

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

from utils import logger


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       params

      _lowercase      :				Any      					=       np.array(_UpperCamelCase  )
      _lowercase      :				Dict      					=       np.array([len(_UpperCamelCase  ) 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      ,    _UpperCamelCase  ):





      """simple docstring"""





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


   def __len__(			self  ):





      """simple docstring"""





      return len(self.lengths  )


   def 						_lowerCamelCase       (			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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.params.max_model_input_size
      _lowercase      :				int      					=       self.lengths > max_len
      logger.info(f'''Splitting {sum(_UpperCamelCase  )} too long sequences.'''  )

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

      _lowercase      :				Any      					=       []
      _lowercase      :				Dict      					=       []
      if self.params.mlm:
         _lowercase      :				Any      					=       self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
      else:
         _lowercase      :				int      					=       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:
            _lowercase      :				Dict      					=       []
            for sub_s in divide_chunks(seq_      ,    max_len - 2  ):
               if sub_s[0] != cls_id:
                  _lowercase      :				Optional[int]      					=       np.insert(_UpperCamelCase      ,    0      ,    _UpperCamelCase  )
               if sub_s[-1] != sep_id:
                  _lowercase      :				Any      					=       np.insert(_UpperCamelCase      ,    len(_UpperCamelCase  )      ,    _UpperCamelCase  )
               assert len(_UpperCamelCase  ) <= max_len
               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
               sub_seqs.append(_UpperCamelCase  )

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

      _lowercase      :				int      					=       np.array(_UpperCamelCase  )
      _lowercase      :				Any      					=       np.array(_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			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 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       [t[0] for t in batch]
      _lowercase      :				List[str]      					=       [t[1] for t in batch]
      assert len(_UpperCamelCase  ) == len(_UpperCamelCase  )

      # Max for paddings
      _lowercase      :				Union[str, Any]      					=       max(_UpperCamelCase  )

      # Pad token ids
      if self.params.mlm:
         _lowercase      :				Dict      					=       self.params.special_tok_ids["pad_token"]
      else:
         _lowercase      :				Any      					=       self.params.special_tok_ids["unk_token"]
      _lowercase      :				List[str]      					=       [list(t.astype(_UpperCamelCase  )  ) + [pad_idx] * (max_seq_len_ - len(_UpperCamelCase  )) for t in token_ids]
      assert len(tk_  ) == len(_UpperCamelCase  )
      assert all(len(_UpperCamelCase  ) == max_seq_len_ for t in tk_  )

      _lowercase      :				int      					=       torch.tensor(tk_  )  # (bs, max_seq_len_)
      _lowercase      :				Optional[Any]      					=       torch.tensor(_UpperCamelCase  )  # (bs)
      return tk_t, lg_t









'''simple docstring'''
from __future__ import annotations







def  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     None:
   if start is None:
      _lowercase      :				Dict      					=       0

   if end is None:
      _lowercase      :				List[Any]      					=       len(snake_case					) - 1

   if start >= end:
      return

   _lowercase      :				int      					=       (start + end) // 2

   slowsort(snake_case		,							snake_case		,							snake_case					)
   slowsort(snake_case		,							mid + 1		,							snake_case					)

   if sequence[end] < sequence[mid]:
      _lowercase					,				_lowercase      :				Any      					=       sequence[mid], sequence[end]

   slowsort(snake_case		,							snake_case		,							end - 1					)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






