






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


snake_case__   :						str													=   5_0_0_0_0
snake_case__   :						Any													=   5_0_0_0

snake_case__   :						str													=   os.path.split(__file__)
snake_case__   :						Union[str, Any]													=   os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def      _snake_case     (__lowercase     ,   __lowercase):





   for i in range(snake_case__):
      UpperCamelCase_						=  dataset[i]






@get_duration
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):





   for i in range(0     ,   len(snake_case__)     ,   snake_case__):
      UpperCamelCase_						=  dataset[i : i + batch_size]






@get_duration
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):





   with dataset.formatted_as(type=snake_case__):
      for i in range(snake_case__):
         UpperCamelCase_						=  dataset[i]






@get_duration
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):





   with dataset.formatted_as(type=snake_case__):
      for i in range(0     ,   snake_case__     ,   snake_case__):
         UpperCamelCase_						=  dataset[i : i + batch_size]






def      _snake_case     ():
   UpperCamelCase_						=  {'num examples': SPEED_TEST_N_EXAMPLES}
   UpperCamelCase_						=  [
       (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': 1000}),
       (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': 1000}),
   ]

   UpperCamelCase_						=  [
       (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': 1000}),
       (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': 1000}),
   ]
   with tempfile.TemporaryDirectory() as tmp_dir:
      print('generating dataset')
      UpperCamelCase_						=  datasets.Features(
          {'list': datasets.Sequence(datasets.Value('float32')), 'numbers': datasets.Value('float32')})
      UpperCamelCase_						=  generate_example_dataset(
          os.path.join(snake_case__     ,   'dataset.arrow')     ,   snake_case__     ,   num_examples=snake_case__     ,   seq_shapes={'list': (100,)}     ,   )
      print('first set of iterations')
      for func, kwargs in functions:
         print(func.__name__     ,   str(snake_case__))
         UpperCamelCase_						=  func(snake_case__     ,   **snake_case__)

      print('shuffling dataset')
      UpperCamelCase_						=  dataset.shuffle()
      print('Second set of iterations (after shuffling')
      for func, kwargs in functions_shuffled:
         print('shuffled '     ,   func.__name__     ,   str(snake_case__))
         UpperCamelCase_						=  func(
             snake_case__     ,   **snake_case__)

   with open(snake_case__     ,   'wb') as f:
      f.write(json.dumps(snake_case__).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     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





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

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

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   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()
