finetune.py

'''
Adapted from code from the HuggingFace Transformers library
Modifications by Aditya Malusare
'''

#!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from typing import Optional
import random 

import datasets
import evaluate
import nltk  # Here to have a nice missing dependency error message early on
import numpy as np
from datasets import load_dataset
from filelock import FileLock

import transformers
from transformers import (
	AutoConfig,
	AutoModelForSeq2SeqLM,
	AutoTokenizer,
	DataCollatorForSeq2Seq,
	HfArgumentParser,
	MBart50Tokenizer,
	MBart50TokenizerFast,
	MBartTokenizer,
	MBartTokenizerFast,
	Seq2SeqTrainer,
	Seq2SeqTrainingArguments,
	set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
from transformers.utils.versions import require_version

alphabet = "ACTG"

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

logger = logging.getLogger(__name__)

gen_args = {
	"source_dataset": "malusare/human-genome-chm13v2-uc",
	"size": 1000000,
	"seed": 1,
	"sequence_length": 512,
	"dataset_corruption_rate": 0.5,
	"sequence_corruption_rate": 0.02,
	"num_processes": 63,
	"show_examples": True
}

try:
	nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
	if is_offline_mode():
		raise LookupError(
			"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
		)
	with FileLock(".lock") as lock:
		nltk.download("punkt", quiet=True)

# A list of all multilingual tokenizer which require lang attribute.
MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast]

def generate_dataset_seq2seq(args):
	# Load the dataset
	dataset = load_dataset(args['source_dataset'])
	dataset = dataset["train"].shuffle(seed=args['seed'])

	# Limit the dataset
	limit = min(args['size'], len(dataset))
	dataset = dataset.select(range(limit))
	dataset = dataset.flatten_indices()

	# Define the corruption function
	def _corrupt(example):
		'''Corrupt the sequence with a given rate.'''
		# Truncate and get rid of soft masks
		example["sequence"] = str.upper(example["sequence"][:args['sequence_length']])
		if random.random() < args['dataset_corruption_rate']:
			# Corrupt the sequence
			random_mask = [random.random() < args['sequence_corruption_rate'] for _ in range(len(example["sequence"]))]
			example["corrupted_sequence"] = ''.join([token if not mask else random.choice(alphabet) for token, mask in zip(example["sequence"], random_mask)])	
			example["labels"] = "1"
		else:
			example["corrupted_sequence"] = example["sequence"]
		return example


	# Apply the corruption function
	dataset = dataset.add_column("corrupted_sequence", dataset["sequence"])
	dataset = dataset.add_column("labels", ["0"] * len(dataset))

	dataset = dataset.map(
		_corrupt,
		num_proc=args['num_processes'],
	)

	# Remove the original sequence column
	dataset = dataset.remove_columns(["sequence"])
	
	# Rename the corrupted sequence column
	dataset = dataset.rename_column("corrupted_sequence", "sequence")

	# Print the first 10 examples
	if args['show_examples']:
		print(dataset[:10])

	return dataset

@dataclass
class ModelArguments:
	"""
	Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
	"""

	model_name_or_path: str = field(
		metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
	)
	config_name: Optional[str] = field(
		default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
	)
	tokenizer_name: Optional[str] = field(
		default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
	)
	cache_dir: Optional[str] = field(
		default=None,
		metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
	)
	use_fast_tokenizer: bool = field(
		default=True,
		metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
	)
	model_revision: str = field(
		default="main",
		metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
	)
	token: str = field(
		default=None,
		metadata={
			"help": (
				"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
				"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
			)
		},
	)
	use_auth_token: bool = field(
		default=None,
		metadata={
			"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
		},
	)
	trust_remote_code: bool = field(
		default=False,
		metadata={
			"help": (
				"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
				"should only be set to `True` for repositories you trust and in which you have read the code, as it will"
				"execute code present on the Hub on your local machine."
			)
		},
	)
	resize_position_embeddings: Optional[bool] = field(
		default=None,
		metadata={
			"help": (
				"Whether to automatically resize the position embeddings if `max_source_length` exceeds "
				"the model's position embeddings."
			)
		},
	)


