

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

from utils import logger


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   a						,   a):
         lowercase__    :   Optional[Any]      =       params

         lowercase__    :   Dict      =       np.array(a)
         lowercase__    :   Optional[int]      =       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):
         return (self.token_ids[index], self.lengths[index])





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





    def       snake_case_				(      self):
         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       snake_case_				(      self):
         lowercase__    :   int      =       self.params.max_model_input_size
         lowercase__    :   int      =       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)]

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

         lowercase__    :   Tuple      =       np.array(a)
         lowercase__    :   List[str]      =       np.array(a)





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





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





    def       snake_case_				(      self):
         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       snake_case_				(      self						,   a):
         lowercase__    :   List[str]      =       [t[0] for t in batch]
         lowercase__    :   Optional[Any]      =       [t[1] for t in batch]
         assert len(a) == len(a)

         # Max for paddings
         lowercase__    :   List[str]      =       max(a)

         # Pad token ids
         if self.params.mlm:
              lowercase__    :   Optional[int]      =       self.params.special_tok_ids['pad_token']
         else:
              lowercase__    :   Tuple      =       self.params.special_tok_ids['unk_token']
         lowercase__    :   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_)

         lowercase__    :   List[str]      =       torch.tensor(tk_)  # (bs, max_seq_len_)
         lowercase__    :   Dict      =       torch.tensor(a)  # (bs)
         return tk_t, lg_t




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

