import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''content''': datasets.Value('''string'''   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple   )							->		int:


									'''simple docstring'''

									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_dummy_examples()}   )]



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






class      __lowercase					(    datasets.BeamBasedBuilder    ):

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return datasets.DatasetInfo(
									    features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )							,							supervised_keys=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

									return [
									    datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'''examples''': get_test_nested_examples()}   )
									]



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									import apache_beam as beam

									return pipeline | "Load Examples" >> beam.Create(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Dict:



			"""simple docstring"""







			return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






def        __UpperCAmelCase    (       )->       List[str]:



			"""simple docstring"""







			return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar''']       )]






class      __lowercase					(    _A    ):

						@require_beam
						def        __a		(	self				:      Optional[int]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_dummy_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_dummy_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      Union[str, Any]   )							->		List[Any]:


									'''simple docstring'''

									import apache_beam as beam

									lowercase							=		beam.io.parquetio.WriteToParquet

									lowercase							=		len(get_test_dummy_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												with patch('''apache_beam.io.parquetio.WriteToParquet'''   ) as write_parquet_mock:
															lowercase							=		partial(__lowerCamelCase							,							num_shards=2   )
															builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertTrue(
												    os.path.exists(
												        os.path.join(
												            __lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train-00000-of-00002.arrow'   )   )   )
												self.assertDictEqual(builder.info.features							,							datasets.Features({'''content''': datasets.Value('''string'''   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												# Order is not preserved when sharding, so we just check that all the elements are there
												self.assertListEqual(sorted(dset['''train''']['''content''']   )							,							sorted(['''foo''', '''bar''', '''foobar''']   )   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset

						@require_beam
						def        __a		(	self				:      int   )							->		int:


									'''simple docstring'''

									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		DummyBeamDataset(cache_dir=__lowerCamelCase   )
												self.assertRaises(datasets.builder.MissingBeamOptions							,							builder.download_and_prepare   )



						@require_beam
						def        __a		(	self				:      str   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		len(get_test_nested_examples()   )
									with tempfile.TemporaryDirectory() as tmp_cache_dir:
												lowercase							=		NestedBeamDataset(cache_dir=__lowerCamelCase							,							beam_runner='''DirectRunner'''   )
												builder.download_and_prepare()
												self.assertTrue(
												    os.path.exists(
												        os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							f'{builder.name}-train.arrow'   )   )   )
												self.assertDictEqual(
												    builder.info.features							,							datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string'''   )}   )}   )   )
												lowercase							=		builder.as_dataset()
												self.assertEqual(dset['''train'''].num_rows							,							__lowerCamelCase   )
												self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples							,							__lowerCamelCase   )
												self.assertDictEqual(dset['''train'''][0]							,							get_test_nested_examples()[0][1]   )
												self.assertDictEqual(
												    dset['''train'''][expected_num_examples - 1]							,							get_test_nested_examples()[expected_num_examples - 1][1]   )
												self.assertTrue(
												    os.path.exists(os.path.join(__lowerCamelCase							,							builder.name							,							'''default'''							,							'''0.0.0'''							,							'''dataset_info.json'''   )   )   )
												del dset