@dataclass
class DataTrainingArguments:
	"""
	Arguments pertaining to what data we are going to input our model for training and eval.
	"""

	lang: Optional[str] = field(default=None, metadata={"help": "Language id for summarization."})

	dataset_name: Optional[str] = field(
		default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
	)
	dataset_config_name: Optional[str] = field(
		default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
	)
	text_column: Optional[str] = field(
		default=None,
		metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
	)
	summary_column: Optional[str] = field(
		default=None,
		metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
	)
	train_file: Optional[str] = field(
		default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
	)
	validation_file: Optional[str] = field(
		default=None,
		metadata={
			"help": (
				"An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
			)
		},
	)
	test_file: Optional[str] = field(
		default=None,
		metadata={
			"help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
		},
	)
	overwrite_cache: bool = field(
		default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
	)
	preprocessing_num_workers: Optional[int] = field(
		default=None,
		metadata={"help": "The number of processes to use for the preprocessing."},
	)
	max_source_length: Optional[int] = field(
		default=512,
		metadata={
			"help": (
				"The maximum total input sequence length after tokenization. Sequences longer "
				"than this will be truncated, sequences shorter will be padded."
			)
		},
	)
	max_target_length: Optional[int] = field(
		default=512,
		metadata={
			"help": (
				"The maximum total sequence length for target text after tokenization. Sequences longer "
				"than this will be truncated, sequences shorter will be padded."
			)
		},
	)
	val_max_target_length: Optional[int] = field(
		default=None,
		metadata={
			"help": (
				"The maximum total sequence length for validation target text after tokenization. Sequences longer "
				"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
				"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
				"during ``evaluate`` and ``predict``."
			)
		},
	)
	pad_to_max_length: bool = field(
		default=False,
		metadata={
			"help": (
				"Whether to pad all samples to model maximum sentence length. "
				"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
				"efficient on GPU but very bad for TPU."
			)
		},
	)
	max_train_samples: Optional[int] = field(
		default=None,
		metadata={
			"help": (
				"For debugging purposes or quicker training, truncate the number of training examples to this "
				"value if set."
			)
		},
	)
	max_eval_samples: Optional[int] = field(
		default=None,
		metadata={
			"help": (
				"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				"value if set."
			)
		},
	)
	max_predict_samples: Optional[int] = field(
		default=None,
		metadata={
			"help": (
				"For debugging purposes or quicker training, truncate the number of prediction examples to this "
				"value if set."
			)
		},
	)
	num_beams: Optional[int] = field(
		default=None,
		metadata={
			"help": (
				"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
				"which is used during ``evaluate`` and ``predict``."
			)
		},
	)
	ignore_pad_token_for_loss: bool = field(
		default=True,
		metadata={
			"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
		},
	)
	source_prefix: Optional[str] = field(
		default="", metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
	)

	forced_bos_token: Optional[str] = field(
		default=None,
		metadata={
			"help": (
				"The token to force as the first generated token after the decoder_start_token_id."
				"Useful for multilingual models like mBART where the first generated token"
				"needs to be the target language token (Usually it is the target language token)"
			)
		},
	)

	def __post_init__(self):
		if (
			self.dataset_name is None
			and self.train_file is None
			and self.validation_file is None
			and self.test_file is None
		):
			raise ValueError("Need either a dataset name or a training, validation, or test file.")
		else:
			if self.train_file is not None:
				extension = self.train_file.split(".")[-1]
				assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
			if self.validation_file is not None:
				extension = self.validation_file.split(".")[-1]
				assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
			if self.test_file is not None:
				extension = self.test_file.split(".")[-1]
				assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
		if self.val_max_target_length is None:
			self.val_max_target_length = self.max_target_length


summarization_name_mapping = {
	"amazon_reviews_multi": ("review_body", "review_title"),
	"big_patent": ("description", "abstract"),
	"cnn_dailymail": ("article", "highlights"),
	"orange_sum": ("text", "summary"),
	"pn_summary": ("article", "summary"),
	"psc": ("extract_text", "summary_text"),
	"samsum": ("dialogue", "summary"),
	"thaisum": ("body", "summary"),
	"xglue": ("news_body", "news_title"),
	"xsum": ("document", "summary"),
	"wiki_summary": ("article", "highlights"),
	"multi_news": ("document", "summary"),
	"dna": ("input_text", "labels"),
}


def main():
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
	if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
	else:
		model_args, data_args, training_args = parser.parse_args_into_dataclasses()

	if model_args.use_auth_token is not None:
		warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
		if model_args.token is not None:
			raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
		model_args.token = model_args.use_auth_token

	# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
	# information sent is the one passed as arguments along with your Python/PyTorch versions.
	send_example_telemetry("run_summarization", model_args, data_args)

	# Setup logging
	logging.basicConfig(
		format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
		datefmt="%m/%d/%Y %H:%M:%S",
		handlers=[logging.StreamHandler(sys.stdout)],
	)

	if training_args.should_log:
		# The default of training_args.log_level is passive, so we set log level at info here to have that default.
		transformers.utils.logging.set_verbosity_info()

	log_level = training_args.get_process_log_level()
	logger.setLevel(log_level)
	transformers.utils.logging.set_verbosity(log_level)
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
		f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
		+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
	)

	if data_args.source_prefix is None and model_args.model_name_or_path in [
		"t5-small",
		"t5-base",
		"t5-large",
		"t5-3b",
		"t5-11b",
	]:
		logger.warning(
			"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
			"`--source_prefix 'summarize: ' `"
		)

	# Detecting last checkpoint.
	last_checkpoint = None
	if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
		last_checkpoint = get_last_checkpoint(training_args.output_dir)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
			raise ValueError(
				f"Output directory ({training_args.output_dir}) already exists and is not empty. "
				"Use --overwrite_output_dir to overcome."
			)
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
				f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
				"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
			)

	# Set seed before initializing model.
	set_seed(training_args.seed)

	# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
	# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
	# (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For CSV/JSON files this script will use the first column for the full texts and the second column for the
	# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		raw_datasets = load_dataset(
			data_args.dataset_name,
			data_args.dataset_config_name,
			cache_dir=model_args.cache_dir,
			token=model_args.token,
			split="train",
		)
		if "validation" not in raw_datasets:
			raw_datasets = raw_datasets.train_test_split(test_size=0.005)
			raw_datasets["validation"] = raw_datasets["test"]
	else:
		data_files = {}
		if data_args.train_file is not None:
			data_files["train"] = data_args.train_file
			extension = data_args.train_file.split(".")[-1]
		if data_args.validation_file is not None:
			data_files["validation"] = data_args.validation_file
			extension = data_args.validation_file.split(".")[-1]
		if data_args.test_file is not None:
			data_files["test"] = data_args.test_file
			extension = data_args.test_file.split(".")[-1]
		raw_datasets = load_dataset(
			extension,
			data_files=data_files,
			cache_dir=model_args.cache_dir,
			token=model_args.token,
		)
	# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
	# https://huggingface.co/docs/datasets/loading_datasets.html.

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	config = AutoConfig.from_pretrained(
		model_args.config_name if model_args.config_name else model_args.model_name_or_path,
		cache_dir=model_args.cache_dir,
		revision=model_args.model_revision,
		token=model_args.token,
		trust_remote_code=model_args.trust_remote_code,
	)
	tokenizer = AutoTokenizer.from_pretrained(
		model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
		cache_dir=model_args.cache_dir,
		use_fast=model_args.use_fast_tokenizer,
		revision=model_args.model_revision,
		token=model_args.token,
		trust_remote_code=model_args.trust_remote_code,
	)
	model = AutoModelForSeq2SeqLM.from_pretrained(
		model_args.model_name_or_path,
		from_tf=bool(".ckpt" in model_args.model_name_or_path),
		config=config,
		cache_dir=model_args.cache_dir,
		revision=model_args.model_revision,
		token=model_args.token,
		trust_remote_code=model_args.trust_remote_code,
	)

	# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
	# on a small vocab and want a smaller embedding size, remove this test.
	embedding_size = model.get_input_embeddings().weight.shape[0]
	if len(tokenizer) > embedding_size:
		model.resize_token_embeddings(len(tokenizer))

	if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
		if isinstance(tokenizer, MBartTokenizer):
			model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.lang]
		else:
			model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.lang)

	if model.config.decoder_start_token_id is None:
		raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

	if (
		hasattr(model.config, "max_position_embeddings")
		and model.config.max_position_embeddings < data_args.max_source_length
	):
		if model_args.resize_position_embeddings is None:
			logger.warning(
				"Increasing the model's number of position embedding vectors from"
				f" {model.config.max_position_embeddings} to {data_args.max_source_length}."
			)
			model.resize_position_embeddings(data_args.max_source_length)
		elif model_args.resize_position_embeddings:
			model.resize_position_embeddings(data_args.max_source_length)
		else:
			raise ValueError(
				f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has"
				f" {model.config.max_position_embeddings} position encodings. Consider either reducing"
				f" `--max_source_length` to {model.config.max_position_embeddings} or to automatically resize the"
				" model's position encodings by passing `--resize_position_embeddings`."
			)

	prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

	# Preprocessing the datasets.
	# We need to tokenize inputs and targets.
	if training_args.do_train:
		if "train" not in raw_datasets:
			raise ValueError("--do_train requires a train dataset")
		column_names = raw_datasets["train"].column_names
	elif training_args.do_eval:
		if "validation" not in raw_datasets:
			raise ValueError("--do_eval requires a validation dataset")
		column_names = raw_datasets["validation"].column_names
	elif training_args.do_predict:
		if "test" not in raw_datasets:
			raise ValueError("--do_predict requires a test dataset")
		column_names = raw_datasets["test"].column_names
	else:
		logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
		return

	if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
		assert (
			data_args.lang is not None
		), f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --lang argument"

		tokenizer.src_lang = data_args.lang
		tokenizer.tgt_lang = data_args.lang

		# For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
		# as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
		forced_bos_token_id = (
			tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None
		)
		model.config.forced_bos_token_id = forced_bos_token_id

	# Get the column names for input/target.
	dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
	if data_args.text_column is None:
		text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
	else:
		text_column = data_args.text_column
		if text_column not in column_names:
			raise ValueError(
				f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
			)
	if data_args.summary_column is None:
		summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
	else:
		summary_column = data_args.summary_column
		if summary_column not in column_names:
			raise ValueError(
				f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
			)

	# Temporarily set max_target_length for training.
	max_target_length = data_args.max_target_length
	padding = "max_length" if data_args.pad_to_max_length else False

	if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
		logger.warning(
			"label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
			f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
		)

	def preprocess_function(examples):
		# remove pairs where at least one record is None

		inputs, targets = [], []
		for i in range(len(examples[text_column])):
			if examples[text_column][i] and examples[summary_column][i]:
				inputs.append(examples[text_column][i])
				targets.append(examples[summary_column][i])

		inputs = [prefix + inp for inp in inputs]
		model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)

		# Tokenize targets with the `text_target` keyword argument
		labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)

		# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
		# padding in the loss.
		if padding == "max_length" and data_args.ignore_pad_token_for_loss:
			labels["input_ids"] = [
				[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
			]

		model_inputs["labels"] = labels["input_ids"]
		return model_inputs

	if training_args.do_train:
		train_dataset = raw_datasets["train"]
		if data_args.max_train_samples is not None:
			max_train_samples = min(len(train_dataset), data_args.max_train_samples)
			train_dataset = train_dataset.select(range(max_train_samples))
		with training_args.main_process_first(desc="train dataset map pre-processing"):
			train_dataset = train_dataset.map(
				preprocess_function,
				batched=True,
				num_proc=data_args.preprocessing_num_workers,
				remove_columns=column_names,
				load_from_cache_file=not data_args.overwrite_cache,
				desc="Running tokenizer on train dataset",
			)

	if training_args.do_eval:
		max_target_length = data_args.val_max_target_length
		eval_dataset = raw_datasets["validation"]
		if data_args.max_eval_samples is not None:
			max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
			eval_dataset = eval_dataset.select(range(max_eval_samples))
		with training_args.main_process_first(desc="validation dataset map pre-processing"):
			eval_dataset = eval_dataset.map(
				preprocess_function,
				batched=True,
				num_proc=data_args.preprocessing_num_workers,
				remove_columns=column_names,
				load_from_cache_file=not data_args.overwrite_cache,
				desc="Running tokenizer on validation dataset",
			)

	if training_args.do_predict:
		max_target_length = data_args.val_max_target_length
		predict_dataset = raw_datasets["test"]
		if data_args.max_predict_samples is not None:
			max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
			predict_dataset = predict_dataset.select(range(max_predict_samples))
		with training_args.main_process_first(desc="prediction dataset map pre-processing"):
			predict_dataset = predict_dataset.map(
				preprocess_function,
				batched=True,
				num_proc=data_args.preprocessing_num_workers,
				remove_columns=column_names,
				load_from_cache_file=not data_args.overwrite_cache,
				desc="Running tokenizer on prediction dataset",
			)

