


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase						=		5_0000
lowerCamelCase						=		5000

lowerCamelCase,       lowerCamelCase						=		os.path.split(__file__)
lowerCamelCase						=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))






@get_duration
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		for i in range(_a	):
				lowerCAmelCase__       :      Tuple   					= dataset[i]






@get_duration
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		for i in range(0     ,       len(_a	)     ,       _a	):
				lowerCAmelCase__       :      Optional[int]   					= dataset[i : i + batch_size]






@get_duration
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		with dataset.formatted_as(type=_a	):
				for i in range(_a	):
						lowerCAmelCase__       :      Any   					= dataset[i]






@get_duration
def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		with dataset.formatted_as(type=_a	):
				for i in range(0     ,       _a     ,       _a	):
						lowerCAmelCase__       :      Optional[int]   					= dataset[i : i + batch_size]






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= {'''num examples''': SPEED_TEST_N_EXAMPLES}
		lowerCAmelCase__       :      Optional[int]   					= [
		    (read, {'''length''': SMALL_TEST}),
		    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
		    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
		    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
		    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
		    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
		    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
		    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
		]

		lowerCAmelCase__       :      Optional[Any]   					= [
		    (read, {'''length''': SMALL_TEST}),
		    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
		    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_000}),
		    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
		    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
		    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_000}),
		]
		with tempfile.TemporaryDirectory() as tmp_dir:
				print('''generating dataset'''	)
				lowerCAmelCase__       :      int   					= datasets.Features(
				    {'''list''': datasets.Sequence(datasets.Value('''float32'''	)	), '''numbers''': datasets.Value('''float32'''	)}	)
				lowerCAmelCase__       :      Any   					= generate_example_dataset(
				    os.path.join(_a     ,       '''dataset.arrow'''	)     ,       _a     ,       num_examples=_a     ,       seq_shapes={'''list''': (100,)}     ,       )
				print('''first set of iterations'''	)
				for func, kwargs in functions:
						print(func.__name__     ,       str(_a	)	)
						lowerCAmelCase__       :      Any   					= func(_a     ,       **_a	)

				print('''shuffling dataset'''	)
				lowerCAmelCase__       :      Union[str, Any]   					= dataset.shuffle()
				print('''Second set of iterations (after shuffling'''	)
				for func, kwargs in functions_shuffled:
						print('''shuffled '''     ,       func.__name__     ,       str(_a	)	)
						lowerCAmelCase__       :      str   					= func(
						    _a     ,       **_a	)

		with open(_a     ,       '''wb'''	) as f:
				f.write(json.dumps(_a	).encode('''utf-8'''	)	)


if __name__ == "__main__":  # useful to run the profiler
		benchmark_iterating()








from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= image.shape[0], image.shape[1]
		# dst image height and width
		lowerCAmelCase__       :      Any   					= height - k_size + 1
		lowerCAmelCase__       :      Tuple   					= width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		lowerCAmelCase__       :      int   					= zeros((dst_height * dst_width, k_size * k_size)	)
		lowerCAmelCase__       :      List[str]   					= 0
		for i, j in product(range(_a	)     ,       range(_a	)	):
				lowerCAmelCase__       :      Union[str, Any]   					= ravel(image[i : i + k_size, j : j + k_size]	)
				lowerCAmelCase__       :      List[Any]   					= window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


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

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
		imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
		waitKey()




