

"""simple docstring"""




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_a:      List[Any]								=     None


_a:      List[Any]								=     logging.get_logger(__name__)


_a:      Union[str, Any]								=     """▁"""

_a:      int								=     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_a:      Optional[int]								=     {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    },
    """tokenizer_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"""
        )
    },
}

_a:      Union[str, Any]								=     {
    """google/reformer-crime-and-punishment""": 52_4288,
}

class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__      			=       PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__      			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__      			=       ["input_ids", "attention_mask"]
	SCREAMING_SNAKE_CASE__      			=       ReformerTokenizer




	def __init__(						self		:  Optional[int]						,		lowerCAmelCase		:  List[Any]=None						,		lowerCAmelCase		:  str=None						,		lowerCAmelCase		:  Any="</s>"						,		lowerCAmelCase		:  Optional[Any]="<unk>"						,		lowerCAmelCase		:  str=[]						,		**lowerCAmelCase		:  int						,		):


		'''simple docstring'''







		super().__init__(
		    __A						,		tokenizer_file=__A						,		eos_token=__A						,		unk_token=__A						,		additional_special_tokens=__A						,		**__A						,		)

		UpperCAmelCase_    			=     vocab_file
		UpperCAmelCase_    			=     False if not self.vocab_file else True




	def  __A   (						self		:  str						,		lowerCAmelCase		:  Union[str, Any]						,		lowerCAmelCase		:  Optional[Any] = None							):


		'''simple docstring'''







		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
			    "tokenizer."							)

		if not os.path.isdir(__A							):
			logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
			return
		UpperCAmelCase_    			=     os.path.join(
		    __A						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

		if os.path.abspath(self.vocab_file							) != os.path.abspath(__A							):
			copyfile(self.vocab_file						,		__A							)

		return (out_vocab_file,)


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



def  __lowerCAmelCase (  A    ,       A=3      ):
	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:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

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

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