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 more than one Russian characters by checking
    each character against the Unicode range for Cyrillic.
    """
    counter = 0
    for char in text:
        if "\u0400" <= char <= "\u04ff":  # Unicode range for Cyrillic characters
            counter += 1
        if counter > 1:
            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
    t = segment_text(
        text.replace("  ", " ").replace("  ", " ").replace("  ", " ")
    )

    if is_russian_wording(t):
        # Normalize the text by replacing characters
        normalized_text = (
            t.lower()
            .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 t


# Example usage
if __name__ == "__main__":
    input_text = "Hello, this is a test input."

    normalized_output = normalize(input_text)
    print(normalized_output)