String Utilities

def full_process(s: str, force_ascii: bool = False) -> str:
    """
    Process string for fuzzy matching by normalizing format.
    
    Processing steps:
    1. Convert to string if not already
    2. Optionally convert to ASCII (removes accented characters)
    3. Remove all non-alphanumeric characters (replaced with spaces)
    4. Trim leading/trailing whitespace
    5. Convert to lowercase
    6. Normalize internal whitespace
    
    Args:
        s: String to process
        force_ascii: If True, convert accented characters to ASCII equivalents
        
    Returns:
        str: Processed and normalized string
    """

def ascii_only(s: str) -> str:
    """
    Convert string to ASCII by removing non-ASCII characters.
    
    Removes characters with ASCII codes 128-255, effectively stripping
    accented characters, emoji, and other non-ASCII content.
    
    Args:
        s: String to convert
        
    Returns:
        str: ASCII-only version of the string
    """

# Translation table for ASCII conversion (removes chars 128-255)
translation_table: dict

from thefuzz import utils

# Standard text normalization
text = "  Hello, World!  "
processed = utils.full_process(text)
print(processed)  # "hello world"

# Handle special characters
text = "New York Mets vs. Atlanta Braves"
processed = utils.full_process(text)  
print(processed)  # "new york mets vs atlanta braves"

from thefuzz import utils

# Convert accented characters
text = "Café Münchën"
ascii_text = utils.ascii_only(text)
print(ascii_text)  # "Caf Mnchen"

# Full processing with ASCII conversion
processed = utils.full_process("Café Münchën", force_ascii=True)
print(processed)  # "caf mnchen"

from thefuzz import fuzz, utils

# Manual preprocessing before comparison
s1 = utils.full_process("New York Mets!")
s2 = utils.full_process("new york mets")
score = fuzz.ratio(s1, s2)
print(score)  # 100 (perfect match after processing)

# Compare with and without processing
raw_score = fuzz.ratio("New York Mets!", "new york mets")
processed_score = fuzz.ratio(
    utils.full_process("New York Mets!"),
    utils.full_process("new york mets")
)
print(f"Raw: {raw_score}, Processed: {processed_score}")

from thefuzz import utils

def custom_processor(text):
    """Custom processing for specific use case."""
    # First apply standard processing
    processed = utils.full_process(text, force_ascii=True)
    
    # Add custom logic
    # Remove common stop words, normalize abbreviations, etc.
    replacements = {
        "street": "st",
        "avenue": "ave", 
        "boulevard": "blvd"
    }
    
    for old, new in replacements.items():
        processed = processed.replace(old, new)
    
    return processed

# Use with fuzzy matching
from thefuzz import process

addresses = ["123 Main Street", "456 Oak Avenue", "789 First Boulevard"]
result = process.extractOne("main st", addresses, processor=custom_processor)

from thefuzz import utils

# For batch processing, consider preprocessing once
texts = ["Text 1", "Text 2", "Text 3", ...]
processed_texts = [utils.full_process(text) for text in texts]

# Then use the processed texts for multiple comparisons
# This avoids repeated preprocessing in fuzzy matching functions

from thefuzz import utils

examples = [
    "Hello, World!",           # → "hello world"
    "  Multiple   Spaces  ",   # → "multiple spaces"
    "New York Mets vs. ATL",   # → "new york mets vs atl"
    "Café Münchën",           # → "café münchën" (or "caf mnchen" with force_ascii=True)
    "user@email.com",          # → "user email com"
    "1st & 2nd Avenue",        # → "1st 2nd avenue"
]

for text in examples:
    processed = utils.full_process(text)
    processed_ascii = utils.full_process(text, force_ascii=True)
    print(f"'{text}' → '{processed}' → '{processed_ascii}'")