"""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 	a_    (							_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Union[str, Any]         =   k_size // 2
						_UpperCamelCase      : int         =   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
						_UpperCamelCase      : List[Any]         =   1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase       ) + square(__lowerCAmelCase       )) / (2 * square(__lowerCAmelCase       ))       )
						return g





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Any         =   image.shape[0], image.shape[1]
						# dst image height and width
						_UpperCamelCase      : str         =   height - k_size + 1
						_UpperCamelCase      : int         =   width - k_size + 1

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

						#  turn the kernel into shape(k*k, 1)
						_UpperCamelCase      : Tuple         =   gen_gaussian_kernel(__lowerCAmelCase	,						__lowerCAmelCase       )
						_UpperCamelCase      : Tuple         =   ravel(__lowerCAmelCase       )

						# reshape and get the dst image
						_UpperCamelCase      : Tuple         =   dot(__lowerCAmelCase	,						__lowerCAmelCase       ).reshape(__lowerCAmelCase	,						__lowerCAmelCase       ).astype(__lowerCAmelCase       )

						return dst


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

						# get values with two different mask size
						UpperCamelCase_									=gaussian_filter(gray, 3, sigma=1)
						UpperCamelCase_									=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 argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0X4E00 and cp <= 0X9FFF)
						    or (cp >= 0X3400 and cp <= 0X4DBF)  #
						    or (cp >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False





def 	a_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

						_UpperCamelCase      : int         =   bert_tokens
						_UpperCamelCase       ,							_UpperCamelCase      : Union[str, Any]         =   0, len(_lowercase       )
						while start < end:
												_UpperCamelCase      : Union[str, Any]         =   True
												if is_chinese(bert_word[start]       ):
																		_UpperCamelCase      : List[Any]         =   min(end - start	,						_lowercase       )
																		for i in range(_lowercase	,						1	,						-1       ):
																								_UpperCamelCase      : str         =   ''''''.join(bert_word[start : start + i]       )
																								if whole_word in chinese_word_set:
																														for j in range(start + 1	,						start + i       ):
																																				_UpperCamelCase      : int         =   '''##''' + bert_word[j]
																														_UpperCamelCase      : int         =   start + i
																														_UpperCamelCase      : Union[str, Any]         =   False
																														break
												if single_word:
																		start += 1
						return bert_word





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [get_chinese_word(_lowercase       ) for r in res]
												ltp_res.extend(_lowercase       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Dict         =   []
						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   bert_tokenizer(lines[i : i + 100]	,						add_special_tokens=_lowercase	,						truncation=_lowercase	,						max_length=512       )
												bert_res.extend(res['''input_ids''']       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Optional[Any]         =   []
						for input_ids, chinese_word in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : str         =   []
												for id in input_ids:
																		_UpperCamelCase      : Dict         =   bert_tokenizer._convert_id_to_token(_lowercase       )
																		input_tokens.append(_lowercase       )
												_UpperCamelCase      : str         =   add_sub_symbol(_lowercase	,						_lowercase       )
												_UpperCamelCase      : List[str]         =   []
												# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
												for i, token in enumerate(_lowercase       ):
																		if token[:2] == "##":
																								_UpperCamelCase      : int         =   token[2:]
																								# save chinese tokens' pos
																								if len(_lowercase       ) == 1 and _is_chinese_char(ord(_lowercase       )       ):
																														ref_id.append(_lowercase       )
												ref_ids.append(_lowercase       )

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
						# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
						with open(args.file_name	,						'''r'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

						with open(args.save_path	,						'''w'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : List[Any]         =   [json.dumps(_lowercase       ) + '''\n''' for ref in ref_ids]
												f.writelines(_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser(description="""prepare_chinese_ref""")
						parser.add_argument(
						    """--file_name""",
						    required=False,
						    type=str,
						    default="""./resources/chinese-demo.txt""",
						    help="""file need process, same as training data in lm""",
						)
						parser.add_argument(
						    """--ltp""",
						    required=False,
						    type=str,
						    default="""./resources/ltp""",
						    help="""resources for LTP tokenizer, usually a path""",
						)
						parser.add_argument(
						    """--bert""",
						    required=False,
						    type=str,
						    default="""./resources/robert""",
						    help="""resources for Bert tokenizer""",
						)
						parser.add_argument(
						    """--save_path""",
						    required=False,
						    type=str,
						    default="""./resources/ref.txt""",
						    help="""path to save res""",
						)

						UpperCamelCase_									=parser.parse_args()
						main(args)



