I don’t know reason that error above appears, although I have directory as in n title.
D:/Języki programowania/Python/Projekty/Pneumonia_detection/chest_xray/chest_xray/train

import pandas as pd
import tensorflow as tf 
import numpy as np
import matplotlib.pyplot as plt


import warnings
warnings.filterwarnings('ignore')

from tensorflow import keras
from keras import layers
from tensorflow.keras.models import Sequential # type: ignore
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense # type: ignore
from tensorflow.keras.layers import Conv2D, MaxPooling2D # type: ignore
from tensorflow.keras.utils import image_dataset_from_directory # type: ignore
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img # type: ignore
from tensorflow.keras.preprocessing import image_dataset_from_directory # type: ignore

import os
import matplotlib.image as mpimg

# Importing dataset
import zipfile

dataset = zipfile.ZipFile("D:/Downloads/archive.zip", 'r')
dataset.extractall()
dataset.close()

# Read the image data
path = 'D:/Downloads/chest_xray/chest_xray/train'

path = 'D:/Języki programowania/Python/Projekty/Pneumonia_Detection_CNN/kaggle/chest_xray/chest_xray/train'

classes = os.listdir(path)
print(classes)

# Define directories for X-ray Images
PNEUMONIA_dir = os.path.join(path + '/' + 'PNEUMONIA')
NORMAL_dir = os.path.join(path + '/' + 'NORMAL')

# Create list of files names in each directory
pneumonia_names = os.listdir(PNEUMONIA_dir)
normal_names = os.listdir(NORMAL_dir)

print("There is", len(pneumonia_names), "images of pneumonia infected in training dataset")
print("There is", len(normal_names), "normal images in training dataset")

# Plot the Pneumonia infected Chest X-Ray images
fig = plt.gcf()
fig.set_size_inches(24, 12)

pic_index = 210

pneumonia_images = [os.path.join(PNEUMONIA_dir, fname)
                    for fname in pneumonia_names[pic_index-8:pic_index]]
for i, img_path in enumerate(pneumonia_images):
    sp = plt.subplot(2, 4, i+1)
    sp.axis('Off')
    
    img = mpimg.imread(img_path)
    plt.imshow(img)

plt.show()

# Plot the Normal Chest X-Ray Images
fig = plt.gcf()
fig.set_size_inches(24, 12)

pic_index = 210

normal_images = [os.path.join(NORMAL_dir, fname)
                 for fname in normal_names[pic_index-8:pic_index]]
for i, img_path in enumerate(normal_images):
    sp = plt.subplot(2, 4, i+1)
    sp.axis('Off')
    
    img = mpimg.imread(img_path)
    plt.imshow(img)
    
plt.show() 


base_dir = 'D:/Języki programowania/Python/Projekty/Pneumonia_detection/chest_xray/chest_xray'
Train_dir = os.path.join(base_dir + '/' + 'train')
if not os.path.exists(Train_dir):
    os.mkdir(Train_dir)
Test_dir = os.path.join(base_dir + '/' +  'test')
if not os.path.exists(Test_dir):
    os.mkdir(Test_dir)
Val_dir = os.path.join(base_dir + '/' + 'val')
if not os.path.exists(Val_dir):
    os.mkdir(Val_dir)


Train = keras.utils.image_dataset_from_directory(
    Train_dir,
    labels = "inferred",
    label_mode = "categorical",
    batch_size = 32,
    image_size = (256, 256)
)
Test = keras.utils.image_dataset_from_directory(
    Test_dir,
    labels = "inferred",
    label_mode = "categorical",
    batch_size = 32,
    image_size = (256, 256)
)
Validation = keras.utils.image_dataset_from_directory(
    Val_dir,
    labels = "inferred",
    label_mode = "categorical",
    batch_size = 32,
    image_size = (256, 256)
)

I expect result as below:
Found 5216 files belonging to 2 classes.
Found 624 files belonging to 2 classes.
Found 16 files belonging to 2 classes.