
"""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    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Any				)  ->    Any:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = k_size // 2
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = 1 / (2 * pi * sigma) * exp(-(square(lowerCamelCase__				) + square(lowerCamelCase__				)) / (2 * square(lowerCamelCase__				))				)
  return g





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Any,						lowerCamelCase__							:							int				)  ->    Dict:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = image.shape[0], image.shape[1]
  # dst image height and width
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = height - k_size + 1
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = width - k_size + 1

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  _SCREAMING_SNAKE_CASE   :				int							    = zeros((dst_height * dst_width, k_size * k_size)				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = 0
  for i, j in product(range(lowerCamelCase__				),						range(lowerCamelCase__				)				):
    _SCREAMING_SNAKE_CASE   :				int							    = ravel(image[i : i + k_size, j : j + k_size]				)
    _SCREAMING_SNAKE_CASE   :				List[str]							    = window
    row += 1

  #  turn the kernel into shape(k*k, 1)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = gen_gaussian_kernel(lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = ravel(lowerCamelCase__				)

  # reshape and get the dst image
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = dot(lowerCamelCase__,						lowerCamelCase__				).reshape(lowerCamelCase__,						lowerCamelCase__				).astype(lowerCamelCase__				)

  return dst


if __name__ == "__main__":
      # read original image
      lowercase_       :						List[str]     					=	imread(R'''../image_data/lena.jpg''')
      # turn image in gray scale value
      lowercase_       :						int     					=	cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      lowercase_       :						Optional[Any]     					=	gaussian_filter(gray, 3, sigma=1)
      lowercase_       :						Any     					=	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 shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

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

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

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





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





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



