
'''simple docstring'''

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 	_UpperCAmelCase				(  )    ->						Optional[int]:
  _lowerCAmelCase       :  Tuple   				=					Dataset.from_dict({"""i""": range(17		)}		)
  _lowerCAmelCase       :  Optional[int]   				=					len(_A		)
  _lowerCAmelCase       :  List[str]   				=					3
  _lowerCAmelCase       :  Dict   				=					[
      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 	_UpperCAmelCase				(  )    ->						int:


  def gen():
    return ({"i": i} for i in range(17		))

  _lowerCAmelCase       :  Any   				=					3
  _lowerCAmelCase       :  str   				=					IterableDataset.from_generator(_A		)
  _lowerCAmelCase       :  Optional[int]   				=					len(list(_A		)		)
  _lowerCAmelCase       :  List[str]   				=					[
      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 	_UpperCAmelCase				(  _lowerCamelCase						:       Any		)    ->						Union[str, Any]:


  def gen(_lowerCamelCase						:       Dict		):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17		))

  _lowerCAmelCase       :  int   				=					3
  _lowerCAmelCase       :  Any   				=					shards_per_node * world_size
  _lowerCAmelCase       :  Optional[int]   				=					{"""shards""": [f'shard_{shard_idx}.txt' for shard_idx in range(_A		)]}
  _lowerCAmelCase       :  Tuple   				=					IterableDataset.from_generator(_A  ,      gen_kwargs=_A		)
  _lowerCAmelCase       :  List[str]   				=					len(list(_A		)		)
  assert full_ds.n_shards == world_size * shards_per_node
  _lowerCAmelCase       :  Tuple   				=					[
      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 	_UpperCAmelCase				(  )    ->						Any:


  def gen():
    return ({"i": i} for i in range(17		))

  _lowerCAmelCase       :  Any   				=					2
  _lowerCAmelCase       :  Optional[Any]   				=					IterableDataset.from_generator(_A		)
  _lowerCAmelCase       :  str   				=					len(list(_A		)		)

  _lowerCAmelCase       :  Dict   				=					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()

  _lowerCAmelCase       :  Union[str, 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 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[Any]		)    ->						Any:
  _lowerCAmelCase       :  Any   				=					2
  _lowerCAmelCase       :  Union[str, Any]   				=					get_torch_dist_unique_port()
  _lowerCAmelCase       :  Optional[int]   				=					Path(_A		).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
  _lowerCAmelCase       :  Dict   				=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  _lowerCAmelCase       :  int   				=					f'\n        --streaming={streaming}\n    '.split()
  _lowerCAmelCase       :  Optional[Any]   				=					[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 	_UpperCAmelCase				(  _lowerCamelCase						:       Tuple  ,      _lowerCamelCase						:       Optional[int]		)    ->						Any:
  _lowerCAmelCase       :  Optional[Any]   				=					True
  _lowerCAmelCase       :  Dict   				=					get_torch_dist_unique_port()
  _lowerCAmelCase       :  Tuple   				=					Path(_A		).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
  _lowerCAmelCase       :  Union[str, Any]   				=					f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
  _lowerCAmelCase       :  Optional[int]   				=					f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
  _lowerCAmelCase       :  Dict   				=					[sys.executable] + distributed_args + args
  execute_subprocess_async(_A  ,      env=os.environ.copy()		)




'''simple docstring'''

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 	_UpperCAmelCase				(  _lowerCamelCase						:       str  ,      _lowerCamelCase						:       List[Any]=3		)    ->						Union[str, Any]:
  _lowerCAmelCase       :  str   				=					int(mask / 2		)
  # copy image size
  _lowerCAmelCase       :  Dict   				=					zeros_like(_lowerCamelCase		)
  for i in range(_lowerCamelCase  ,      gray_img.shape[0] - bd		):
    for j in range(_lowerCamelCase  ,      gray_img.shape[1] - bd		):
      # get mask according with mask
      _lowerCAmelCase       :  Union[str, Any]   				=					ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
      # calculate mask median
      _lowerCAmelCase       :  str   				=					sort(_lowerCamelCase		)[inta(divide((multiply(_lowerCamelCase  ,      _lowerCamelCase		))  ,      2		) + 1		)]
      _lowerCAmelCase       :  Dict   				=					median
  return median_img


if __name__ == "__main__":
     # read original image
     UpperCamelCase_     			=       imread("""../image_data/lena.jpg""")
     # turn image in gray scale value
     UpperCamelCase_     			=       cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     UpperCamelCase_     			=       median_filter(gray, 3)
     UpperCamelCase_     			=       median_filter(gray, 5)

     # show result images
     imshow("""median filter with 3x3 mask""", medianaxa)
     imshow("""median filter with 5x5 mask""", medianaxa)
     waitKey(0)


