



'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      int     ,      _a :      str     ,      _a :      Tuple  )   ->   str:
    __lowerCamelCase			:			Optional[Any]       			=					params

    __lowerCamelCase			:			Any       			=					np.array(_a  )
    __lowerCamelCase			:			int       			=					np.array([len(_a  ) 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 :      str     ,      _a :      Optional[int]  )   ->   List[Any]:
    return (self.token_ids[index], self.lengths[index])




  def __len__(					self :      int  )   ->   Any:
    return len(self.lengths  )




  def 		_lowercase	(					self :      Tuple  )   ->   List[Any]:
    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 		_lowercase	(					self :      Optional[int]  )   ->   Any:
    __lowerCamelCase			:			List[str]       			=					self.params.max_model_input_size
    __lowerCamelCase			:			List[Any]       			=					self.lengths > max_len
    logger.info(f'Splitting {sum(_a  )} too long sequences.'  )

    def divide_chunks(_a :      Union[str, Any]     ,      _a :      Any  ):
      return [l[i : i + n] for i in range(0     ,      len(_a  )     ,      _a  )]

    __lowerCamelCase			:			Dict       			=					[]
    __lowerCamelCase			:			Tuple       			=					[]
    if self.params.mlm:
      __lowerCamelCase			,__lowerCamelCase			:			Any       			=					self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
    else:
      __lowerCamelCase			,__lowerCamelCase			:			Dict       			=					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:
        __lowerCamelCase			:			Tuple       			=					[]
        for sub_s in divide_chunks(seq_     ,      max_len - 2  ):
          if sub_s[0] != cls_id:
            __lowerCamelCase			:			str       			=					np.insert(_a     ,      0     ,      _a  )
          if sub_s[-1] != sep_id:
            __lowerCamelCase			:			List[str]       			=					np.insert(_a     ,      len(_a  )     ,      _a  )
          assert len(_a  ) <= max_len
          assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
          sub_seqs.append(_a  )

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

    __lowerCamelCase			:			str       			=					np.array(_a  )
    __lowerCamelCase			:			int       			=					np.array(_a  )




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




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




  def 		_lowercase	(					self :      List[str]  )   ->   List[str]:
    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 		_lowercase	(					self :      Union[str, Any]     ,      _a :      int  )   ->   Tuple:
    __lowerCamelCase			:			Optional[Any]       			=					[t[0] for t in batch]
    __lowerCamelCase			:			Dict       			=					[t[1] for t in batch]
    assert len(_a  ) == len(_a  )

    # Max for paddings
    __lowerCamelCase			:			Dict       			=					max(_a  )

    # Pad token ids
    if self.params.mlm:
      __lowerCamelCase			:			int       			=					self.params.special_tok_ids['pad_token']
    else:
      __lowerCamelCase			:			Optional[int]       			=					self.params.special_tok_ids['unk_token']
    __lowerCamelCase			:			int       			=					[list(t.astype(_a  )  ) + [pad_idx] * (max_seq_len_ - len(_a  )) for t in token_ids]
    assert len(tk_  ) == len(_a  )
    assert all(len(_a  ) == max_seq_len_ for t in tk_  )

    __lowerCamelCase			:			Any       			=					torch.tensor(tk_  )  # (bs, max_seq_len_)
    __lowerCamelCase			:			List[Any]       			=					torch.tensor(_a  )  # (bs)
    return tk_t, lg_t











'''simple docstring'''





# Algorithm for the pigeonhole sorting



def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			int       			=					min(_lowerCAmelCase			)  # min() finds the minimum value
  __lowerCamelCase			:			List[Any]       			=					max(_lowerCAmelCase			)  # max() finds the maximum value

  __lowerCamelCase			:			List[Any]       			=					max_val - min_val + 1  # size is difference of max and min values plus one

  # list of pigeonholes of size equal to the variable size
  __lowerCamelCase			:			List[Any]       			=					[0] * size

  # Populate the pigeonholes.
  for x in a:
    assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			), "integers only please"
    holes[x - min_val] += 1

  # Putting the elements back into the array in an order.
  __lowerCamelCase			:			Tuple       			=					0
  for count in range(_lowerCAmelCase			):
    while holes[count] > 0:
      holes[count] -= 1
      __lowerCamelCase			:			List[Any]       			=					count + min_val
      i += 1

def 			a_	(				) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					[8, 3, 2, 7, 4, 6, 8]
  pigeonhole_sort(_lowerCAmelCase			)
  print('Sorted order is:'			,' '.join(_lowerCAmelCase			)			)


if __name__ == "__main__":
   main()






