Datasets:

HiTZ
/

Parallel-XNLIvar

from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
import torch
from huggingface_hub import login
import re
import sys
from sklearn.metrics import accuracy_score
import argparse
import json
import pathlib
from typing import List, Dict
import numpy as np
import pandas as pd
import logging
from datetime import datetime
import os

MAX_NEW_TOKENS = 5
TEMPERATURE = 0.3


with open("/scratch/jbengoetxea/phd/XNLIvar/scripts/generative/config.json", "r") as f:
    config = json.load(f)

def parse_args():
    #os.environ['TRANSFORMERS_CACHE'] = '/XXXX-7/users/XXXX-1/metaphor_LLMs/paraphrase_gen/.cache/huggingface/hub'

    parser = argparse.ArgumentParser(
            description="Finetune a transformers model on a text classification task"
        )

    parser.add_argument(
    "--dataset",
    type=str,
    default=None,
    required=True,
    help="Name of the dataset to predict gold_labels",
    choices=["xnli-eu-native", "xnli-eu-var", "xnli-es-native", "xnli-es-var", "xnli-en", "xnli-es", "xnli-eu", "xnli-es-var-no-rep", "xnli-eu-var-no-rep", "xnli-eu-var-less-biz", "xnli-eu-var-less-gip", "xnli-eu-biz", "xnli-eu-gip", "xnli-eu-naf", "xnli-eu-nat-biz", "xnli-eu-nat-gip", "xnli-eu-nat-naf"]
    )
    
    parser.add_argument(
    "--model",
    type=str,
    default=None,
    required=True,
    help="Model name in config", 
    choices=["llama3instruct8", "llama3instruct70", "gemmainstruct9", "gemmainstruct27", "latxainstruct70", "llama3base70"]
    )
    
    parser.add_argument(
    "--output_dir",
    type=str,
    default=None,
    required=True,
    help="Output path to dump predictions"
    )
    
    parser.add_argument(
    "--task",
    type=str,
    default=None,
    required=True,
    help="Type of task formulation",
    choices=["binary", "trilabel", "qa-zero", "qa-few"]
    )
    
    parser.add_argument(
    "--prompt_type",
    type=str,
    default=None,
    required=True,
    help="Type of prompt"
    )
    
    parser.add_argument(
    "--paraphrases",
    action="store_true",
    required=False,
    help="Dataset with paraphrases generated automatically"
    )
    
    parser.add_argument(
    "--paraphrase_source",
    type=str,
    default=None,
    required=False,
    help="Model used to generate paraphrases"
    )
    
    args = parser.parse_args()
    
    return args

def load_dataset(data_path: str) -> pd.DataFrame:
    df = None
    extension = pathlib.Path(data_path).suffix
    if extension.endswith("json"):
        df = pd.read_json(data_path)
    elif extension.endswith("jsonl"):
        df = pd.read_json(data_path, lines=True)
    elif extension.endswith("tsv"):
        df = pd.read_csv(data_path, sep="\t")
    else:
        df = pd.read_csv(data_path)
        
    return df

def dump_predictions(out_path: str, premises: List, hypotheses: List, gold_labels: List, predictions: List, paraphrased_sents=None):
    if paraphrased_sents:
        with open(out_path, "w") as o:
            o.write("premise\thypothesis\tgold_label\tprediction\tparaphrased_sentence\n") 
            for p, h, g, pr, paraph in zip(premises, hypotheses, gold_labels, predictions, paraphrased_sents):
                o.write(f"{p}\t{h}\t{g}\t{pr}\t{paraph}\n")
    else:
        with open(out_path, "w") as o:
            o.write("premise\thypothesis\tgold_label\tprediction\n") 
            for p, h, g, pr in zip(premises, hypotheses, gold_labels, predictions):
                o.write(f"{p}\t{h}\t{g}\t{pr}\n")
            
    print(f"{len(predictions)} Predictions stored in {out_path}")
    
def map_labels(predictions: List[str], label_mapping: Dict):
    predictions_clean = [pred.strip("<>.,") for pred in predictions.lower().split()]
    for pred in predictions_clean:
        for label in label_mapping:
            label_lower = label.lower()
            # Allow partial matching in both directions
            if pred in label_lower or label_lower in pred:
                return label_mapping[label]
    return "unk"
        
def get_column_values(df, col_id):
        return df[col_id].tolist()


def map_labels_to_string(labels: List):
    label_strings = []
    for label in labels:
        if label == 0: 
            label_strings.append("entailment")
        elif label == 1: 
            label_strings.append("neutral")
        else: 
            label_strings.append("contradiction")
        
    return label_strings

def main():
    
    args = parse_args()

