






import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def __A    (      __lowerCAmelCase	)->  Dict:  # picklable for multiprocessing





						"""simple docstring"""



						return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __A    (      )->  List[Any]:





						"""simple docstring"""



						with parallel_backend('spark'	):
												assert ParallelBackendConfig.backend_name == "spark"

						_UpperCAmelCase									=			[1, 2, 3]
						with pytest.raises(__lowerCAmelCase	):
												with parallel_backend('unsupported backend'	):
																		map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=2	)

						with pytest.raises(__lowerCAmelCase	):
												with parallel_backend('unsupported backend'	):
																		map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=-1	)


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc' ,		[2, -1]	)
def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			[1, 2]
						_UpperCAmelCase									=			{'a': 1, 'b': 2}
						_UpperCAmelCase									=			{'a': [1, 2], 'b': [3, 4]}
						_UpperCAmelCase									=			{'a': {'1': 1}, 'b': 2}
						_UpperCAmelCase									=			{'a': 1, 'b': 2, 'c': 3, 'd': 4}
						_UpperCAmelCase									=			[2, 3]
						_UpperCAmelCase									=			{'a': 2, 'b': 3}
						_UpperCAmelCase									=			{'a': [2, 3], 'b': [4, 5]}
						_UpperCAmelCase									=			{'a': {'1': 2}, 'b': 3}
						_UpperCAmelCase									=			{'a': 2, 'b': 3, 'c': 4, 'd': 5}

						with parallel_backend('spark'	):
												assert map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=__lowerCAmelCase	) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=__lowerCAmelCase	) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=__lowerCAmelCase	) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=__lowerCAmelCase	) == expected_map_nested_sa
												assert map_nested(__lowerCAmelCase ,		__lowerCAmelCase ,		num_proc=__lowerCAmelCase	) == expected_map_nested_sa




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_     (  snake_case=32			,							snake_case=10			,							snake_case=1_00			,							snake_case=10_26			,							snake_case=True			,							snake_case="data/tokenized_stories_train_wikitext103.jbl"			,							snake_case="igf_context_pairs.jbl"			,							)  ->				Union[str, Any]:
       set_seed(3     )
       # generate train_data and objective_set
       lowercase__			,						lowercase__:   List[str]  			=   generate_datasets(
           snake_case			,							snake_case			,							number=snake_case			,							min_len=10_26			,							trim=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?
       lowercase__:   Optional[Any]  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     ).item()
       print('perplexity on objective set:'			,							snake_case     )

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

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




def    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

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

       # Initialize secondary learner to use embedding weights of model
       lowercase__:   Any  			=   SecondaryLearner(snake_case     )

       # Train secondary learner
       lowercase__:   Tuple  			=   train_secondary_learner(
           snake_case			,							snake_case			,							max_epochs=snake_case			,							batch_size=snake_case			,							eval_freq=1_00			,							igf_model_path=snake_case			,							)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

       lowercase__:   int  			=   max_steps // (len(snake_case     )) + 1
       lowercase__:   Union[str, Any]  			=   0
       lowercase__:   Optional[Any]  			=   torch.zeros((1, context_len)			,							dtype=torch.long			,							device=snake_case     )
       lowercase__			,						lowercase__			,						lowercase__:   Union[str, Any]  			=   recopy_model(snake_case			,							snake_case			,							snake_case     )

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

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

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

                     if secondary_learner is not None:
                            lowercase__:   Optional[Any]  			=   secondary_learner.forward(
                                torch.tensor(snake_case			,							dtype=torch.long			,							device=snake_case     ).unsqueeze(0     )     )[0].item()
                            observed_qs.append(float(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:
                                   lowercase__:   Optional[Any]  			=   -1
                            if predicted_q < threshold:
                                   lowercase__:   str  			=   False

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

                                   print('Test perplexity, step'			,							snake_case			,							':'			,							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()			,							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    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'     )

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

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

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

       parser.add_argument(
           '--context_len'			,							default=32			,							type=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=1_00			,							type=snake_case			,							help='number of articles that are long enough to be used as our objective set'			,							)
       parser.add_argument(
           '--eval_freq'			,							default=1_00			,							type=snake_case			,							help='secondary model evaluation is triggered at eval_freq'     )

       parser.add_argument('--max_steps'			,							default=10_00			,							type=snake_case			,							help='To calculate training epochs'     )

       parser.add_argument(
           '--secondary_learner_batch_size'			,							default=1_28			,							type=snake_case			,							help='batch size of training data for secondary learner'			,							)

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

       parser.add_argument(
           '--eval_interval'			,							default=10			,							type=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=1_00			,							type=snake_case			,							help='The number of examples split to be used as objective_set/test_data'     )

       parser.add_argument(
           '--min_len'			,							default=10_26			,							type=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=snake_case			,							help='number of epochs to train secondary learner'     )

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

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

       parser.add_argument(
           '--recopy_model'			,							default=snake_case			,							type=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=1_00			,							min_len=10_26			,							trim=snake_case			,							data_file='data/tokenized_stories_train_wikitext103.jbl'			,							igf_data_file='igf_context_pairs.jbl'			,							)

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

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           snake_case			,							secondary_learner_max_epochs=15			,							secondary_learner_batch_size=1_28			,							eval_freq=1_00			,							igf_model_path='igf_model.pt'			,							)

       # load pretrained gpt2 model
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

       # Generate train and test data to train and evaluate gpt2 model
       lowercase__			,						lowercase__:   Tuple  			=   generate_datasets(
           context_len=32			,							file='data/tokenized_stories_train_wikitext103.jbl'			,							number=1_00			,							min_len=10_26			,							trim=snake_case     )

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           snake_case			,							snake_case			,							snake_case			,							context_len=32			,							max_steps=10_00			,							batch_size=16			,							threshold=1.0			,							recopy_model=snake_case			,							secondary_learner=snake_case			,							eval_interval=10			,							finetuned_model_name='gpt2_finetuned.pt'			,							)


if __name__ == "__main__":
 main()


