
"""simple docstring"""





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

from utils import logger


class A_	(lowercase__      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    params

     UpperCAmelCase_					:					int			     =    np.array(lowercase_  )
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array([len(lowercase_  ) 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				,   lowercase_  ):

     """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"""
     UpperCAmelCase_					:					List[str]			     =    self.params.max_model_input_size
     UpperCAmelCase_					:					str			     =    self.lengths > max_len
     logger.info(F"""Splitting {sum(lowercase_  )} too long sequences."""  )

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

     UpperCAmelCase_					:					Union[str, Any]			     =    []
     UpperCAmelCase_					:					List[Any]			     =    []
     if self.params.mlm:
        UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
     else:
        UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    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:
           UpperCAmelCase_					:					Optional[int]			     =    []
           for sub_s in divide_chunks(seq_				,   max_len - 2  ):
              if sub_s[0] != cls_id:
                 UpperCAmelCase_					:					str			     =    np.insert(lowercase_				,   0				,   lowercase_  )
              if sub_s[-1] != sep_id:
                 UpperCAmelCase_					:					List[str]			     =    np.insert(lowercase_				,   len(lowercase_  )				,   lowercase_  )
              assert len(lowercase_  ) <= max_len
              assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
              sub_seqs.append(lowercase_  )

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

     UpperCAmelCase_					:					List[str]			     =    np.array(lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    np.array(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    len(self  )
     UpperCAmelCase_					:					Tuple			     =    self.lengths > 11
     UpperCAmelCase_					:					Any			     =    self.token_ids[indices]
     UpperCAmelCase_					:					List[Any]			     =    self.lengths[indices]
     UpperCAmelCase_					:					List[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:
        UpperCAmelCase_					:					List[str]			     =    self.params.special_tok_ids["unk_token"]
     UpperCAmelCase_					:					Optional[Any]			     =    len(self  )
     UpperCAmelCase_					:					List[str]			     =    np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
     UpperCAmelCase_					:					List[Any]			     =    (unk_occs / self.lengths) < 0.5
     UpperCAmelCase_					:					Tuple			     =    self.token_ids[indices]
     UpperCAmelCase_					:					str			     =    self.lengths[indices]
     UpperCAmelCase_					:					int			     =    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				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    [t[0] for t in batch]
     UpperCAmelCase_					:					Tuple			     =    [t[1] for t in batch]
     assert len(lowercase_  ) == len(lowercase_  )

     # Max for paddings
     UpperCAmelCase_					:					int			     =    max(lowercase_  )

     # Pad token ids
     if self.params.mlm:
        UpperCAmelCase_					:					Optional[Any]			     =    self.params.special_tok_ids["pad_token"]
     else:
        UpperCAmelCase_					:					int			     =    self.params.special_tok_ids["unk_token"]
     UpperCAmelCase_					:					int			     =    [list(t.astype(lowercase_  )  ) + [pad_idx] * (max_seq_len_ - len(lowercase_  )) for t in token_ids]
     assert len(tk_  ) == len(lowercase_  )
     assert all(len(lowercase_  ) == max_seq_len_ for t in tk_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.tensor(tk_  )  # (bs, max_seq_len_)
     UpperCAmelCase_					:					str			     =    torch.tensor(lowercase_  )  # (bs)
     return tk_t, lg_t





class  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    name
											snake_case_							=    value
											snake_case_							=    weight




				def __repr__(					self       ):
											return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''




				def     a							(					self       ):
											return self.value




				def     a							(					self       ):
											return self.name




				def     a							(					self       ):
											return self.weight




				def     a							(					self       ):
											return self.value / self.weight





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							for i in range(len(UpperCamelCase__      )      ):
														menu.append(Things(name[i]   ,							value[i]   ,							weight[i]      )      )
							return menu





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    sorted(UpperCamelCase__   ,							key=UpperCamelCase__   ,							reverse=UpperCamelCase__      )
							snake_case_							=    []
							snake_case_				,    snake_case_							=    0.0, 0.0
							for i in range(len(UpperCamelCase__      )      ):
														if (total_cost + items_copy[i].get_weight()) <= max_cost:
																					result.append(items_copy[i]      )
																					total_cost += items_copy[i].get_weight()
																					total_value += items_copy[i].get_value()
							return (result, total_value)





def 					__lowerCamelCase	( ):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




