



"""simple docstring"""


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        lowercase		(					_SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  int ):






				'''simple docstring'''



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







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Any    ,   _SCREAMING_SNAKE_CASE      :  Tuple    ,   _SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



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

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

				#  turn the kernel into shape(k*k, 1)
				_UpperCAmelCase							=			gen_gaussian_kernel(_SCREAMING_SNAKE_CASE    ,   _SCREAMING_SNAKE_CASE )
				_UpperCAmelCase							=			ravel(_SCREAMING_SNAKE_CASE )

				# reshape and get the dst image
				_UpperCAmelCase							=			dot(_SCREAMING_SNAKE_CASE    ,   _SCREAMING_SNAKE_CASE ).reshape(_SCREAMING_SNAKE_CASE    ,   _SCREAMING_SNAKE_CASE ).astype(_SCREAMING_SNAKE_CASE )

				return dst


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

					# get values with two different mask size
					__A      :							Dict   =      gaussian_filter(gray, 3, sigma=1)
					__A      :							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()







'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





