





'''simple docstring'''

from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve





def 		UpperCamelCase       (							_lowerCamelCase      :      np.ndarray		, _lowerCamelCase      :      np.ndarray		, _lowerCamelCase      :      np.ndarray ):
    A__       =    np.stack((horizontal_flow, vertical_flow)		, 2 )

    # Create a grid of all pixel coordinates and subtract the flow to get the
    # target pixels coordinates
    A__       =    np.stack(
        np.meshgrid(np.arange(0		, image.shape[1] )		, np.arange(0		, image.shape[0] ) )		, 2 )
    A__       =    np.round(grid - flow ).astype(np.intaa )

    # Find the locations outside of the original image
    A__       =    (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]] ))
    A__       =    0

    A__       =    image[grid[:, :, 1], grid[:, :, 0]]

    # Set pixels at invalid locations to 0
    A__       =    0

    return warped





def 		UpperCamelCase       (							_lowerCamelCase      :      np.ndarray		, _lowerCamelCase      :      np.ndarray		, _lowerCamelCase      :      SupportsIndex		, _lowerCamelCase      :      float | None = None		, ):
    if alpha is None:
        A__       =    0.1

    # Initialize flow
    A__       =    np.zeros_like(_lowerCamelCase )
    A__       =    np.zeros_like(_lowerCamelCase )

    # Prepare kernels for the calculation of the derivatives and the average velocity
    A__       =    np.array([[-1, 1], [-1, 1]] ) * 0.2_5
    A__       =    np.array([[-1, -1], [1, 1]] ) * 0.2_5
    A__       =    np.array([[1, 1], [1, 1]] ) * 0.2_5
    A__       =    np.array(
        [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]] )

    # Iteratively refine the flow
    for _ in range(_lowerCamelCase ):
        A__       =    warp(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
        A__       =    convolve(_lowerCamelCase		, _lowerCamelCase ) + convolve(_lowerCamelCase		, _lowerCamelCase )
        A__       =    convolve(_lowerCamelCase		, _lowerCamelCase ) + convolve(_lowerCamelCase		, _lowerCamelCase )
        A__       =    convolve(_lowerCamelCase		, _lowerCamelCase ) + convolve(_lowerCamelCase		, -kernel_t )

        A__       =    convolve(_lowerCamelCase		, _lowerCamelCase )
        A__       =    convolve(_lowerCamelCase		, _lowerCamelCase )

        # This updates the flow as proposed in the paper (Step 12)
        A__       =    (
            derivative_x * avg_horizontal_velocity
            + derivative_y * avg_vertical_velocity
            + derivative_t
        )
        A__       =    update / (alpha**2 + derivative_x**2 + derivative_y**2)

        A__       =    avg_horizontal_velocity - derivative_x * update
        A__       =    avg_vertical_velocity - derivative_y * update

    return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''simple docstring'''

import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
    A__       =    copy.deepcopy(_lowerCamelCase )
    A__       =    sorted(_lowerCamelCase )
    A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
    return quantized





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
    A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
    A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
    # count number of elements per group
    A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
    A__       =    [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase ) )
    logger.info("Count of instances per bin: {}".format(_lowerCamelCase ) )
    return groups






class      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
     if not isinstance(lowercase_		, lowercase_							):
         raise ValueError(
             "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
     A__       =    sampler
     A__       =    group_ids
     A__       =    batch_size
 def __iter__(       self   :int							)->							Tuple:
     A__       =    defaultdict(lowercase_							)
     A__       =    defaultdict(lowercase_							)

     A__       =    0
     for idx in self.sampler:
         A__       =    self.group_ids[idx]
         buffer_per_group[group_id].append(lowercase_							)
         samples_per_group[group_id].append(lowercase_							)
         if len(buffer_per_group[group_id]							) == self.batch_size:
             yield buffer_per_group[group_id]  # TODO
             num_batches += 1
             del buffer_per_group[group_id]
         assert len(buffer_per_group[group_id]							) < self.batch_size

     # now we have run out of elements that satisfy
     # the group criteria, let's return the remaining
     # elements so that the size of the sampler is
     # deterministic
     A__       =    len(self							)
     A__       =    expected_num_batches - num_batches
     if num_remaining > 0:
         # for the remaining batches, group the batches by similar lengths
         A__       =    []
         for group_id, idxs in sorted(buffer_per_group.items()		, key=lambda lowercase_							: x[0]							):
             batch_idx.extend(lowercase_							)
             if len(lowercase_							) >= self.batch_size:
                 yield batch_idx[: self.batch_size]
                 A__       =    batch_idx[self.batch_size :]
                 num_remaining -= 1
         if len(lowercase_							) > 0:
             yield batch_idx
             num_remaining -= 1
     assert num_remaining == 0



 def __len__(       self   :int							)->							Tuple:
     return (len(self.sampler							) + self.batch_size - 1) // self.batch_size



