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import argparse


def get_parser() -> argparse.ArgumentParser:
    ...


def process_args(args: argparse.Namespace) -> argparse.Namespace:
    ...

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import json
from enum import Enum
from typing import get_origin, get_args, Any, Dict, List

import streamlit as st
from pydantic import BaseModel


# Example Enum
class Color(str, Enum):

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def get_files_in_dir(rootdir: str , suffix: str, subdirs: bool = True) -> List[str]:
    rootdir = Path(str(rootdir))
    
    if subdirs:
        glob_pattern = "**/*"
    else:
        glob_pattern = "*"
        
    files_lower = list(rootdir.glob(f"{glob_pattern}{suffix.lower()}"))
    files_upper = list(rootdir.glob(f"{glob_pattern}{suffix.upper()}"))

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import logging
from copy import deepcopy
from typing import List, Dict, Any
from pathlib import Path

from shapely.geometry import box as bbox_to_polygon
from tqdm import tqdm
import rasterio
import rasterio.warp

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from distutils.core import setup
from setuptools import find_packages

requirements = [r for r in open("requirements.txt", "r").read().splitlines() if not r.startswith("#")]

setup(name='MyPackage',
      version='1.0',
      description='Description of my package',
      author='author-name',
      author_email='author@email.com',

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"""
This code will split the MyDataset class into a training set and a validation set, 
using the train_test_split function from scikit-learn to generate the indices for 
the training and validation sets. 
The train_test_split function randomly splits the indices into two sets, 
with the training set containing 80% of the data and the validation set containing the remaining 20%. 
The training and validation sets are then created using the Subset class and wrapped in PyTorch DataLoaders, 
which can be used to iterate over the datasets and feed the data into a PyTorch model during training and evaluation.
"""

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import os
import sys
from pathlib import Path

# create absolute paths for this file and parents
__realpath__ = str(Path(os.path.realpath(__file__)))
__parents__ = [str(p) for p in Path(__realpath__).parents]

# add to PATH so that we can `import ...`
sys.path.append(__parents__[0])

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def get_py_locals(filepath: str):
    file_locals = {}
    with open(filepath) as f:
        content = f.read()
        exec(content, None, file_locals)
    return file_locals