs

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  Optional[Any]    						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2				)

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = True

   lowerCAmelCase   :     List[Any]	      = (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModel.from_pretrained('''bert-base-cased'''				)
    _UpperCamelCase:  int    						=			model(np.ones((1, 1)				)				)
    self.assertIsNotNone(_lowercase				)