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir, exist_ok=True)
        
    logger_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.log")
    
    logger = logging.getLogger(__name__)
    logging.basicConfig(filename=os.path.join(logger_path), encoding='utf-8', level=logging.INFO)

    # Disable compilation (to avoid recompile_limit errors)
    #torch._dynamo.disable()
    #torch._dynamo.config.suppress_errors = True
    #torch._dynamo.config.recompile_limit = 100 

    login(token='LOGIN_TOKEN') # Add hf login token
    model_id = config.get("models", {}).get(args.model, "")
    logger.info(f"Model used: {model_id}")
    logger.info(f"Prompt task: {args.task}")
    logger.info(f"Dataset with paraphrases: {args.paraphrases}")
    logger.info(f"Prompt config: {args.prompt_type}")
    device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    logger.info(f"Device in use: {device}")
    

    # TORCH_LOGS=recompiles

    datasets_config = config.get("datasets", {})
    prompt_config = config.get("prompts", {}).get(args.task, {})
    
    print(args.task)
    print(datasets_config.get(args.dataset, {}).get("prompts", []))
    
    # Ensure trilabel setup only for Meta4XNLI 
    assert args.task in datasets_config.get(args.dataset, {}).get("prompts", [])
    
    if args.paraphrases:
        data_path =  datasets_config.get(args.dataset, {}).get("data_path_paraphrase", "")
    else:
        data_path =  datasets_config.get(args.dataset, {}).get("data_path", "")

    logger.info(f"Dataset loaded from: {data_path}")
    df = load_dataset(data_path)
    logger.info(f"Loaded samples: {len(df)}")
    premises = get_column_values(df, datasets_config.get(args.dataset, "").get("prem_col", ""))
    hypotheses = get_column_values(df, datasets_config.get(args.dataset, "").get("hyp_col", ""))
    if args.paraphrases:
        gold_labels = get_column_values(df, "gold_label")
    else:
        gold_labels = [l for l in get_column_values(df, datasets_config.get(args.dataset, "").get("label_col", ""))]
    
    print(gold_labels)

    gold_labels = map_labels_to_string(gold_labels)
    print(gold_labels)

    labels = list(set(gold_labels))

    set_seed(5)

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    

    print("MODEL ID:", model_id)


    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)

    print("here i am")
    
    tokenizer.pad_token_id = tokenizer.eos_token_id

    predictions = []
    for p, h, l in zip(premises, hypotheses, gold_labels):
        preffix_prompt = prompt_config.get(args.prompt_type, {}).get("preffix", "")
        print(preffix_prompt)
        print(args.prompt_type)
        if args.prompt_type == "chain":
            prompt = preffix_prompt + f"\n Premise: {p}\n Hypothesis: {h}\n Answer: "
            logger.info(f"Prompt: {prompt}")
        else:
            prompt = preffix_prompt + f" {p} -> {h}: "
            logger.info(f"Prompt: {prompt}")
        
        
        label_mappings = prompt_config.get(args.prompt_type, {}).get("label_mapping")
        
        logger.info(f"Label mappings: {label_mappings}")
        
        inputs = tokenizer([prompt], return_tensors="pt").to(device)
        
        logger.info(f"{p}\t{h}\t{l}")
                
    
        


        # #################
        # input_text = "Write me a poem about Machine Learning."
        # input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

        # outputs = model.generate(**input_ids, max_new_tokens=32)
        # print(tokenizer.decode(outputs[0]))
        # #################



        
        outputs = model.generate(**inputs, max_new_tokens=MAX_NEW_TOKENS, return_dict_in_generate=True, output_scores=True, temperature=TEMPERATURE)
    
        transition_scores = model.compute_transition_scores(
            outputs.sequences, outputs.scores, normalize_logits=True
        )
        logger.info(f"{outputs.sequences}\t{outputs.scores}")
        
