from datasets import load_dataset

swag = load_dataset("swag", "regular")

swag["train"][0]

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

ending_names = ["ending0", "ending1", "ending2", "ending3"]


def preprocess_function(examples):
    first_sentences = [[context] * 4 for context in examples["sent1"]]
    question_headers = examples["sent2"]
    second_sentences = [
        [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
    ]

    first_sentences = sum(first_sentences, [])
    second_sentences = sum(second_sentences, [])

    tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
    return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}

tokenized_swag = swag.map(preprocess_function, batched=True)

from dataclasses import dataclass
from transformers.tokenization_utils_base import PreTrainedTokenizerBase, PaddingStrategy
from typing import Optional, Union
import torch


@dataclass
class DataCollatorForMultipleChoice:
    """
    Data collator that will dynamically pad the inputs for multiple choice received.
    """

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature.pop(label_name) for feature in features]
        batch_size = len(features)
        num_choices = len(features[0]["input_ids"])
        flattened_features = [
            [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features
        ]
        flattened_features = sum(flattened_features, [])

        batch = self.tokenizer.pad(
            flattened_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
        batch["labels"] = torch.tensor(labels, dtype=torch.int64)
        return batch

from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer

model = AutoModelForMultipleChoice.from_pretrained("bert-base-uncased")

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_swag["train"],
    eval_dataset=tokenized_swag["validation"],
    tokenizer=tokenizer,
    data_collator=DataCollatorForMultipleChoice(tokenizer=tokenizer),
)

trainer.train()


data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
tf_train_set = tokenized_swag["train"].to_tf_dataset(
    columns=["attention_mask", "input_ids"],
    label_cols=["labels"],
    shuffle=True,
    batch_size=batch_size,
    collate_fn=data_collator,
)

tf_validation_set = tokenized_swag["validation"].to_tf_dataset(
    columns=["attention_mask", "input_ids"],
    label_cols=["labels"],
    shuffle=False,
    batch_size=batch_size,
    collate_fn=data_collator,
)

from transformers import create_optimizer

batch_size = 16
num_train_epochs = 2
total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)

from transformers import TFAutoModelForMultipleChoice

model = TFAutoModelForMultipleChoice.from_pretrained("bert-base-uncased")

model.compile(
    optimizer=optimizer,
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
)

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)