






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__A  							=  10_00_00_00_00_00
__A  							=  1_00_00

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







def 			lowercase__  (				A_     ,			A_		)  ->							datasets.Dataset:







       """simple docstring"""






       __UpperCAmelCase    							=pa.Table.from_pydict({"""col""": [0] * chunk_size}		)
       __UpperCAmelCase    							=pa.concat_tables([table] * (num_examples // chunk_size)		)
       return datasets.Dataset(SCREAMING_SNAKE_CASE__     ,			fingerprint="""table_100B"""		)



@dataclass
class 							_A :
    """simple docstring"""

    lowerCamelCase  :       str   =   42
    lowerCamelCase  :       List[Any]   =   42
    lowerCamelCase  :       List[Any]   =   42
    lowerCamelCase  :       str   =   42



    def _a       (							self			:    str			)			->       List[Any]:
           __UpperCAmelCase    							=np.random.default_rng(self.seed			)
           __UpperCAmelCase    							=rng.integers(low=self.low   ,    high=self.high   ,    size=self.size			).tolist()



    def __iter__(							self			:    str			)			->       Union[str, Any]:
           return iter(self._sampled_values			)



    def __len__(							self			:    Union[str, Any]			)			->       List[Any]:
           return self.size







@get_duration
def 			lowercase__  (				A_		)  ->							List[Any]:







       """simple docstring"""






       __UpperCAmelCase    							=dataset[0]







@get_duration
def 			lowercase__  (				A_		)  ->							List[str]:







       """simple docstring"""






       __UpperCAmelCase    							=dataset[-1]







@get_duration
def 			lowercase__  (				A_		)  ->							Tuple:







       """simple docstring"""






       __UpperCAmelCase    							=dataset[range(len(SCREAMING_SNAKE_CASE__		) // 2     ,			len(SCREAMING_SNAKE_CASE__		) // 2 + 1024		)]







@get_duration
def 			lowercase__  (				A_		)  ->							Tuple:







       """simple docstring"""






       __UpperCAmelCase    							=dataset[RandIter(0     ,			len(SCREAMING_SNAKE_CASE__		)     ,			1024     ,			seed=42		)]







def 			lowercase__  (				)  ->							Dict:







       """simple docstring"""






       __UpperCAmelCase    							={'''num examples''': SPEED_TEST_N_EXAMPLES}
       __UpperCAmelCase    							=(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
       print("""generating dataset"""		)
       __UpperCAmelCase    							=generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE__     ,			chunk_size=SCREAMING_SNAKE_CASE__		)
       print("""Functions"""		)
       for func in functions:
              print(func.__name__		)
              __UpperCAmelCase    							=func(SCREAMING_SNAKE_CASE__		)

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


if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like







def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:







       """simple docstring"""






       __UpperCAmelCase    							=int(mask / 2		)
       # copy image size
       __UpperCAmelCase    							=zeros_like(A_		)
       for i in range(A_     ,			gray_img.shape[0] - bd		):
              for j in range(A_     ,			gray_img.shape[1] - bd		):
                     # get mask according with mask
                     __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                     # calculate mask median
                     __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                     __UpperCAmelCase    							=median
       return median_img


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

   # get values with two different mask size
   __A  							=  median_filter(gray, 3)
   __A  							=  median_filter(gray, 5)

   # show result images
   imshow("median filter with 3x3 mask", medianaxa)
   imshow("median filter with 5x5 mask", medianaxa)
   waitKey(0)




