


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

from utils import logger







class  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :Tuple    ,__snake_case     :List[Any]    ,__snake_case     :Any   )      ->					Tuple:
												a__    =  params

												a__    =  np.array(__snake_case   )
												a__    =  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     :str    ,__snake_case     :Union[str, Any]   )      ->					int:
												return (self.token_ids[index], self.lengths[index])
					def __len__(   self     :Union[str, Any]   )      ->					List[Any]:
												return len(self.lengths   )
					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					str:
												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     :int   )      ->					Optional[int]:
												a__    =  self.params.max_model_input_size
												a__    =  self.lengths > max_len
												logger.info(F'Splitting {sum(__snake_case   )} too long sequences.'   )

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

												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(__snake_case    ,0    ,__snake_case   )
																																	if sub_s[-1] != sep_id:
																																								a__    =  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]   )

												a__    =  np.array(__snake_case   )
												a__    =  np.array(__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Dict:
												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 				lowerCamelCase__(   self     :List[Any]   )      ->					Tuple:
												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 				lowerCamelCase__(   self     :List[str]   )      ->					List[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 				lowerCamelCase__(   self     :int    ,__snake_case     :Optional[int]   )      ->					int:
												a__    =  [t[0] for t in batch]
												a__    =  [t[1] for t in batch]
												assert len(__snake_case   ) == len(__snake_case   )

												# Max for paddings
												a__    =  max(__snake_case   )

												# 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(__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_   )

												a__    =  torch.tensor(tk_   )  # (bs, max_seq_len_)
												a__    =  torch.tensor(__snake_case   )  # (bs)
												return tk_t, lg_t








def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if length <= 0 or not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise ValueError('Length must be a positive integer.'     )
							return [n * (2 * n - 1) for n in range(__lowerCAmelCase     )]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))




