


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

   '''simple docstring'''






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


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

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

