
'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     float:




   """simple docstring"""
   _validate_point(SCREAMING_SNAKE_CASE_						)
   _validate_point(SCREAMING_SNAKE_CASE_						)
   if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
      raise ValueError("""Both points must be in the same n-dimensional space"""						)

   return float(sum(abs(a - b						) for a, b in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     None:




   """simple docstring"""
   if point:
      if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
         for item in point:
            if not isinstance(SCREAMING_SNAKE_CASE_    ,      (int, float)						):
               _SCREAMING_SNAKE_CASE      =				(
                   """Expected a list of numbers as input, found """
                   F"{type(SCREAMING_SNAKE_CASE_						).__name__}"
               )
               raise TypeError(SCREAMING_SNAKE_CASE_						)
      else:
         _SCREAMING_SNAKE_CASE      =				F"Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE_						).__name__}"
         raise TypeError(SCREAMING_SNAKE_CASE_						)
   else:
      raise ValueError("""Missing an input"""						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     float:




   """simple docstring"""
   _validate_point(SCREAMING_SNAKE_CASE_						)
   _validate_point(SCREAMING_SNAKE_CASE_						)
   if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
      raise ValueError("""Both points must be in the same n-dimensional space"""						)

   return float(sum(abs(x - y						) for x, y in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()








'''simple docstring'''



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

from utils import logger

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				params

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				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       ,							A__      )    ->			Dict:
          return (self.token_ids[index], self.lengths[index])






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






       def        UpperCamelCase				(							self      )    ->			Dict:
          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        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.params.max_model_input_size
          _SCREAMING_SNAKE_CASE      =				self.lengths > max_len
          logger.info(F"Splitting {sum(A__      )} too long sequences."      )

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

          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				[]
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				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:
                _SCREAMING_SNAKE_CASE      =				[]
                for sub_s in divide_chunks(seq_       ,							max_len - 2      ):
                   if sub_s[0] != cls_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							0       ,							A__      )
                   if sub_s[-1] != sep_id:
                      _SCREAMING_SNAKE_CASE      =				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]      )

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array(A__      )






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






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






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          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        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in batch]
          _SCREAMING_SNAKE_CASE      =				[t[1] for t in batch]
          assert len(A__      ) == len(A__      )

          # Max for paddings
          _SCREAMING_SNAKE_CASE      =				max(A__      )

          # Pad token ids
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""pad_token"""]
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				[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_      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor(tk_      )  # (bs, max_seq_len_)
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )  # (bs)
          return tk_t, lg_t






