


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 __UpperCAmelCase       (		__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     k_size // 2
       UpperCAmelCase__						,			UpperCAmelCase__										=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase__										=     1 / (2 * pi * sigma) * exp(-(square(__A			) + square(__A			)) / (2 * square(__A			))			)
       return g





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					int:



       '''simple docstring'''





       UpperCAmelCase__						,			UpperCAmelCase__										=     image.shape[0], image.shape[1]
       # dst image height and width
       UpperCAmelCase__										=     height - k_size + 1
       UpperCAmelCase__										=     width - k_size + 1

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

       #  turn the kernel into shape(k*k, 1)
       UpperCAmelCase__										=     gen_gaussian_kernel(__A ,						__A			)
       UpperCAmelCase__										=     ravel(__A			)

       # reshape and get the dst image
       UpperCAmelCase__										=     dot(__A ,						__A			).reshape(__A ,						__A			).astype(__A			)

       return dst


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

    # get values with two different mask size
    A      = gaussian_filter(gray, 3, sigma=1)
    A      = 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()









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





