





'''simple docstring'''






import math
import sys

import cva
import numpy as np






def      __A						(							a_						:  np.ndarray      ,a_						:  float	):
		# For applying gaussian function for each element in matrix.
		lowerCAmelCase     :    List[str]						=   math.sqrt(a_	)
		lowerCAmelCase     :    Dict						=   1 / (sigma * math.sqrt(2 * math.pi	))
		return cons * np.exp(-((img / sigma) ** 2) * 0.5	)






def      __A						(							a_						:  np.ndarray      ,a_						:  int      ,a_						:  int      ,a_						:  int	):
		lowerCAmelCase     :    Tuple						=   kernel_size // 2
		return img[x - half : x + half + 1, y - half : y + half + 1]






def      __A						(							a_						:  int      ,a_						:  float	):
		# Creates a gaussian kernel of given dimension.
		lowerCAmelCase     :    List[str]						=   np.zeros((kernel_size, kernel_size)	)
		for i in range(0      ,a_	):
				for j in range(0      ,a_	):
						lowerCAmelCase     :    Optional[int]						=   math.sqrt(
						    abs(i - kernel_size // 2	) ** 2 + abs(j - kernel_size // 2	) ** 2	)
		return vec_gaussian(a_      ,a_	)






def      __A						(							a_						:  np.ndarray      ,a_						:  float      ,a_						:  float      ,a_						:  int      ,):
		lowerCAmelCase     :    List[str]						=   np.zeros(img.shape	)
		lowerCAmelCase     :    Dict						=   get_gauss_kernel(a_      ,a_	)
		lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   img.shape
		for i in range(kernel_size // 2      ,size_x - kernel_size // 2	):
				for j in range(kernel_size // 2      ,size_y - kernel_size // 2	):
						lowerCAmelCase     :    Tuple						=   get_slice(a_      ,a_      ,a_      ,a_	)
						lowerCAmelCase     :    int						=   img_s - img_s[kernel_size // 2, kernel_size // 2]
						lowerCAmelCase     :    Optional[Any]						=   vec_gaussian(a_      ,a_	)
						lowerCAmelCase     :    int						=   np.multiply(a_      ,a_	)
						lowerCAmelCase     :    int						=   np.multiply(a_      ,a_	)
						lowerCAmelCase     :    List[Any]						=   np.sum(a_	) / np.sum(a_	)
						lowerCAmelCase     :    Optional[Any]						=   val
		return imga






def      __A						(							a_						:  list	):
		lowerCAmelCase     :    Optional[Any]						=   args[1] if args[1:] else "../image_data/lena.jpg"
		lowerCAmelCase     :    Tuple						=   float(args[2]	) if args[2:] else 1.0
		lowerCAmelCase     :    Optional[int]						=   float(args[3]	) if args[3:] else 1.0
		if args[4:]:
				lowerCAmelCase     :    Union[str, Any]						=   int(args[4]	)
				lowerCAmelCase     :    Optional[Any]						=   kernel_size + abs(kernel_size % 2 - 1	)
		else:
				lowerCAmelCase     :    Any						=   5
		return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
					lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase       		=      parse_args(sys.argv)
					lowerCAmelCase       		=      cva.imread(filename, 0)
					cva.imshow("""input image""", img)

					lowerCAmelCase       		=      img / 2_55
					lowerCAmelCase       		=      out.astype("""float32""")
					lowerCAmelCase       		=      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
					lowerCAmelCase       		=      out * 2_55
					lowerCAmelCase       		=      np.uinta(out)
					cva.imshow("""output image""", out)
					cva.waitKey(0)
					cva.destroyAllWindows()










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



