


'''simple docstring'''



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

from utils import logger





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




 def __init__(       self						,    __a						,    __a							):

      '''simple docstring'''





      __a      :	str    =  params

      __a      :	Union[str, Any]    =  np.array(_A							)
      __a      :	List[str]    =  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						,    __a							):

      '''simple docstring'''





      return (self.token_ids[index], self.lengths[index])




 def __len__(       self							):

      '''simple docstring'''





      return len(self.lengths							)




 def 				__UpperCAmelCase							(       self							):

      '''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							):

      '''simple docstring'''





      __a      :	Tuple    =  self.params.max_model_input_size
      __a      :	Union[str, Any]    =  self.lengths > max_len
      logger.info(f"""Splitting {sum(_A							)} too long sequences."""							)

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

      __a      :	Optional[int]    =  []
      __a      :	int    =  []
      if self.params.mlm:
           __a      :	int    =  self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
      else:
           __a      :	Optional[Any]    =  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      :	Optional[Any]    =  []
                for sub_s in divide_chunks(seq_						,    max_len - 2							):
                     if sub_s[0] != cls_id:
                          __a      :	Optional[Any]    =  np.insert(_A						,    0						,    _A							)
                     if sub_s[-1] != sep_id:
                          __a      :	List[Any]    =  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      :	Union[str, Any]    =  np.array(_A							)
      __a      :	Union[str, Any]    =  np.array(_A							)




 def 				__UpperCAmelCase							(       self							):

      '''simple docstring'''





      __a      :	List[str]    =  len(self							)
      __a      :	Dict    =  self.lengths > 11
      __a      :	Union[str, Any]    =  self.token_ids[indices]
      __a      :	str    =  self.lengths[indices]
      __a      :	Any    =  len(self							)
      logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""							)




 def 				__UpperCAmelCase							(       self							):

      '''simple docstring'''





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




 def 				__UpperCAmelCase							(       self							):

      '''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						,    __a							):

      '''simple docstring'''





      __a      :	int    =  [t[0] for t in batch]
      __a      :	Any    =  [t[1] for t in batch]
      assert len(_A							) == len(_A							)

      # Max for paddings
      __a      :	Any    =  max(_A							)

      # Pad token ids
      if self.params.mlm:
           __a      :	Optional[int]    =  self.params.special_tok_ids['pad_token']
      else:
           __a      :	Tuple    =  self.params.special_tok_ids['unk_token']
      __a      :	List[str]    =  [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      :	Tuple    =  torch.tensor(tk_							)  # (bs, max_seq_len_)
      __a      :	Any    =  torch.tensor(_A							)  # (bs)
      return tk_t, lg_t







'''simple docstring'''



__lowercase     :						Optional[Any]	=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase     :						List[str]	=     ['a', 'b', 'c', 'd', 'e']






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	Any    =  start
					# add current to visited
					visited.append(_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															__a      :	Dict    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # if all neighbors visited add current to sort
					sort.append(_SCREAMING_SNAKE_CASE       )
					# if all vertices haven't been visited select a new one to visit
					if len(_SCREAMING_SNAKE_CASE       ) != len(_SCREAMING_SNAKE_CASE       ):
										for vertice in vertices:
															if vertice not in visited:
																				__a      :	List[Any]    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # return sort
					return sort


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     topological_sort('a', [], [])
	print(sort)



