


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      params

  __A	:		Dict	  =      np.array(__lowercase    )
  __A	:		Any	  =      np.array([len(__lowercase    ) for t in data]    )

  self.check()
  self.remove_long_sequences()
  self.remove_empty_sequences()
  self.remove_unknown_sequences()
  self.check()
  self.print_statistics()


 def __getitem__(   self   ,   __lowercase    ):

  """simple docstring"""







  return (self.token_ids[index], self.lengths[index])


 def __len__(   self    ):

  """simple docstring"""







  return len(self.lengths    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  assert len(self.token_ids    ) == len(self.lengths    )
  assert all(self.lengths[i] == len(self.token_ids[i]    ) for i in range(len(self.lengths    )    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.params.max_model_input_size
  __A	:		Optional[int]	  =      self.lengths > max_len
  logger.info(F"""Splitting {sum(__lowercase    )} too long sequences."""    )

  def divide_chunks(__lowercase   ,   __lowercase    ):
   return [l[i : i + n] for i in range(0   ,   len(__lowercase    )   ,   __lowercase    )]

  __A	:		int	  =      []
  __A	:		int	  =      []
  if self.params.mlm:
   __A  ,__A	:		List[str]	  =      self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
  else:
   __A  ,__A	:		Optional[Any]	  =      self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

  for seq_, len_ in zip(self.token_ids   ,   self.lengths    ):
   assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
   if len_ <= max_len:
    new_tok_ids.append(seq_    )
    new_lengths.append(len_    )
   else:
    __A	:		int	  =      []
    for sub_s in divide_chunks(seq_   ,   max_len - 2    ):
     if sub_s[0] != cls_id:
      __A	:		Optional[int]	  =      np.insert(__lowercase   ,   0   ,   __lowercase    )
     if sub_s[-1] != sep_id:
      __A	:		List[str]	  =      np.insert(__lowercase   ,   len(__lowercase    )   ,   __lowercase    )
     assert len(__lowercase    ) <= max_len
     assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
     sub_seqs.append(__lowercase    )

    new_tok_ids.extend(__lowercase    )
    new_lengths.extend([len(__lowercase    ) for l in sub_seqs]    )

  __A	:		str	  =      np.array(__lowercase    )
  __A	:		Dict	  =      np.array(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      len(self    )
  __A	:		Optional[Any]	  =      self.lengths > 11
  __A	:		Any	  =      self.token_ids[indices]
  __A	:		int	  =      self.lengths[indices]
  __A	:		Any	  =      len(self    )
  logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  if "unk_token" not in self.params.special_tok_ids:
   return
  else:
   __A	:		Tuple	  =      self.params.special_tok_ids['unk_token']
  __A	:		Any	  =      len(self    )
  __A	:		Optional[Any]	  =      np.array([np.count_nonzero(a == unk_token_id    ) for a in self.token_ids]    )
  __A	:		List[Any]	  =      (unk_occs / self.lengths) < 0.5
  __A	:		Any	  =      self.token_ids[indices]
  __A	:		int	  =      self.lengths[indices]
  __A	:		int	  =      len(self    )
  logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  if not self.params.is_master:
   return
  logger.info(F"""{len(self    )} sequences"""    )
  # data_len = sum(self.lengths)
  # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
  # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

  # unk_idx = self.params.special_tok_ids['unk_token']
  # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
  # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      [t[0] for t in batch]
  __A	:		int	  =      [t[1] for t in batch]
  assert len(__lowercase    ) == len(__lowercase    )

  # Max for paddings
  __A	:		str	  =      max(__lowercase    )

  # Pad token ids
  if self.params.mlm:
   __A	:		Union[str, Any]	  =      self.params.special_tok_ids['pad_token']
  else:
   __A	:		int	  =      self.params.special_tok_ids['unk_token']
  __A	:		Tuple	  =      [list(t.astype(__lowercase    )    ) + [pad_idx] * (max_seq_len_ - len(__lowercase    )) for t in token_ids]
  assert len(tk_    ) == len(__lowercase    )
  assert all(len(__lowercase    ) == max_seq_len_ for t in tk_    )

  __A	:		str	  =      torch.tensor(tk_    )  # (bs, max_seq_len_)
  __A	:		Dict	  =      torch.tensor(__lowercase    )  # (bs)
  return tk_t, lg_t










'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






