


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     lowercase	(    a=32     , a=10     , a=100     , a=1026     , a=True     , a="data/tokenized_stories_train_wikitext103.jbl"     , a="igf_context_pairs.jbl"     , ):



		'''simple docstring'''




		set_seed(3 )
		# generate train_data and objective_set
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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?
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

		# load pretrained model
		SCREAMING_SNAKE_CASE_				:Dict				     =						load_gpta("gpt2" ).to(a )
		print("computing perplexity on objective set" )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						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     lowercase	(    a     , a=15     , a=128     , a=100     , a="igf_model.pt"     , ):



		'''simple docstring'''




		set_seed(42 )

		# Load pre-trained model
		SCREAMING_SNAKE_CASE_				:int				     =						GPTaLMHeadModel.from_pretrained("gpt2" )

		# Initialize secondary learner to use embedding weights of model
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						SecondaryLearner(a )

		# Train secondary learner
		SCREAMING_SNAKE_CASE_				:Any				     =						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     lowercase	(    a     , a     , a     , a=32     , a=1000     , a=16     , a=1.0     , a=recopy_gpta     , a=None     , a=10     , a="gpt2_finetuned.pt"     , ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
		SCREAMING_SNAKE_CASE_				:List[str]				     =						RandomSampler(a )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						DataLoader(a     , sampler=a )

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

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

		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		SCREAMING_SNAKE_CASE_				:str				     =						[]

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

						if secondary_learner is not None:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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:
										SCREAMING_SNAKE_CASE_				:str				     =						-1
								if predicted_q < threshold:
										SCREAMING_SNAKE_CASE_				:Optional[int]				     =						False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu() ) )
								SCREAMING_SNAKE_CASE_				:Optional[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()
								SCREAMING_SNAKE_CASE_				:str				     =						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:
										SCREAMING_SNAKE_CASE_				:Tuple				     =						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     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						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
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						joblib.load("data/IGF_values.jbl" )

		# Train secondary learner
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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
		SCREAMING_SNAKE_CASE_				:Tuple				     =						GPTaLMHeadModel.from_pretrained("gpt2" )
		set_seed(42 )

		# Generate train and test data to train and evaluate gpt2 model
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						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()










import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     Any=-1):
				# in NER datasets, the last column is usually reserved for NER label
				SCREAMING_SNAKE_CASE_				:Tuple				     =						label_idx






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
						SCREAMING_SNAKE_CASE_				:int				     =						[]
						for line in f:
								if line.startswith("-DOCSTART-") or line == "" or line == "\n":
										if words:
												examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
												guid_index += 1
												SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
												SCREAMING_SNAKE_CASE_				:Any				     =						[]
								else:
										SCREAMING_SNAKE_CASE_				:int				     =						line.split(" ")
										words.append(splits[0])
										if len(UpperCAmelCase) > 1:
												labels.append(splits[self.label_idx].replace("\n"   ,  ""))
										else:
												# Examples could have no label for mode = "test"
												labels.append("O")
						if words:
								examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						0
				for line in test_input_reader:
						if line.startswith("-DOCSTART-") or line == "" or line == "\n":
								writer.write(UpperCAmelCase)
								if not preds_list[example_id]:
										example_id += 1
						elif preds_list[example_id]:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
								writer.write(UpperCAmelCase)
						else:
								logger.warning("Maximum sequence length exceeded: No prediction for '%s'."   ,  line.split()[0])







		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Any				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						["O"] + labels
						return labels
				else:
						return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict):
				# in CONLL2003 dataset chunk column is second-to-last
				super().__init__(label_idx=-2)







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						f.read().splitlines()
						if "O" not in labels:
								SCREAMING_SNAKE_CASE_				:Dict				     =						["O"] + labels
						return labels
				else:
						return [
						    "O",
						    "B-ADVP",
						    "B-INTJ",
						    "B-LST",
						    "B-PRT",
						    "B-NP",
						    "B-SBAR",
						    "B-VP",
						    "B-ADJP",
						    "B-CONJP",
						    "B-PP",
						    "I-ADVP",
						    "I-INTJ",
						    "I-LST",
						    "I-PRT",
						    "I-NP",
						    "I-SBAR",
						    "I-VP",
						    "I-ADJP",
						    "I-CONJP",
						    "I-PP",
						]




class 						_UpperCAmelCase      (     lowercase							):






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Union[Split, str]):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						mode.value
				SCREAMING_SNAKE_CASE_				:List[str]				     =						os.path.join(UpperCAmelCase   ,  F"{mode}.txt")
				SCREAMING_SNAKE_CASE_				:Dict				     =						1
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[]

				with open(UpperCAmelCase   ,  encoding="utf-8") as f:
						for sentence in parse_incr(UpperCAmelCase):
								SCREAMING_SNAKE_CASE_				:List[str]				     =						[]
								SCREAMING_SNAKE_CASE_				:int				     =						[]
								for token in sentence:
										words.append(token["form"])
										labels.append(token["upos"])
								assert len(UpperCAmelCase) == len(UpperCAmelCase)
								if words:
										examples.append(InputExample(guid=F"{mode}-{guid_index}"   ,  words=UpperCAmelCase   ,  labels=UpperCAmelCase))
										guid_index += 1
				return examples






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     TextIO   ,  UpperCAmelCase				:     List):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for sentence in parse_incr(UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						preds_list[example_id]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						""
						for token in sentence:
								out += F"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
						out += "\n"
						writer.write(UpperCAmelCase)
						example_id += 1







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				if path:
						with open(UpperCAmelCase   ,  "r") as f:
								return f.read().splitlines()
				else:
						return [
						    "ADJ",
						    "ADP",
						    "ADV",
						    "AUX",
						    "CCONJ",
						    "DET",
						    "INTJ",
						    "NOUN",
						    "NUM",
						    "PART",
						    "PRON",
						    "PROPN",
						    "PUNCT",
						    "SCONJ",
						    "SYM",
						    "VERB",
						    "X",
						]