	# Data collator
	label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
	data_collator = DataCollatorForSeq2Seq(
		tokenizer,
		model=model,
		label_pad_token_id=label_pad_token_id,
		pad_to_multiple_of=8 if training_args.fp16 else None,
	)

	# Metric
	metric = evaluate.load("accuracy")

	def postprocess_text(preds, labels):
		preds = [pred.strip() for pred in preds]
		labels = [label.strip() for label in labels]

		return preds, labels

	from sklearn.metrics import accuracy_score

	def compute_metrics(eval_preds):
		preds, labels = eval_preds
		if isinstance(preds, tuple):
			preds = preds[0]
		# Replace -100s used for padding as we can't decode them
		preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
		decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
		labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
		decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
		# Some simple post-processing
		decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
		result = accuracy_score(y_pred=decoded_preds, y_true=decoded_labels)
		result = {'accuracy': result}
		prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
		result["gen_len"] = np.mean(prediction_lens)
		return result

	# Override the decoding parameters of Seq2SeqTrainer
	training_args.generation_max_length = (
		training_args.generation_max_length
		if training_args.generation_max_length is not None
		else data_args.val_max_target_length
	)
	training_args.generation_num_beams = (
		data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
	)

	training_args.set_evaluate(strategy="steps", steps=500)
	training_args.eval_delay = 0
	training_args.save_total_limit = 1
	training_args.num_train_epochs = 10

	logger.info(f"Training/evaluation parameters {training_args}")

	# Initialize our Trainer
	trainer = Seq2SeqTrainer(
		model=model,
		args=training_args,
		train_dataset=train_dataset if training_args.do_train else None,
		eval_dataset=eval_dataset if training_args.do_eval else None,
		tokenizer=tokenizer,
		data_collator=data_collator,
		compute_metrics=compute_metrics if training_args.predict_with_generate else None,
	)

	# Training
	if training_args.do_train:
		checkpoint = None
		if training_args.resume_from_checkpoint is not None:
			checkpoint = training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			checkpoint = last_checkpoint
		train_result = trainer.train()
		trainer.save_model()  # Saves the tokenizer too for easy upload

		metrics = train_result.metrics
		max_train_samples = (
			data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
		)
		metrics["train_samples"] = min(max_train_samples, len(train_dataset))

		trainer.log_metrics("train", metrics)
		trainer.save_metrics("train", metrics)
		trainer.save_state()

	# Evaluation
	results = {}
	if training_args.do_eval:
		logger.info("*** Evaluate ***")
		if isinstance(eval_dataset, dict):
			metrics = {}
			for eval_ds_name, eval_ds in eval_dataset.items():
				dataset_metrics = trainer.evaluate(eval_dataset=eval_ds, metric_key_prefix=f"eval_{eval_ds_name}")
				metrics.update(dataset_metrics)
		else:
			metrics = trainer.evaluate(metric_key_prefix="eval")
		max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
		metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))

		trainer.log_metrics("eval", metrics)
		trainer.save_metrics("eval", metrics)

	if training_args.do_predict:
		logger.info("*** Predict ***")

		predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict")
		metrics = predict_results.metrics
		max_predict_samples = (
			data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
		)
		metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))

		trainer.log_metrics("predict", metrics)
		trainer.save_metrics("predict", metrics)

		if trainer.is_world_process_zero():
			if training_args.predict_with_generate:
				predictions = predict_results.predictions
				predictions = np.where(predictions != -100, predictions, tokenizer.pad_token_id)
				predictions = tokenizer.batch_decode(
					predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
				)
				predictions = [pred.strip() for pred in predictions]
				output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
				with open(output_prediction_file, "w") as writer:
					writer.write("\n".join(predictions))

	kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
	if data_args.dataset_name is not None:
		kwargs["dataset_tags"] = data_args.dataset_name
		if data_args.dataset_config_name is not None:
			kwargs["dataset_args"] = data_args.dataset_config_name
			kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
		else:
			kwargs["dataset"] = data_args.dataset_name

	if data_args.lang is not None:
		kwargs["language"] = data_args.lang

	if training_args.push_to_hub:
		trainer.push_to_hub(**kwargs)
	else:
		trainer.create_model_card(**kwargs)

	return results


def _mp_fn(index):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()