






"""simple docstring"""





import os
from collections import deque

import torch
from torch.utils.data import Dataset






class      a						(					__snake_case		):






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=""       ,						__SCREAMING_SNAKE_CASE     :							Dict="train"					)     ->		Dict:
      assert os.path.isdir(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						[]
      lowerCamelCase_         =						os.listdir(__SCREAMING_SNAKE_CASE					)
      for story_filename in story_filenames_list:
        if "summary" in story_filename:
          continue
        lowerCamelCase_         =						os.path.join(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        if not os.path.isfile(__SCREAMING_SNAKE_CASE					):
          continue
        self.documents.append(__SCREAMING_SNAKE_CASE					)






    def __len__(       self     :							Any					)     ->		Dict:
      return len(self.documents					)






    def __getitem__(       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		Tuple:
      lowerCamelCase_         =						self.documents[idx]
      lowerCamelCase_         =						document_path.split('/'					)[-1]
      with open(__SCREAMING_SNAKE_CASE       ,						encoding='utf-8'					) as source:
        lowerCamelCase_         =						source.read()
        lowerCamelCase_     ,	lowerCamelCase_         =						process_story(__SCREAMING_SNAKE_CASE					)
      return document_name, story_lines, summary_lines







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any] )      ->				List[str]:
  lowerCamelCase_         =						list(filter(lambda _lowerCamelCase : len(_lowerCamelCase ) != 0		,				[line.strip() for line in raw_story.split('\n' )] ) )

  # for some unknown reason some lines miss a period, add it
  lowerCamelCase_         =						[_add_missing_period(_lowerCamelCase ) for line in nonempty_lines]

  # gather article lines
  lowerCamelCase_         =						[]
  lowerCamelCase_         =						deque(_lowerCamelCase )
  while True:
    try:
      lowerCamelCase_         =						lines.popleft()
      if element.startswith('@highlight' ):
        break
      story_lines.append(_lowerCamelCase )
    except IndexError:
      # if "@highlight" is absent from the file we pop
      # all elements until there is None, raising an exception.
      return story_lines, []

    # gather summary lines
  lowerCamelCase_         =						list(filter(lambda _lowerCamelCase : not t.startswith('@highlight' )		,				_lowerCamelCase ) )

  return story_lines, summary_lines







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any] )      ->				Optional[int]:
  lowerCamelCase_         =						['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
  if line.startswith('@highlight' ):
    return line
  if line[-1] in END_TOKENS:
    return line
  return line + "."







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		int )      ->				Optional[Any]:
  if len(_lowerCamelCase ) > block_size:
    return sequence[:block_size]
  else:
    sequence.extend([pad_token_id] * (block_size - len(_lowerCamelCase )) )
    return sequence







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Union[str, Any] )      ->				List[Any]:
  lowerCamelCase_         =						torch.ones_like(_lowerCamelCase )
  lowerCamelCase_         =						sequence == pad_token_id
  lowerCamelCase_         =						0
  return mask







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[int]		,				_lowerCamelCase       :		int		,				_lowerCamelCase       :		Optional[Any] )      ->				Tuple:
  lowerCamelCase_         =						[tokenizer.encode(_lowerCamelCase ) for line in story_lines]
  lowerCamelCase_         =						[token for sentence in story_lines_token_ids for token in sentence]
  lowerCamelCase_         =						[tokenizer.encode(_lowerCamelCase ) for line in summary_lines]
  lowerCamelCase_         =						[token for sentence in summary_lines_token_ids for token in sentence]

  return story_token_ids, summary_token_ids







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		int )      ->				Tuple:
  lowerCamelCase_         =						[]
  for sequence in batch:
    lowerCamelCase_         =						-1
    lowerCamelCase_         =						[]
    for s in sequence:
      if s == separator_token_id:
        sentence_num += 1
      embeddings.append(sentence_num % 2 )
    batch_embeddings.append(_lowerCamelCase )
  return torch.tensor(_lowerCamelCase )












"""simple docstring"""





from cva import destroyAllWindows, imread, imshow, waitKey







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple )      ->				Dict:
  # getting number of pixels in the image
  lowerCamelCase_     ,	lowerCamelCase_         =						img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_lowerCamelCase ):
    for j in range(_lowerCamelCase ):
      lowerCamelCase_         =						[255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						imread('''image_data/lena.jpg''', 1)

  # convert to its negative
  _SCREAMING_SNAKE_CASE					:				List[Any]					    =						convert_to_negative(img)

  # show result image
  imshow('''negative of original image''', img)
  waitKey(0)
  destroyAllWindows()




