import pandas as pd
from datasets import load_dataset, load_metric
from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer, DataCollatorForTokenClassification
import numpy as np

# Load the CoNLL-2003 dataset with trust_remote_code
dataset = load_dataset("conll2003", trust_remote_code=True)

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")

# Define label list and map labels to IDs
label_list = dataset['train'].features['ner_tags'].feature.names

# Tokenize and align labels function
def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples['tokens'], truncation=True, padding='max_length', is_split_into_words=True)
    labels = []
    for i, label in enumerate(examples['ner_tags']):
        word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:  # Set the special tokens to -100.
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)
    tokenized_inputs["labels"] = labels
    return tokenized_inputs

# Tokenize the datasets
tokenized_datasets = dataset.map(tokenize_and_align_labels, batched=True)

# Split the dataset into training and evaluation sets
train_dataset = tokenized_datasets["train"]
eval_dataset = tokenized_datasets["validation"]

# Load the model
model = AutoModelForTokenClassification.from_pretrained("bert-base-cased", num_labels=len(label_list))

# Data collator for token classification
data_collator = DataCollatorForTokenClassification(tokenizer)

# 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,
)

# Define the trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=data_collator,
    compute_metrics=lambda p: {
        "accuracy": (p.predictions.argmax(-1) == p.label_ids).astype(np.float32).mean().item(),
        "f1": load_metric("seqeval").compute(predictions=np.argmax(p.predictions, axis=2), references=p.label_ids)['overall_f1']
    },
)

# Train the model
trainer.train()

# Evaluate the model
results = trainer.evaluate()

# Print the results
print("Evaluation results:", results)

# Predict on the evaluation set
predictions, labels, _ = trainer.predict(eval_dataset)
predictions = np.argmax(predictions, axis=2)

# Convert the predictions and labels to the original tags
true_labels = [[label_list[l] for l in label if l != -100] for label in labels]
true_predictions = [
    [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
    for prediction, label in zip(predictions, labels)
]

# Create a DataFrame for the results
results_df = pd.DataFrame({
    'tokens': eval_dataset['tokens'],
    'true_labels': true_labels,
    'predicted_labels': true_predictions
})

# Save the results to a CSV file
results_df.to_csv('mnt/data/ner_results.csv', index=False)

print("Wyniki analizy NER zostały zapisane do pliku 'mnt/data/ner_results.csv'.")