



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like
def 						snake_case__							(       __lowercase				,  __lowercase=3							)		->		List[str]:







						"""simple docstring"""

						A__						:				List[str]         =			int(mask / 2							)
						# copy image size
						A__						:				List[Any]         =			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
																		A__						:				List[Any]         =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]							)
																		# calculate mask median
																		A__						:				str         =			sort(A_							)[inta(divide((multiply(A_				,  A_							))				,  2							) + 1							)]
																		A__						:				Optional[Any]         =			median
						return median_img


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

		# get values with two different mask size
		snake_case     :    str						=    median_filter(gray, 3)
		snake_case     :    Optional[Any]						=    median_filter(gray, 5)

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



from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

