


'''simple docstring'''


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCamelCase__     						=  180






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							Optional[int]             =   k_size // 2
		_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		_UpperCAmelCase		:							Union[str, Any]             =   (
		    1
		    / (2 * np.pi * sigma)
		    * np.exp(-(np.square(__lowerCAmelCase ) + np.square(__lowerCAmelCase )) / (2 * np.square(__lowerCAmelCase )) )
		)
		return g






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							List[str]             =   np.zeros(__lowerCAmelCase )

		for row in range(1   ,			image_shape[0] - 1 ):
				for col in range(1   ,			image_shape[1] - 1 ):
						_UpperCAmelCase		:							List[Any]             =   gradient_direction[row, col]

						if (
						    0 <= direction < PI / 8
						    or 15 * PI / 8 <= direction <= 2 * PI
						    or 7 * PI / 8 <= direction <= 9 * PI / 8
						):
								_UpperCAmelCase		:							str             =   sobel_grad[row, col - 1]
								_UpperCAmelCase		:							Tuple             =   sobel_grad[row, col + 1]
								if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
										_UpperCAmelCase		:							List[str]             =   sobel_grad[row, col]

						elif (
						    PI / 8 <= direction < 3 * PI / 8
						    or 9 * PI / 8 <= direction < 11 * PI / 8
						):
								_UpperCAmelCase		:							Tuple             =   sobel_grad[row + 1, col - 1]
								_UpperCAmelCase		:							Optional[Any]             =   sobel_grad[row - 1, col + 1]
								if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
										_UpperCAmelCase		:							List[str]             =   sobel_grad[row, col]

						elif (
						    3 * PI / 8 <= direction < 5 * PI / 8
						    or 11 * PI / 8 <= direction < 13 * PI / 8
						):
								_UpperCAmelCase		:							int             =   sobel_grad[row - 1, col]
								_UpperCAmelCase		:							Union[str, Any]             =   sobel_grad[row + 1, col]
								if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
										_UpperCAmelCase		:							Optional[int]             =   sobel_grad[row, col]

						elif (
						    5 * PI / 8 <= direction < 7 * PI / 8
						    or 13 * PI / 8 <= direction < 15 * PI / 8
						):
								_UpperCAmelCase		:							List[str]             =   sobel_grad[row - 1, col - 1]
								_UpperCAmelCase		:							str             =   sobel_grad[row + 1, col + 1]
								if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
										_UpperCAmelCase		:							List[Any]             =   sobel_grad[row, col]

		return destination






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		for row in range(1   ,			image_shape[0] - 1 ):
				for col in range(1   ,			image_shape[1] - 1 ):
						if destination[row, col] >= threshold_high:
								_UpperCAmelCase		:							str             =   strong
						elif destination[row, col] <= threshold_low:
								_UpperCAmelCase		:							str             =   0
						else:
								_UpperCAmelCase		:							Union[str, Any]             =   weak






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		for row in range(1   ,			image_shape[0] ):
				for col in range(1   ,			image_shape[1] ):
						if destination[row, col] == weak:
								if 255 in (
								    destination[row, col + 1],
								    destination[row, col - 1],
								    destination[row - 1, col],
								    destination[row + 1, col],
								    destination[row - 1, col - 1],
								    destination[row + 1, col - 1],
								    destination[row - 1, col + 1],
								    destination[row + 1, col + 1],
								):
										_UpperCAmelCase		:							List[str]             =   strong
								else:
										_UpperCAmelCase		:							Optional[int]             =   0






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=15   ,			__lowerCAmelCase=30   ,			__lowerCAmelCase=128   ,			__lowerCAmelCase=255 ):
		# gaussian_filter
		_UpperCAmelCase		:							List[Any]             =   img_convolve(__lowerCAmelCase   ,			gen_gaussian_kernel(9   ,			sigma=1.4 ) )
		# get the gradient and degree by sobel_filter
		_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[int]             =   sobel_filter(__lowerCAmelCase )
		_UpperCAmelCase		:							List[str]             =   PI + np.radadeg(__lowerCAmelCase )

		_UpperCAmelCase		:							int             =   suppress_non_maximum(image.shape   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		detect_high_low_threshold(
		    image.shape   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		track_edge(image.shape   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		return destination


if __name__ == "__main__":
						# read original image in gray mode
						lowerCamelCase__     						=  cva.imread(r'../image_data/lena.jpg', 0)
						# canny edge detection
						lowerCamelCase__     						=  canny(lena)
						cva.imshow('canny', canny_destination)
						cva.waitKey(0)







'''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 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Optional[int]             =   Dataset.from_dict({"i": range(17 )} )
		_UpperCAmelCase		:							int             =   len(__lowerCAmelCase )
		_UpperCAmelCase		:							Dict             =   3
		_UpperCAmelCase		:							str             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert sum(len(__lowerCAmelCase ) 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 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							Any             =   3
		_UpperCAmelCase		:							Any             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							str             =   len(list(__lowerCAmelCase ) )
		_UpperCAmelCase		:							Tuple             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert sum(len(list(__lowerCAmelCase ) ) 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 			__lowerCAmelCase			(__lowerCAmelCase ):
		def gen(__lowerCAmelCase ):
				for shard in shards:
						yield from ({"i": i, "shard": shard} for i in range(17 ))

		_UpperCAmelCase		:							Tuple             =   3
		_UpperCAmelCase		:							Tuple             =   shards_per_node * world_size
		_UpperCAmelCase		:							Optional[int]             =   {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowerCAmelCase )]}
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase   ,			gen_kwargs=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   len(list(__lowerCAmelCase ) )
		assert full_ds.n_shards == world_size * shards_per_node
		_UpperCAmelCase		:							Optional[Any]             =   [
		    split_dataset_by_node(__lowerCAmelCase   ,			rank=__lowerCAmelCase   ,			world_size=__lowerCAmelCase ) for rank in range(__lowerCAmelCase )
		]
		assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
		assert sum(len(list(__lowerCAmelCase ) ) 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 			__lowerCAmelCase			():
		def gen():
				return ({"i": i} for i in range(17 ))

		_UpperCAmelCase		:							List[str]             =   2
		_UpperCAmelCase		:							int             =   IterableDataset.from_generator(__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   len(list(__lowerCAmelCase ) )

		_UpperCAmelCase		:							int             =   split_dataset_by_node(__lowerCAmelCase   ,			rank=0   ,			world_size=__lowerCAmelCase ).shuffle(seed=42 )
		assert len(list(__lowerCAmelCase ) ) == 1 + full_size // world_size
		with pytest.raises(__lowerCAmelCase ):
				split_dataset_by_node(__lowerCAmelCase   ,			rank=0   ,			world_size=__lowerCAmelCase ).shuffle()

		_UpperCAmelCase		:							Dict             =   split_dataset_by_node(full_ds.shuffle(seed=42 )   ,			rank=0   ,			world_size=__lowerCAmelCase )
		assert len(list(__lowerCAmelCase ) ) == 1 + full_size // world_size
		with pytest.raises(__lowerCAmelCase ):
				split_dataset_by_node(full_ds.shuffle()   ,			rank=0   ,			world_size=__lowerCAmelCase )






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



