# import nltk
# from nltk.tokenize import word_tokenize, sent_tokenize
# from nltk.corpus import stopwords
# from nltk.stem import PorterStemmer, WordNetLemmatizer
# from nltk.tag import pos_tag
# from nltk.chunk import ne_chunk
# from typing import List, Union, Dict, Optional

# class SRSWTINLTKProcessor:
#     """NLTK-based text processing operations."""
    
#     def __init__(self, language: str = 'english'):
#         """
#         Initialize NLTK processor.
        
#         Args:
#             language: Language for stopwords and processing
#         """
#         # Download required NLTK data
#         try:
#             nltk.data.find('tokenizers/punkt')
#             nltk.data.find('corpora/stopwords')
#             nltk.data.find('taggers/averaged_perceptron_tagger')
#             nltk.data.find('corpora/wordnet')
#         except LookupError:
#             nltk.download('punkt')
#             nltk.download('stopwords')
#             nltk.download('averaged_perceptron_tagger')
#             nltk.download('wordnet')
#             nltk.download('maxent_ne_chunker')
#             nltk.download('words')
#             nltk.download('maxent_ne_chunker_tab')
        
#         self.language = language
#         self.stop_words = set(stopwords.words(language))
#         self.stemmer = PorterStemmer()
#         self.lemmatizer = WordNetLemmatizer()
    
#     def tokenize_words(self, text: str) -> List[str]:
#         """Tokenize text into words."""
#         return word_tokenize(text)
    
#     def tokenize_sentences(self, text: str) -> List[str]:
#         """Tokenize text into sentences."""
#         return sent_tokenize(text)
    
#     def remove_stopwords(self, tokens: List[str]) -> List[str]:
#         """Remove stopwords from tokens."""
#         return [token for token in tokens if token.lower() not in self.stop_words]
    
#     def stem_words(self, tokens: List[str]) -> List[str]:
#         """Stem words using Porter Stemmer."""
#         return [self.stemmer.stem(token) for token in tokens]
    
#     def lemmatize_words(self, tokens: List[str]) -> List[str]:
#         """Lemmatize words using WordNet."""
#         return [self.lemmatizer.lemmatize(token) for token in tokens]
    
#     def pos_tag_text(self, tokens: List[str]) -> List[tuple]:
#         """Perform POS tagging on tokens."""
#         return pos_tag(tokens)
    
#     def extract_named_entities(self, tokens: List[str]) -> List:
#         """Extract named entities from tokens."""
#         pos_tags = pos_tag(tokens)
#         return ne_chunk(pos_tags)
    
#     def get_word_frequencies(self, tokens: List[str]) -> Dict[str, int]:
#         """Calculate word frequencies."""
#         return nltk.FreqDist(tokens)
    
#     def process_text(self, text: str, steps: Optional[List[str]] = None) -> Dict:
#         """
#         Process text through multiple NLTK operations.
        
#         Args:
#             text: Input text
#             steps: List of processing steps to apply:
#                 - tokenize: Word tokenization
#                 - sentences: Sentence tokenization
#                 - stopwords: Stopword removal
#                 - stem: Stemming
#                 - lemmatize: Lemmatization
#                 - pos: POS tagging
#                 - ner: Named entity recognition
#                 - frequencies: Word frequencies
#         """
#         if steps is None:
#             steps = ['tokenize', 'stopwords', 'lemmatize', 'pos']
            
#         result = {}
#         tokens = None
        
#         for step in steps:
#             if step == 'tokenize':
#                 tokens = self.tokenize_words(text)
#                 result['tokens'] = tokens
#             elif step == 'sentences':
#                 result['sentences'] = self.tokenize_sentences(text)
#             elif step == 'stopwords':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['filtered_tokens'] = self.remove_stopwords(tokens)
#             elif step == 'stem':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['stemmed'] = self.stem_words(tokens)
#             elif step == 'lemmatize':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['lemmatized'] = self.lemmatize_words(tokens)
#             elif step == 'pos':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['pos_tags'] = self.pos_tag_text(tokens)
#             elif step == 'ner':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['named_entities'] = self.extract_named_entities(tokens)
#             elif step == 'frequencies':
#                 tokens = tokens or self.tokenize_words(text)
#                 result['word_frequencies'] = self.get_word_frequencies(tokens)
                
#         return result

