import nltk
from nltk.corpus import wordnet, stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk import pos_tag

nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger_eng')

lemmatizer = WordNetLemmatizer()

def get_wordnet_pos(nltk_pos_tag):
    if nltk_pos_tag.startswith('J'):
        return wordnet.ADJ
    elif nltk_pos_tag.startswith('V'):
        return wordnet.VERB
    elif nltk_pos_tag.startswith('N'):
        return wordnet.NOUN
    elif nltk_pos_tag.startswith('R'):
        return wordnet.ADV
    else:
        return wordnet.NOUN

text = """
The striped bats are hanging on their feet for best.
"""

tokens = word_tokenize(text)
pos_tags = pos_tag(tokens)

lemmatized_tokens = [lemmatizer.lemmatize(token, get_wordnet_pos(pos)) for token, pos in pos_tags]

print("Original Tokens and POS Tags:")
print(pos_tags)
print("\nLemmatized Tokens:")
print(lemmatized_tokens)