from datasets import load_dataset
from transformers import MarianMTModel, MarianTokenizer, TrainingArguments, Trainer, DataCollatorForSeq2Seq

# Load dataset
dataset = load_dataset('csv', data_files='dataset.tsv', delimiter='\t')

# Load MarianMT tokenizer for translation task
tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-hi')

# Tokenize the English text (source language)
def tokenize_function(examples):
    return tokenizer(examples['english'], truncation=True, padding='max_length', max_length=128)

# Tokenize both English and Hindi sentences
tokenized_datasets = dataset.map(tokenize_function, batched=True)

def tokenize_target_function(examples):
    return tokenizer(examples['hindi'], truncation=True, padding='max_length', max_length=128)

tokenized_datasets = tokenized_datasets.map(tokenize_target_function, batched=True)

# Data Collator for padding sequences
data_collator = DataCollatorForSeq2Seq(tokenizer, model=None)

# Load MarianMT model for translation
model = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-en-hi')

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
    save_total_limit=2,
    predict_with_generate=True,
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test'],
    tokenizer=tokenizer,
    data_collator=data_collator,
)

# Start training
trainer.train()

# Save the model
trainer.save_model('./my_hindi_translation_model')

# Evaluate the model
results = trainer.evaluate()
print(results)

# Generate a prediction
model.eval()
inputs = tokenizer("How are you?", return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))