        
        #print(f"transition scores: {transition_scores}", flush=True)

        #print(f"transition scores: {transition_scores}", flush=True)
        # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
        # encoder-decoder models, like BART or T5.
        input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
        generated_tokens = outputs.sequences[:, input_length:]
        for tok, score in zip(generated_tokens[0], transition_scores[0]):
            # | token | token string | log probability | probability
            logger.info(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score}")
            #logger.info(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
            #o.write(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")


        answers = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
        logger.info(f"Answers: {answers}, split: {answers.split()}")
        logger.info(f"Mapped label: {map_labels(answers, label_mappings)}")
        predictions.append(map_labels(answers, label_mappings))
        logger.info("Label added to predictions.")
        
            
    logger.debug(gold_labels[:5], predictions[:5], flush=True)
    assert len(gold_labels) == len(predictions)
    logger.info(f"Gold: {len(gold_labels)}, Pred: {len(predictions)}")
    
   
    predictions_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.tsv")
    
    if args.paraphrases:
        paraphrased_sents = df.iloc[:, -1].tolist()
        logger.info(f"Dumping predictions with paraphrased sentences, met location: {list(df.columns)[-1]}")
        dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions, paraphrased_sents)    
    else:
        dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions)
    
    logger.info(f"Predictions dumped to {predictions_path}")
    
    
    
    accuracy = accuracy_score(gold_labels, predictions, normalize=True)
    logger.info(f"Accuracy {len(gold_labels)}, {len(predictions)}: {accuracy}\n")
    


if __name__ == "__main__":
    main()

	from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
	import torch
	from huggingface_hub import login
	import re
	import sys
	from sklearn.metrics import accuracy_score
	import argparse
	import json
	import pathlib
	from typing import List, Dict
	import numpy as np
	import pandas as pd
	import logging
	from datetime import datetime
	import os

	MAX_NEW_TOKENS = 5
	TEMPERATURE = 0.3


	with open("/scratch/jbengoetxea/phd/XNLIvar/scripts/generative/config.json", "r") as f:
	config = json.load(f)

	def parse_args():
	#os.environ['TRANSFORMERS_CACHE'] = '/XXXX-7/users/XXXX-1/metaphor_LLMs/paraphrase_gen/.cache/huggingface/hub'

	parser = argparse.ArgumentParser(
	description="Finetune a transformers model on a text classification task"
	)

	parser.add_argument(
	"--dataset",
	type=str,
	default=None,
	required=True,
	help="Name of the dataset to predict gold_labels",
	choices=["xnli-eu-native", "xnli-eu-var", "xnli-es-native", "xnli-es-var", "xnli-en", "xnli-es", "xnli-eu", "xnli-es-var-no-rep", "xnli-eu-var-no-rep", "xnli-eu-var-less-biz", "xnli-eu-var-less-gip", "xnli-eu-biz", "xnli-eu-gip", "xnli-eu-naf", "xnli-eu-nat-biz", "xnli-eu-nat-gip", "xnli-eu-nat-naf"]
	)

	parser.add_argument(
	"--model",
	type=str,
	default=None,
	required=True,
	help="Model name in config",
	choices=["llama3instruct8", "llama3instruct70", "gemmainstruct9", "gemmainstruct27", "latxainstruct70", "llama3base70"]
	)

	parser.add_argument(
	"--output_dir",
	type=str,
	default=None,
	required=True,
	help="Output path to dump predictions"
	)

	parser.add_argument(
	"--task",
	type=str,
	default=None,
	required=True,
	help="Type of task formulation",
	choices=["binary", "trilabel", "qa-zero", "qa-few"]
	)

	parser.add_argument(
	"--prompt_type",
	type=str,
	default=None,
	required=True,
	help="Type of prompt"
	)

	parser.add_argument(
	"--paraphrases",
	action="store_true",
	required=False,
	help="Dataset with paraphrases generated automatically"
	)

