






"""simple docstring"""


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

from utils import logger


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= params

     __A									= np.array(_lowerCamelCase  )
     __A									= np.array([len(_lowerCamelCase  ) 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 :     Union[str, Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     return (self.token_ids[index], self.lengths[index])


   def __len__(	self :     Any  ):

     '''simple docstring'''
     return len(self.lengths  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''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 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= self.params.max_model_input_size
     __A									= self.lengths > max_len
     logger.info(f'Splitting {sum(_lowerCamelCase  )} too long sequences.'  )

     def divide_chunks(_lowerCamelCase :     Dict,       _lowerCamelCase :     Optional[Any]  ):
       return [l[i : i + n] for i in range(0,       len(_lowerCamelCase  ),       _lowerCamelCase  )]

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

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

     __A									= np.array(_lowerCamelCase  )
     __A									= np.array(_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= len(self  )
     __A									= self.lengths > 11
     __A									= self.token_ids[indices]
     __A									= self.lengths[indices]
     __A									= len(self  )
     logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''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 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     __A									= [t[0] for t in batch]
     __A									= [t[1] for t in batch]
     assert len(_lowerCamelCase  ) == len(_lowerCamelCase  )

     # Max for paddings
     __A									= max(_lowerCamelCase  )

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

     __A									= torch.tensor(tk_  )  # (bs, max_seq_len_)
     __A									= torch.tensor(_lowerCamelCase  )  # (bs)
     return tk_t, lg_t












"""simple docstring"""


from collections import defaultdict
from math import ceil, sqrt



def 					lowerCAmelCase				(    __UpperCamelCase = 1_0_0_0_0_0_0		,       __UpperCamelCase = 1_0 ):




  """simple docstring"""







  __A									= defaultdict(__UpperCamelCase )

  for outer_width in range(3		,       (t_limit // 4) + 2 ):
    if outer_width * outer_width > t_limit:
      __A									= max(
          ceil(sqrt(outer_width * outer_width - t_limit ) )		,       1 )
    else:
      __A									= 1

    hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

    for hole_width in range(__UpperCamelCase		,       outer_width - 1		,       2 ):
      count[outer_width * outer_width - hole_width * hole_width] += 1

  return sum(1 for n in count.values() if 1 <= n <= 1_0 )


if __name__ == "__main__":
       print(F'''{solution() = }''')




