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import argparse
import math
import os
import time
from typing import List, Optional, Sequence, Tuple

import numpy as np
import rasterio
import rasterio.windows
import torch

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def batch_histogram(data_tensor, num_classes=-1):
    """
    From https://discuss.pytorch.org/t/batched-torch-histc/179741
    
    Computes histograms of integral values, even if in batches (as opposed to torch.histc and torch.histogram).
    Arguments:
        data_tensor: a D1 x ... x D_n torch.LongTensor
        num_classes (optional): the number of classes present in data.
                                If not provided, tensor.max() + 1 is used (an error is thrown if tensor is empty).
    Returns:

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#!/usr/bin/env python3
"""
benchmark_sieve.py

Benchmark sieving, polygonizing, and simplifying GeoTIFFs using two methods:
  - "gdal": subprocess calls to gdal_sieve.py, gdal_polygonize.py, and ogr2ogr
  - "python": pure Python using rasterio.features, shapely, and fiona
"""

import os

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class RCFSegmentationFeatures(RCF):
    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """Forward pass of the RCF model.

        Args:
            x: a tensor with shape (C, H, W)
            y: a tensor with shape (H, W)

        Returns:
            a tensor of size (``self.num_features``)

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def check_if_scene_is_cloudy_at_box(item: pystac.Item, box: shapely.geometry.Polygon):
    """Uses the S2 Scene Classification Layer (SCL) to determine if a S2 L2A scene is cloudy at a given bbox.

    Args:
        item (pystac.Item): The S2 L2A item to check
        box (shapely.geometry.Polygon): The geometry to check (should be EPSG:4326)

    Returns:
        float: The fraction of the box that is classified as "Cloud medium probability" + "Cloud high probability"
    """

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