

'''simple docstring'''





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

from utils import logger


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      params

							lowerCamelCase_  				=      np.array(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      np.array([len(SCREAMING_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   ,			SCREAMING_SNAKE_CASE_		)  ->     List[str]:






							'''simple docstring'''



							return (self.token_ids[index], self.lengths[index])
		def __len__(					self		)  ->     Any:






							'''simple docstring'''



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






							'''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 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.params.max_model_input_size
							lowerCamelCase_  				=      self.lengths > max_len
							logger.info(f'''Splitting {sum(SCREAMING_SNAKE_CASE_		)} too long sequences.'''		)

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

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

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

							lowerCamelCase_  				=      np.array(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      np.array(SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      len(self		)
							lowerCamelCase_  				=      self.lengths > 11
							lowerCamelCase_  				=      self.token_ids[indices]
							lowerCamelCase_  				=      self.lengths[indices]
							lowerCamelCase_  				=      len(self		)
							logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''		)
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



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






							'''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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      [t[0] for t in batch]
							lowerCamelCase_  				=      [t[1] for t in batch]
							assert len(SCREAMING_SNAKE_CASE_		) == len(SCREAMING_SNAKE_CASE_		)

							# Max for paddings
							lowerCamelCase_  				=      max(SCREAMING_SNAKE_CASE_		)

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

							lowerCamelCase_  				=      torch.tensor(tk_		)  # (bs, max_seq_len_)
							lowerCamelCase_  				=      torch.tensor(SCREAMING_SNAKE_CASE_		)  # (bs)
							return tk_t, lg_t










from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
