

"""simple docstring"""





import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


__UpperCamelCase						:   Union[str, Any]							   =	logging.getLogger(__name__)

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			argparse.ArgumentParser(description='''Train a unigram tokenizer on the wikitext dataset.'''				)
	parser.add_argument(
	    '''--dataset_name'''		,  type=A_		,  default='''wikitext'''		,  help='''Name of the training. Explore datasets at: hf.co/datasets.'''		,  )
	parser.add_argument(
	    '''--dataset_config'''		,  type=A_		,  default='''wikitext-103-raw-v1'''		,  help='''Configuration name of the dataset.'''				)
	parser.add_argument(
	    '''--batch_size'''		,  type=A_		,  default=10_00		,  help='''Batch size during training.'''		,  )
	parser.add_argument(
	    '''--vocab_size'''		,  type=A_		,  default=1_00_48		,  help='''Size of the desired vocabulary.'''		,  )
	parser.add_argument(
	    '''--limit'''		,  default=A_		,  type=A_		,  help='''Limit the number of shards (used for debugging).'''		,  )
	parser.add_argument(
	    '''--export_to_hub'''		,  action='''store_true'''		,  )

	lowerCAmelCase__			:		Optional[int]						       =			parser.parse_args()
	return args

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		Optional[int]						       =			datasets.load_dataset(args.dataset_name		,  args.dataset_config		,  split='''train'''				)

	if args.limit is not None:
		lowerCAmelCase__			:		Optional[int]						       =			min(len(A_				)		,  args.limit				)
		lowerCAmelCase__			:		Tuple						       =			dataset.select(range(A_				)				)
		logger.info(f'Limiting the dataset to {args.limit} entries.'				)

	def batch_iterator():
		for i in range(0		,  len(A_				)		,  args.batch_size				):
			yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
	lowerCAmelCase__			:		Optional[int]						       =			Tokenizer(Unigram()				)
	lowerCAmelCase__			:		Any						       =			normalizers.Sequence([normalizers.Replace('''``'''		,  '''"'''				), normalizers.Replace('''\'\''''		,  '''"'''				)]				)
	lowerCAmelCase__			:		List[str]						       =			pre_tokenizers.Metaspace()

	# Prepare the trainer.
	lowerCAmelCase__			:		Any						       =			UnigramTrainer(
	    unk_token='''<unk>'''		,  special_tokens=['''[CLS]''', '''[SEP]''', '''<unk>''', '''<pad>''', '''[MASK]''']		,  vocab_size=args.vocab_size		,  )

	logger.info('''Training the tokenizer.'''				)
	tokenizer.train_from_iterator(batch_iterator()		,  trainer=A_				)
	logger.info('''Tokenizer training complete!'''				)

	lowerCAmelCase__			:		Dict						       =			tokenizer.token_to_id('''[CLS]'''				)
	lowerCAmelCase__			:		Dict						       =			tokenizer.token_to_id('''[SEP]'''				)
	lowerCAmelCase__			:		Optional[Any]						       =			processors.TemplateProcessing(
	    single='''[CLS]:0 $A:0 [SEP]:0'''		,  pair='''[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'''		,  special_tokens=[
	        ('''[CLS]''', cls_token_id),
	        ('''[SEP]''', sep_token_id),
	    ]		,  )
	lowerCAmelCase__			:		Tuple						       =			decoders.Metaspace()

	if args.export_to_hub:
		logger.info('''Exporting the trained tokenzier to Hub.'''				)
		lowerCAmelCase__			:		Union[str, Any]						       =			AlbertTokenizerFast(tokenizer_object=A_				)
		new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''				)


if __name__ == "__main__":
							__UpperCamelCase						:   List[str]							   =	parse_args()
							main(args)







"""simple docstring"""





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

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_=3				):
	lowerCAmelCase__			:		Union[str, Any]						       =			int(mask / 2				)
	# copy image size
	lowerCAmelCase__			:		Dict						       =			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
			lowerCAmelCase__			:		str						       =			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]				)
			# calculate mask median
			lowerCAmelCase__			:		str						       =			sort(A_				)[inta(divide((multiply(A_		,  A_				))		,  2				) + 1				)]
			lowerCAmelCase__			:		List[str]						       =			median
	return median_img


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

							# get values with two different mask size
							__UpperCamelCase						:   int							   =	median_filter(gray, 3)
							__UpperCamelCase						:   Tuple							   =	median_filter(gray, 5)

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




