





'''simple docstring'''







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

from utils import logger
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Optional[int]					,				__a     :					Union[str, Any]					,				__a     :					str					):
								_a								= params

								_a								= np.array(__a					)
								_a								= 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     :					Dict					,				__a     :					Optional[Any]					):
								return (self.token_ids[index], self.lengths[index])




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




				def   UpperCamelCase__   (  self     :					int					):
								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     :					Union[str, Any]					):
								_a								= self.params.max_model_input_size
								_a								= self.lengths > max_len
								logger.info(f'Splitting {sum(__a					)} too long sequences.'					)

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

								_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(__a					,				0					,				__a					)
																				if sub_s[-1] != sep_id:
																								_a								= 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]					)

								_a								= np.array(__a					)
								_a								= np.array(__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_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   UpperCamelCase__   (  self     :					Optional[Any]					):
								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   UpperCamelCase__   (  self     :					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   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					):
								_a								= [t[0] for t in batch]
								_a								= [t[1] for t in batch]
								assert len(__a					) == len(__a					)

								# Max for paddings
								_a								= max(__a					)

								# 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(__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_					)

								_a								= torch.tensor(tk_					)  # (bs, max_seq_len_)
								_a								= torch.tensor(__a					)  # (bs)
								return tk_t, lg_t







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str						)	->	list:
				if n_term == "":
								return []
				_a								= []
				for temp in range(int(lowercase						)						):
								series.append(F'1/{temp + 1}' if series else "1"						)
				return series


if __name__ == "__main__":
					lowerCAmelCase_					:    Union[str, Any]      	=			input('Enter the last number (nth term) of the Harmonic Series')
					print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
					print(harmonic_series(nth_term))
