




'''simple docstring'''

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros




def    snake_case__     (  a       ,      a	)			->       Optional[Any]:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						image.shape
			snake_case__          =						cols - block_size[1] + 1
			snake_case__          =						rows - block_size[0] + 1
			snake_case__          =						zeros((dst_height * dst_width, block_size[1] * block_size[0])	)
			snake_case__          =						0
			for i in range(0       ,      a	):
						for j in range(0       ,      a	):
									snake_case__          =						ravel(image[i : i + block_size[0], j : j + block_size[1]]	)
									snake_case__          =						window
									row += 1

			return image_array




def    snake_case__     (  a       ,      a	)			->       Any:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						image.shape[0], image.shape[1]
			snake_case__          =						filter_kernel.shape[0]
			snake_case__          =						k_size // 2
			# Pads image with the edge values of array.
			snake_case__          =						pad(a       ,      a       ,      mode="""edge"""	)

			# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
			snake_case__          =						imacol(a       ,      (k_size, k_size)	)

			#  turn the kernel into shape(k*k, 1)
			snake_case__          =						ravel(a	)
			# reshape and get the dst image
			snake_case__          =						dot(a       ,      a	).reshape(a       ,      a	)
			return dst


if __name__ == "__main__":
		# read original image
		a__         =     imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		a__         =     cvtColor(img, COLOR_BGR2GRAY)
		# Laplace operator
		a__         =     array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
		a__         =     img_convolve(gray, Laplace_kernel).astype(uinta)
		imshow('''Laplacian''', out)
		waitKey(0)






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase				    =      (30, 487)
__lowercase				    =      (36, 1_024)
__lowercase				    =      (100, 100)
__lowercase				    =      100

__lowercase				    =      datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

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


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Optional[Any]  ,				__lowercase:		List[Any]):



  '''simple docstring'''






  with ArrowWriter(features=__lowercase  ,				path=os.path.join(__lowercase  ,				'''beta.arrow''')) as writer:
    for key, record in dummy_data:
      __lowerCAmelCase								=					my_features.encode_example(__lowercase)
      writer.write(__lowercase)
    __lowerCAmelCase						,    __lowerCAmelCase								=					writer.finalize()


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					{}
  __lowerCAmelCase								=					(
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase								=					datasets.Features({'''image''': ArrayaD(__lowercase  ,				dtype='''float32''')})
    __lowerCAmelCase								=					generate_examples(features=__lowercase  ,				num_examples=__lowercase)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''')))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': SPEED_TEST_SHAPE})
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,				)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

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


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