






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


a__       =     logging.get_logger(__name__)


@dataclass
class    _lowerCAmelCase			:



				"""simple docstring"""



				_lowercase				:   str          =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()      )}      )
				_lowercase				:   str          =  field(
				    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}      )
				_lowercase				:   int          =  field(
				    default=1_28     ,      metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }     ,      )
				_lowercase				:   bool          =  field(
				    default=UpperCAmelCase__     ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}      )



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       self.task_name.lower()


class    _lowerCAmelCase			(		UpperCAmelCase__      ):



				"""simple docstring"""



				_lowercase				:   Union[str, Any]          =  "train"
				_lowercase				:   Any          =  "dev"
				_lowercase				:   Union[str, Any]          =  "test"


class    _lowerCAmelCase			(		UpperCAmelCase__      ):



				"""simple docstring"""



				_lowercase				:   GlueDataTrainingArguments
				_lowercase				:   str
				_lowercase				:   List[InputFeatures]



				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    GlueDataTrainingArguments			,		UpperCamelCase__    :    PreTrainedTokenizerBase			,		UpperCamelCase__    :    Optional[int] = None			,		UpperCamelCase__    :    Union[str, Split] = Split.train			,		UpperCamelCase__    :    Optional[str] = None			,		):






											'''simple docstring'''







											warnings.warn(
											    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
											    """library. You can have a look at this example script for pointers: """
											    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""			,		lowerCamelCase__			,		)
											snake_case__	  =       args
											snake_case__	  =       glue_processors[args.task_name]()
											snake_case__	  =       glue_output_modes[args.task_name]
											if isinstance(lowerCamelCase__			,		lowerCamelCase__):
																		try:
																									snake_case__	  =       Split[mode]
																		except KeyError:
																									raise KeyError("""mode is not a valid split name""")
        # Load data features from cache or dataset file
											snake_case__	  =       os.path.join(
											    cache_dir if cache_dir is not None else args.data_dir			,		F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''			,		)
											snake_case__	  =       self.processor.get_labels()
											if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
											    "RobertaTokenizer",
											    "RobertaTokenizerFast",
											    "XLMRobertaTokenizer",
											    "BartTokenizer",
											    "BartTokenizerFast",
											):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		snake_case__	  =       label_list[2], label_list[1]
											snake_case__	  =       label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											snake_case__	  =       cached_features_file + ".lock"
											with FileLock(lowerCamelCase__):
																		if os.path.exists(lowerCamelCase__) and not args.overwrite_cache:
																									snake_case__	  =       time.time()
																									snake_case__	  =       torch.load(lowerCamelCase__)
																									logger.info(
																									    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''			,		time.time() - start)
																		else:
																									logger.info(F'''Creating features from dataset file at {args.data_dir}''')

																									if mode == Split.dev:
																																snake_case__	  =       self.processor.get_dev_examples(args.data_dir)
																									elif mode == Split.test:
																																snake_case__	  =       self.processor.get_test_examples(args.data_dir)
																									else:
																																snake_case__	  =       self.processor.get_train_examples(args.data_dir)
																									if limit_length is not None:
																																snake_case__	  =       examples[:limit_length]
																									snake_case__	  =       glue_convert_examples_to_features(
																									    lowerCamelCase__			,		lowerCamelCase__			,		max_length=args.max_seq_length			,		label_list=lowerCamelCase__			,		output_mode=self.output_mode			,		)
																									snake_case__	  =       time.time()
																									torch.save(self.features			,		lowerCamelCase__)
																									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																									logger.info(
																									    F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''')



				def __len__(      self    :    Optional[Any]):






											'''simple docstring'''







											return len(self.features)



				def __getitem__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







											return self.features[i]



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return self.label_list








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 _UpperCAmelCase   (	a						:	int=32  ,					a						:	int=10  ,					a						:	Tuple=100  ,					a						:	str=1026  ,					a						:	Union[str, Any]=True  ,					a						:	Optional[int]="data/tokenized_stories_train_wikitext103.jbl"  ,					a						:	Tuple="igf_context_pairs.jbl"  ,					):
							set_seed(3 )
							# generate train_data and objective_set
							snake_case__  , snake_case__	  =       generate_datasets(
							    a  ,					a  ,					number=a  ,					min_len=1026  ,					trim=a )
							# 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?
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

							# load pretrained model
							snake_case__	  =       load_gpta("""gpt2""" ).to(a )
							print("""computing perplexity on objective set""" )
							snake_case__	  =       compute_perplexity(a  ,					a  ,					a ).item()
							print("""perplexity on objective set:"""  ,					a )

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

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

def _UpperCAmelCase   (	a						:	int  ,					a						:	Dict=15  ,					a						:	Optional[int]=128  ,					a						:	str=100  ,					a						:	Optional[int]="igf_model.pt"  ,					):
							set_seed(42 )

							# Load pre-trained model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )

							# Initialize secondary learner to use embedding weights of model
							snake_case__	  =       SecondaryLearner(a )

							# Train secondary learner
							snake_case__	  =       train_secondary_learner(
							    a  ,					a  ,					max_epochs=a  ,					batch_size=a  ,					eval_freq=100  ,					igf_model_path=a  ,					)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str  ,					a						:	List[Any]=32  ,					a						:	Any=1000  ,					a						:	str=16  ,					a						:	Tuple=1.0  ,					a						:	str=recopy_gpta  ,					a						:	Any=None  ,					a						:	Optional[int]=10  ,					a						:	Tuple="gpt2_finetuned.pt"  ,					):
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
							snake_case__	  =       RandomSampler(a )
							snake_case__	  =       DataLoader(a  ,					sampler=a )

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

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

							snake_case__	  =       []
							snake_case__	  =       []

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

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

																												# 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:
																																			snake_case__	  =       -1
																												if predicted_q < threshold:
																																			snake_case__	  =       False

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

																																			print("""Test perplexity, step"""  ,					a  ,					""":"""  ,					a )
            # 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()  ,					a )
							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 _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )

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

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

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

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

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

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

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

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

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

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

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

							parser.add_argument(
							    """--threshold"""  ,					default=1.0  ,					type=a  ,					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=a  ,					help="""finetuned_model_name""" )

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

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

							# Train secondary learner
							snake_case__	  =       training_secondary_learner(
							    a  ,					secondary_learner_max_epochs=15  ,					secondary_learner_batch_size=128  ,					eval_freq=100  ,					igf_model_path="""igf_model.pt"""  ,					)

							# load pretrained gpt2 model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )
							set_seed(42 )

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

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


if __name__ == "__main__":
	main()
