




"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A_						=				imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A_						=				cvtColor(img, COLOR_BGR2GRAY)


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Dict								=  cn.convert_to_negative(snake_case__    )
  # assert negative_img array for at least one True
  assert negative_img.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(snake_case__		,   1_10    )    ).startswith(
        """<PIL.Image.Image image mode=RGB size=100x100 at"""    )


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  canny.gen_gaussian_kernel(9		,   sigma=1.4    )
  # Assert ambiguous array
  assert resp.all()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  imread("""digital_image_processing/image_data/lena_small.jpg"""		,   0    )
  # assert ambiguous array for all == True
  assert canny_img.all()
  _snake_case							:		Optional[Any]								=  canny.canny(snake_case__    )
  # assert canny array for at least one True
  assert canny_array.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  assert gg.gaussian_filter(snake_case__		,   5		,   sigma=0.9    ).all()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
  _snake_case							:		Optional[int]								=  conv.img_convolve(snake_case__		,   snake_case__    ).astype(snake_case__    )
  assert res.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  assert med.median_filter(snake_case__		,   3    ).any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case		,					_snake_case							:		int								=  sob.sobel_filter(snake_case__    )
  assert grad.any() and theta.any()


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		str								=  sp.make_sepia(snake_case__		,   20    )
  assert sepia.all()


def 					UpperCAmelCase__       (snake_case__							:					str = "digital_image_processing/image_data/lena_small.jpg"    ):
  """simple docstring"""

  _snake_case							:		Any								=  bs.Burkes(imread(snake_case__		,   1    )		,   1_20    )
  burkes.process()
  assert burkes.output_img.any()


def 					UpperCAmelCase__       (snake_case__							:					str = "digital_image_processing/image_data/lena_small.jpg"		,   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  rs.NearestNeighbour(imread(snake_case__		,   1    )		,   4_00		,   2_00    )
  nn.process()
  assert nn.output.any()






def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  """digital_image_processing/image_data/lena.jpg"""

  # Reading the image and converting it to grayscale.
  _snake_case							:		List[Any]								=  imread(snake_case__		,   0    )

  # Test for get_neighbors_pixel function() return not None
  _snake_case							:		str								=  0
  _snake_case							:		Union[str, Any]								=  0
  _snake_case							:		Optional[int]								=  image[x_coordinate][y_coordinate]

  _snake_case							:		str								=  lbp.get_neighbors_pixel(
      snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  _snake_case							:		Tuple								=  np.zeros((image.shape[0], image.shape[1])    )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,   image.shape[0]    ):
    for j in range(0		,   image.shape[1]    ):
      _snake_case							:		Optional[int]								=  lbp.local_binary_value(snake_case__		,   snake_case__		,   snake_case__    )

  assert lbp_image.any()









import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

