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	from pathlib import Path
	import random
	import shutil
	from datasets import load_dataset, concatenate_datasets, Features, Sequence, ClassLabel, Value, DatasetDict
	from transformers import TrainingArguments
	from span_marker import SpanMarkerModel, Trainer
	from span_marker.model_card import SpanMarkerModelCardData
	from huggingface_hub import upload_folder, upload_file


	"""
	FEATURES = Features({"tokens": Sequence(feature=Value(dtype='string')), "ner_tags": Sequence(feature=ClassLabel(names=['O', 'B-ORG', 'I-ORG']))})


	def load_fewnerd():
	def mapper(sample):
	sample["ner_tags"] = [int(tag == 5) for tag in sample["ner_tags"]]
	sample["ner_tags"] = [2 if tag == 1 and idx > 0 and sample["ner_tags"][idx - 1] == 1 else tag for idx, tag in enumerate(sample["ner_tags"])]
	return sample

	dataset = load_dataset("DFKI-SLT/few-nerd", "supervised")
	dataset = dataset.map(mapper, remove_columns=["id", "fine_ner_tags"])
	dataset = dataset.cast(FEATURES)
	return dataset


	def load_conll():
	label_mapping = {3: 1, 4: 2}
	def mapper(sample):
	sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
	return sample

	dataset = load_dataset("conll2003")
	dataset = dataset.map(mapper, remove_columns=["id", "pos_tags", "chunk_tags"])
	dataset = dataset.cast(FEATURES)
	return dataset


	def load_ontonotes():
	label_mapping = {11: 1, 12: 2}
	def mapper(sample):
	sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
	return sample

	dataset = load_dataset("tner/ontonotes5")
	dataset = dataset.rename_column("tags", "ner_tags")
	dataset = dataset.map(mapper)
	dataset = dataset.cast(FEATURES)
	return dataset


	def load_multinerd():
	label_mapping = {5: 1, 6: 2}
	def mapper(sample):
	sample["ner_tags"] = [label_mapping.get(tag, 0) for tag in sample["ner_tags"]]
	return sample

	def lang_filter(sample):
	return sample["lang"] == "en"

	dataset = load_dataset("Babelscape/multinerd")
	dataset = dataset.filter(lang_filter)
	dataset = dataset.map(mapper, remove_columns="lang")
	dataset = dataset.cast(FEATURES)
	return dataset


	def preprocess_raw_dataset(raw_dataset):
	# Set the number of sentences without an org equal to the number of sentences with an org
	def has_org(sample):
	return bool(sum(sample["ner_tags"]))

	def has_no_org(sample):
	return not has_org(sample)

	dataset_org = raw_dataset.filter(has_org)
	dataset_no_org = raw_dataset.filter(has_no_org)
	dataset_no_org = dataset_no_org.select(random.sample(range(len(dataset_no_org)), k=len(dataset_org)))
	dataset = concatenate_datasets([dataset_org, dataset_no_org])
	return dataset
	"""


	def main() -> None:
	# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
	labels = ["O", "B-ORG", "I-ORG"]
	"""
	fewnerd_dataset = load_fewnerd()
	conll_dataset = load_conll()
	ontonotes_dataset = load_ontonotes()
	multinerd_dataset = load_multinerd()

	raw_train_dataset = concatenate_datasets([fewnerd_dataset["train"], conll_dataset["train"], ontonotes_dataset["train"], multinerd_dataset["train"]])
	raw_eval_dataset = concatenate_datasets([fewnerd_dataset["validation"], conll_dataset["validation"], ontonotes_dataset["validation"], multinerd_dataset["validation"]])
	raw_test_dataset = concatenate_datasets([fewnerd_dataset["test"], conll_dataset["test"], ontonotes_dataset["test"], multinerd_dataset["test"]])

	train_dataset = preprocess_raw_dataset(raw_train_dataset)
	eval_dataset = preprocess_raw_dataset(raw_eval_dataset)
	test_dataset = preprocess_raw_dataset(raw_test_dataset)

	dataset_dict = DatasetDict({
	"train": train_dataset,
	"validation": eval_dataset,
	"test": test_dataset,
	})
	dataset_dict.push_to_hub("ner-orgs", private=True)
	"""
	# breakpoint()
	dataset = load_dataset("tomaarsen/ner-orgs")

	train_dataset = dataset["train"]
	eval_dataset = dataset["validation"]
	eval_dataset = eval_dataset.select(random.sample(range(len(eval_dataset)), k=3000))
	test_dataset = dataset["test"]

	# Initialize a SpanMarker model using a pretrained BERT-style encoder
	encoder_id = "bert-base-cased"
	model_id = f"tomaarsen/span-marker-bert-base-orgs"
	model = SpanMarkerModel.from_pretrained(
	encoder_id,
	labels=labels,
	# SpanMarker hyperparameters:
	model_max_length=256,
	marker_max_length=128,
	entity_max_length=8,
	# Model card variables
	model_card_data=SpanMarkerModelCardData(
	model_id=model_id,
	encoder_id=encoder_id,
	dataset_name="FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD",
	language=["en"],
	),
	)

	# Prepare the 🤗 transformers training arguments
	output_dir = Path("models") / model_id
	args = TrainingArguments(
	output_dir=output_dir,
	run_name=model_id,
	# Training Hyperparameters:
	learning_rate=5e-5,
	per_device_train_batch_size=32,
	per_device_eval_batch_size=32,
	num_train_epochs=3,
	weight_decay=0.01,
	warmup_ratio=0.1,
	bf16=True, # Replace `bf16` with `fp16` if your hardware can't use bf16.
	# Other Training parameters
	logging_first_step=True,
	logging_steps=100,
	evaluation_strategy="steps",
	save_strategy="steps",
	eval_steps=3000,
	save_total_limit=1,
	dataloader_num_workers=4,
	)

	# Initialize the trainer using our model, training args & dataset, and train
	trainer = Trainer(
	model=model,
	args=args,
	train_dataset=train_dataset,
	eval_dataset=eval_dataset,
	)
	trainer.train()

	# Compute & save the metrics on the test set
	metrics = trainer.evaluate(test_dataset, metric_key_prefix="test")
	trainer.save_metrics("test", metrics)

	# Save the model & training script locally
	trainer.save_model(output_dir / "checkpoint-final")
	shutil.copy2(__file__, output_dir / "checkpoint-final" / "train.py")

	# Upload everything to the Hub
	breakpoint()
	model.push_to_hub(model_id, private=True)
	upload_folder(folder_path=output_dir / "runs", path_in_repo="runs", repo_id=model_id)
	upload_file(path_or_fileobj=__file__, path_in_repo="train.py", repo_id=model_id)
	upload_file(path_or_fileobj=output_dir / "all_results.json", path_in_repo="all_results.json", repo_id=model_id)
	upload_file(path_or_fileobj=output_dir / "emissions.csv", path_in_repo="emissions.csv", repo_id=model_id)


	if __name__ == "__main__":
	main()