


'''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       _snake_case     (  A=32				,					A=10				,					A=100				,					A=1026				,					A=True				,					A="data/tokenized_stories_train_wikitext103.jbl"				,					A="igf_context_pairs.jbl"				,					)		->					Optional[int]:
							set_seed(3 )
							# generate train_data and objective_set
							lowerCAmelCase__     ,      lowerCAmelCase__      =			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__      =			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )

							# load pretrained model
							lowerCAmelCase__      =			load_gpta('''gpt2''' ).to(__SCREAMING_SNAKE_CASE )
							print('''computing perplexity on objective set''' )
							lowerCAmelCase__      =			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       _snake_case     (  A				,					A=15				,					A=128				,					A=100				,					A="igf_model.pt"				,					)		->					Tuple:
							set_seed(42 )

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

							# Initialize secondary learner to use embedding weights of model
							lowerCAmelCase__      =			SecondaryLearner(__SCREAMING_SNAKE_CASE )

							# Train secondary learner
							lowerCAmelCase__      =			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       _snake_case     (  A				,					A				,					A				,					A=32				,					A=1000				,					A=16				,					A=1.0				,					A=recopy_gpta				,					A=None				,					A=10				,					A="gpt2_finetuned.pt"				,					)		->					Tuple:
							lowerCAmelCase__      =			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
							lowerCAmelCase__      =			RandomSampler(__SCREAMING_SNAKE_CASE )
							lowerCAmelCase__      =			DataLoader(__SCREAMING_SNAKE_CASE				,					sampler=__SCREAMING_SNAKE_CASE )

							lowerCAmelCase__      =			max_steps // (len(__SCREAMING_SNAKE_CASE )) + 1
							lowerCAmelCase__      =			0
							lowerCAmelCase__      =			torch.zeros((1, context_len)				,					dtype=torch.long				,					device=__SCREAMING_SNAKE_CASE )
							lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			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__      =			[]
							lowerCAmelCase__      =			0

							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			[]

							# Compute the performance of the transformer model at the beginning
							lowerCAmelCase__      =			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__      =			random.randint(0				,					example.size(2 ) - context_len - 1 )
																					lowerCAmelCase__      =			example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					lowerCAmelCase__      =			model(__SCREAMING_SNAKE_CASE				,					labels=__SCREAMING_SNAKE_CASE )
																					lowerCAmelCase__      =			True

																					if secondary_learner is not None:
																												lowerCAmelCase__      =			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__      =			-1
																												if predicted_q < threshold:
																																			lowerCAmelCase__      =			False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												lowerCAmelCase__      =			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__      =			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__      =			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       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			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__      =			joblib.load('''data/IGF_values.jbl''' )

							# Train secondary learner
							lowerCAmelCase__      =			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__      =			GPTaLMHeadModel.from_pretrained('''gpt2''' )
							set_seed(42 )

							# Generate train and test data to train and evaluate gpt2 model
							lowerCAmelCase__     ,      lowerCAmelCase__      =			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()





'''simple docstring'''






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def       _snake_case     (  A				,					A )		->					List[Any]:
							lowerCAmelCase__      =			[]
							for part_id in partition_order:
														lowerCAmelCase__      =			df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
														for row_idx, row in enumerate(A ):
																					expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
							return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
							# that each partition can hold 2 rows.
							spark_builder._repartition_df_if_needed(max_shard_size=16 )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[int]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(2 )
							lowerCAmelCase__      =			[1, 0]
							lowerCAmelCase__      =			_generate_iterable_examples(A				,					A )  # Reverse the partitions.
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					A )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(1 )
							lowerCAmelCase__      =			SparkExamplesIterable(A )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(A ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Union[str, Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch('''numpy.random.Generator''' ) as generator_mock:
														lowerCAmelCase__      =			lambda A : x.reverse()
														lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[2, 1, 0] )

														lowerCAmelCase__      =			SparkExamplesIterable(A ).shuffle_data_sources(A )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(A ):
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
																					assert row_id == expected_row_id
																					assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=0				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=1				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# Choose a small max_shard_size for maximum partitioning.
							spark_builder._repartition_df_if_needed(max_shard_size=1 )
							# The new number of partitions should not be greater than the number of rows.
							assert spark_builder.df.rdd.getNumPartitions() == 100