






import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def       a__						(  ):
   lowercase__						:	str         = Dataset.from_dict({"""i""": range(17					)}					)
   lowercase__						:	Optional[int]         = len(A					)
   lowercase__						:	int         = 3
   lowercase__						:	Tuple         = [
       split_dataset_by_node(A		,				rank=A		,				world_size=A					) for rank in range(A					)
   ]
   assert sum(len(A					) for ds in datasets_per_rank					) == full_size
   assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
def       a__						(  ):
   def gen():
      return ({"i": i} for i in range(17					))

   lowercase__						:	Tuple         = 3
   lowercase__						:	str         = IterableDataset.from_generator(A					)
   lowercase__						:	Dict         = len(list(A					)					)
   lowercase__						:	int         = [
       split_dataset_by_node(A		,				rank=A		,				world_size=A					) for rank in range(A					)
   ]
   assert sum(len(list(A					)					) for ds in datasets_per_rank					) == full_size
   assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
@pytest.mark.parametrize("""shards_per_node"""		,				[1, 2, 3]					)
def       a__						(  A					):
   def gen(A					):
      for shard in shards:
         yield from ({"i": i, "shard": shard} for i in range(17					))

   lowercase__						:	List[str]         = 3
   lowercase__						:	str         = shards_per_node * world_size
   lowercase__						:	Optional[Any]         = {"""shards""": [F'''shard_{shard_idx}.txt''' for shard_idx in range(A					)]}
   lowercase__						:	List[Any]         = IterableDataset.from_generator(A		,				gen_kwargs=A					)
   lowercase__						:	List[Any]         = len(list(A					)					)
   assert full_ds.n_shards == world_size * shards_per_node
   lowercase__						:	Any         = [
       split_dataset_by_node(A		,				rank=A		,				world_size=A					) for rank in range(A					)
   ]
   assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
   assert sum(len(list(A					)					) for ds in datasets_per_rank					) == full_size
   assert len({tuple(x.values()					) for ds in datasets_per_rank for x in ds}					) == full_size
def       a__						(  ):
   def gen():
      return ({"i": i} for i in range(17					))

   lowercase__						:	Tuple         = 2
   lowercase__						:	str         = IterableDataset.from_generator(A					)
   lowercase__						:	Optional[int]         = len(list(A					)					)

   lowercase__						:	Any         = split_dataset_by_node(A		,				rank=0		,				world_size=A					).shuffle(seed=42					)
   assert len(list(A					)					) == 1 + full_size // world_size
   with pytest.raises(A					):
      split_dataset_by_node(A		,				rank=0		,				world_size=A					).shuffle()

   lowercase__						:	List[Any]         = split_dataset_by_node(full_ds.shuffle(seed=42					)		,				rank=0		,				world_size=A					)
   assert len(list(A					)					) == 1 + full_size // world_size
   with pytest.raises(A					):
      split_dataset_by_node(full_ds.shuffle()		,				rank=0		,				world_size=A					)
@pytest.mark.parametrize("""streaming"""		,				[False, True]					)
@require_torch
@pytest.mark.skipif(os.name == """nt"""		,				reason="""execute_subprocess_async doesn't support windows"""					)
@pytest.mark.integration
def       a__						(  A					):
   lowercase__						:	Any         = 2
   lowercase__						:	Tuple         = get_torch_dist_unique_port()
   lowercase__						:	Optional[Any]         = Path(A					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
   lowercase__						:	Dict         = F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
   lowercase__						:	List[str]         = F'''
        --streaming={streaming}
    '''.split()
   lowercase__						:	List[str]         = [sys.executable] + distributed_args + args
   execute_subprocess_async(A		,				env=os.environ.copy()					)






@pytest.mark.parametrize(
    """nproc_per_node, num_workers"""		,				[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]		,				)
@require_torch
@pytest.mark.skipif(os.name == """nt"""		,				reason="""execute_subprocess_async doesn't support windows"""					)
@pytest.mark.integration
def       a__						(  A		,				A					):
   lowercase__						:	List[Any]         = True
   lowercase__						:	List[str]         = get_torch_dist_unique_port()
   lowercase__						:	Optional[int]         = Path(A					).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
   lowercase__						:	List[str]         = F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
   lowercase__						:	List[Any]         = F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
   lowercase__						:	Any         = [sys.executable] + distributed_args + args
   execute_subprocess_async(A		,				env=os.environ.copy()					)










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Dict         = np.stack((horizontal_flow, vertical_flow)		,				2					)

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

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

   lowercase__						:	Tuple         = image[grid[:, :, 1], grid[:, :, 0]]

   # Set pixels at invalid locations to 0
   lowercase__						:	str         = 0

   return warped
def       a__						(  A		,				A		,				A		,				A = None		,				):
   if alpha is None:
      lowercase__						:	Tuple         = 0.1

   # Initialize flow
   lowercase__						:	Optional[int]         = np.zeros_like(A					)
   lowercase__						:	Tuple         = np.zeros_like(A					)

   # Prepare kernels for the calculation of the derivatives and the average velocity
   lowercase__						:	Tuple         = np.array([[-1, 1], [-1, 1]]					) * 0.2_5
   lowercase__						:	int         = np.array([[-1, -1], [1, 1]]					) * 0.2_5
   lowercase__						:	Optional[int]         = np.array([[1, 1], [1, 1]]					) * 0.2_5
   lowercase__						:	Optional[Any]         = 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(A					):
      lowercase__						:	Optional[int]         = warp(A		,				A		,				A					)
      lowercase__						:	Union[str, Any]         = convolve(A		,				A					) + convolve(A		,				A					)
      lowercase__						:	Union[str, Any]         = convolve(A		,				A					) + convolve(A		,				A					)
      lowercase__						:	str         = convolve(A		,				A					) + convolve(A		,				-kernel_t					)

      lowercase__						:	str         = convolve(A		,				A					)
      lowercase__						:	Tuple         = convolve(A		,				A					)

      # This updates the flow as proposed in the paper (Step 12)
      lowercase__						:	List[Any]         = (
          derivative_x * avg_horizontal_velocity
          + derivative_y * avg_vertical_velocity
          + derivative_t
      )
      lowercase__						:	Union[str, Any]         = update / (alpha**2 + derivative_x**2 + derivative_y**2)

      lowercase__						:	Optional[int]         = avg_horizontal_velocity - derivative_x * update
      lowercase__						:	int         = avg_vertical_velocity - derivative_y * update

   return horizontal_flow, vertical_flow


if __name__ == "__main__":
 import doctest

 doctest.testmod()


