### Code to generate dataset

# %%
from datasets import load_dataset

dataset = load_dataset("conll2003")

# %%
dataset

# %%
dataset['train'][0]['tokens']

# %%
ner_tags= {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}

# %%
# Swap keys and values using dictionary comprehension
swapped_dict = {v: k for k, v in ner_tags.items()}

# Print the swapped dictionary
print(swapped_dict)

# %%
[swapped_dict[x] for x in dataset['train'][0]['ner_tags']]

# %%
dataset['train'][0]

# %%
def label_tokens(entry):
    entry['ner_labels'] = [swapped_dict[x] for x in entry['ner_tags']]
    return entry


# %%
dataset['train'] = dataset["train"].map(label_tokens)
dataset['test'] = dataset["test"].map(label_tokens)
dataset['validation'] = dataset["validation"].map(label_tokens)


# %%
def tokens_to_sentence(entry):
    entry['sentence'] = ' '.join(entry['tokens'])
    return entry

dataset['train'] = dataset["train"].map(tokens_to_sentence)
dataset['test'] = dataset["test"].map(tokens_to_sentence)
dataset['validation'] = dataset["validation"].map(tokens_to_sentence)


# %%
def extract_entities(entry):
    entities = {'PER': [], 'ORG': [], 'LOC': [], 'MISC': []}
    current_entity = {"type": None, "words": []}
    for word, label in zip(entry['sentence'].split(), entry['ner_labels']):
        if label.startswith('B-'):
            entity_type = label.split('-')[1]
            if current_entity["type"] == entity_type:
                entities[entity_type].append(' '.join(current_entity["words"]))
                current_entity["words"] = [word]
            else:
                if current_entity["type"] is not None:
                    entities[current_entity["type"]].append(' '.join(current_entity["words"]))
                current_entity = {"type": entity_type, "words": [word]}
        elif label.startswith('I-'):
            if current_entity["type"] is not None:
                current_entity["words"].append(word)
        else:
            if current_entity["type"] is not None:
                entities[current_entity["type"]].append(' '.join(current_entity["words"]))
            current_entity = {"type": None, "words": []}
    if current_entity["type"] is not None:
        entities[current_entity["type"]].append(' '.join(current_entity["words"]))

    entry['entities'] = entities
    return entry

# Extract entities
dataset['train'] = dataset["train"].map(extract_entities)
dataset['test'] = dataset["test"].map(extract_entities)
dataset['validation'] = dataset["validation"].map(extract_entities)



# %%
dataset['train'][10]['sentence'], dataset['train'][10]['entities']

# %%
dataset.push_to_hub("areias/conll2003-generative")

# %%
from collections import Counter

def get_count(entries):
    # Initialize counters for each entity type
    per_counter = Counter()
    org_counter = Counter()
    loc_counter = Counter()
    misc_counter = Counter()

    # Count the occurrences of each type of entity
    for item in entries:
        per_counter.update(item['entities']['PER'])
        org_counter.update(item['entities']['ORG'])
        loc_counter.update(item['entities']['LOC'])
        misc_counter.update(item['entities']['MISC'])

    # Print the counts for each type of entity
    print("Total PER entities:", sum(per_counter.values()))
    print("Total ORG entities:", sum(org_counter.values()))
    print("Total LOC entities:", sum(loc_counter.values()))
    print("Total MISC entities:", sum(misc_counter.values()))


# %%
get_count(dataset['train'])

# %%
get_count(dataset['test'])

# %%
get_count(dataset['validation'])

# %%