import logging
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Initialize the T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("google/byt5-small")
model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")

def is_russian_wording(text):
    """
    Check if the text contains any Russian characters by checking 
    each character against the Unicode range for Cyrillic.
    """
    for char in text:
        if '\u0400' <= char <= '\u04FF':  # Unicode range for Cyrillic characters
            return True
    return False

def segment_text(text):
    """
    Use a neural network model to segment text into words.
    """
    # Encode the input text for the model
    inputs = tokenizer.encode("segment: " + text, return_tensors="pt")
    
    # Generate predictions
    with torch.no_grad():
        outputs = model.generate(inputs)
    
    # Decode the generated tokens back to text
    segmented_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return segmented_text

def normalize(text):
    """
    Normalize English text to resemble Russian characters.
    """
    # Segment the text first
    segmented_text = segment_text(text.replace('  ', ' ').replace('  ', ' ').replace('  ', ' '))
    
    # Normalize after segmentation
    segmented_text = segmented_text.lower()
    
    if is_russian_wording(segmented_text):
        # Normalize the text by replacing characters
        normalized_text = (segmented_text
                           .replace('e', 'е')
                           .replace('o', 'о')
                           .replace('x', 'х')
                           .replace('a', 'а')
                           .replace('r', 'г')
                           .replace('m', 'м')
                           .replace('u', 'и')
                           .replace('n', 'п')
                           .replace('p', 'р')
                           .replace('t', 'т')
                           .replace('y', 'у')
                           .replace('h', 'н')
                           .replace('i', 'й')
                           .replace('c', 'с')
                           .replace('k', 'к')
                           .replace('b', 'в')
                           .replace('3', 'з')
                           .replace('4', 'ч')
                           .replace('0', 'о')
                           .replace('d', 'д')
                           .replace('z', 'з'))
        
        return normalized_text
    
    return segmented_text

# Example usage
if __name__ == "__main__":
    input_text = "Hello, this is a test input."
    
    normalized_output = normalize(input_text)
    print(normalized_output)