






"""simple docstring"""





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

from utils import logger
class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    params

       UpperCamelCase__         =    np.array(snake_case_      )
       UpperCamelCase__         =    np.array([len(snake_case_      ) 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 ,				snake_case_      )		->       Tuple:
       return (self.token_ids[index], self.lengths[index])

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    self.params.max_model_input_size
       UpperCamelCase__         =    self.lengths > max_len
       logger.info(F'Splitting {sum(snake_case_      )} too long sequences.'      )

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

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

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

       UpperCamelCase__         =    np.array(snake_case_      )
       UpperCamelCase__         =    np.array(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    len(self      )
       UpperCamelCase__         =    self.lengths > 11
       UpperCamelCase__         =    self.token_ids[indices]
       UpperCamelCase__         =    self.lengths[indices]
       UpperCamelCase__         =    len(self      )
       logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.'      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       str:
       UpperCamelCase__         =    [t[0] for t in batch]
       UpperCamelCase__         =    [t[1] for t in batch]
       assert len(snake_case_      ) == len(snake_case_      )

       # Max for paddings
       UpperCamelCase__         =    max(snake_case_      )

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

       UpperCamelCase__         =    torch.tensor(tk_      )  # (bs, max_seq_len_)
       UpperCamelCase__         =    torch.tensor(snake_case_      )  # (bs)
       return tk_t, lg_t












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