	parser.add_argument(
	"--paraphrase_source",
	type=str,
	default=None,
	required=False,
	help="Model used to generate paraphrases"
	)

	args = parser.parse_args()

	return args

	def load_dataset(data_path: str) -> pd.DataFrame:
	df = None
	extension = pathlib.Path(data_path).suffix
	if extension.endswith("json"):
	df = pd.read_json(data_path)
	elif extension.endswith("jsonl"):
	df = pd.read_json(data_path, lines=True)
	elif extension.endswith("tsv"):
	df = pd.read_csv(data_path, sep="\t")
	else:
	df = pd.read_csv(data_path)

	return df

	def dump_predictions(out_path: str, premises: List, hypotheses: List, gold_labels: List, predictions: List, paraphrased_sents=None):
	if paraphrased_sents:
	with open(out_path, "w") as o:
	o.write("premise\thypothesis\tgold_label\tprediction\tparaphrased_sentence\n")
	for p, h, g, pr, paraph in zip(premises, hypotheses, gold_labels, predictions, paraphrased_sents):
	o.write(f"{p}\t{h}\t{g}\t{pr}\t{paraph}\n")
	else:
	with open(out_path, "w") as o:
	o.write("premise\thypothesis\tgold_label\tprediction\n")
	for p, h, g, pr in zip(premises, hypotheses, gold_labels, predictions):
	o.write(f"{p}\t{h}\t{g}\t{pr}\n")

	print(f"{len(predictions)} Predictions stored in {out_path}")

	def map_labels(predictions: List[str], label_mapping: Dict):
	predictions_clean = [pred.strip("<>.,") for pred in predictions.lower().split()]
	for pred in predictions_clean:
	for label in label_mapping:
	label_lower = label.lower()
	# Allow partial matching in both directions
	if pred in label_lower or label_lower in pred:
	return label_mapping[label]
	return "unk"

	def get_column_values(df, col_id):
	return df[col_id].tolist()


	def map_labels_to_string(labels: List):
	label_strings = []
	for label in labels:
	if label == 0:
	label_strings.append("entailment")
	elif label == 1:
	label_strings.append("neutral")
	else:
	label_strings.append("contradiction")

	return label_strings

	def main():

	args = parse_args()

	if not os.path.exists(args.output_dir):
	os.makedirs(args.output_dir, exist_ok=True)

	logger_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.log")

	logger = logging.getLogger(__name__)
	logging.basicConfig(filename=os.path.join(logger_path), encoding='utf-8', level=logging.INFO)

	# Disable compilation (to avoid recompile_limit errors)
	#torch._dynamo.disable()
	#torch._dynamo.config.suppress_errors = True
	#torch._dynamo.config.recompile_limit = 100

	login(token='LOGIN_TOKEN') # Add hf login token
	model_id = config.get("models", {}).get(args.model, "")
	logger.info(f"Model used: {model_id}")
	logger.info(f"Prompt task: {args.task}")
	logger.info(f"Dataset with paraphrases: {args.paraphrases}")
	logger.info(f"Prompt config: {args.prompt_type}")
	device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')
	logger.info(f"Device in use: {device}")


	# TORCH_LOGS=recompiles

	datasets_config = config.get("datasets", {})
	prompt_config = config.get("prompts", {}).get(args.task, {})

	print(args.task)
	print(datasets_config.get(args.dataset, {}).get("prompts", []))

	# Ensure trilabel setup only for Meta4XNLI
	assert args.task in datasets_config.get(args.dataset, {}).get("prompts", [])

	if args.paraphrases:
	data_path = datasets_config.get(args.dataset, {}).get("data_path_paraphrase", "")
	else:
	data_path = datasets_config.get(args.dataset, {}).get("data_path", "")

	logger.info(f"Dataset loaded from: {data_path}")
	df = load_dataset(data_path)
	logger.info(f"Loaded samples: {len(df)}")
	premises = get_column_values(df, datasets_config.get(args.dataset, "").get("prem_col", ""))
	hypotheses = get_column_values(df, datasets_config.get(args.dataset, "").get("hyp_col", ""))
	if args.paraphrases:
	gold_labels = get_column_values(df, "gold_label")
	else:
	gold_labels = [l for l in get_column_values(df, datasets_config.get(args.dataset, "").get("label_col", ""))]

	print(gold_labels)

	gold_labels = map_labels_to_string(gold_labels)
	print(gold_labels)

	labels = list(set(gold_labels))

	set_seed(5)

	tokenizer = AutoTokenizer.from_pretrained(model_id)


	print("MODEL ID:", model_id)


	model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)

	print("here i am")

	tokenizer.pad_token_id = tokenizer.eos_token_id

	predictions = []
	for p, h, l in zip(premises, hypotheses, gold_labels):
	preffix_prompt = prompt_config.get(args.prompt_type, {}).get("preffix", "")
	print(preffix_prompt)
	print(args.prompt_type)
	if args.prompt_type == "chain":
	prompt = preffix_prompt + f"\n Premise: {p}\n Hypothesis: {h}\n Answer: "
	logger.info(f"Prompt: {prompt}")
	else:
	prompt = preffix_prompt + f" {p} -> {h}: "
	logger.info(f"Prompt: {prompt}")


	label_mappings = prompt_config.get(args.prompt_type, {}).get("label_mapping")

	logger.info(f"Label mappings: {label_mappings}")

	inputs = tokenizer([prompt], return_tensors="pt").to(device)

	logger.info(f"{p}\t{h}\t{l}")





	# #################
	# input_text = "Write me a poem about Machine Learning."
	# input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

	# outputs = model.generate(**input_ids, max_new_tokens=32)
	# print(tokenizer.decode(outputs[0]))
	# #################




	outputs = model.generate(**inputs, max_new_tokens=MAX_NEW_TOKENS, return_dict_in_generate=True, output_scores=True, temperature=TEMPERATURE)

	transition_scores = model.compute_transition_scores(
	outputs.sequences, outputs.scores, normalize_logits=True
	)
	logger.info(f"{outputs.sequences}\t{outputs.scores}")


	#print(f"transition scores: {transition_scores}", flush=True)

	#print(f"transition scores: {transition_scores}", flush=True)
	# input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
	# encoder-decoder models, like BART or T5.
	input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
	generated_tokens = outputs.sequences[:, input_length:]
	for tok, score in zip(generated_tokens[0], transition_scores[0]):
	# \| token \| token string \| log probability \| probability
	logger.info(f"\| {tok:5d} \| {tokenizer.decode(tok):8s} \| {score}")
	#logger.info(f"\| {tok:5d} \| {tokenizer.decode(tok):8s} \| {score.numpy():.3f} \| {np.exp(score.numpy()):.2%}")
	#o.write(f"\| {tok:5d} \| {tokenizer.decode(tok):8s} \| {score.numpy():.3f} \| {np.exp(score.numpy()):.2%}")


	answers = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)
	logger.info(f"Answers: {answers}, split: {answers.split()}")
	logger.info(f"Mapped label: {map_labels(answers, label_mappings)}")
	predictions.append(map_labels(answers, label_mappings))
	logger.info("Label added to predictions.")


	logger.debug(gold_labels[:5], predictions[:5], flush=True)
	assert len(gold_labels) == len(predictions)
	logger.info(f"Gold: {len(gold_labels)}, Pred: {len(predictions)}")


	predictions_path = os.path.join(args.output_dir, f"{args.prompt_type}_{args.paraphrase_source+'_' if args.paraphrase_source else ''}{datetime.now().strftime('%d-%m-%Y_%H_%M_%S')}.tsv")

	if args.paraphrases:
	paraphrased_sents = df.iloc[:, -1].tolist()
	logger.info(f"Dumping predictions with paraphrased sentences, met location: {list(df.columns)[-1]}")
	dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions, paraphrased_sents)
	else:
	dump_predictions(predictions_path, premises, hypotheses, gold_labels, predictions)

	logger.info(f"Predictions dumped to {predictions_path}")



	accuracy = accuracy_score(gold_labels, predictions, normalize=True)
	logger.info(f"Accuracy {len(gold_labels)}, {len(predictions)}: {accuracy}\n")



	if __name__ == "__main__":
	main()