
"""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










from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowerCamelCase	:							int				=TypeVar('''T''')





class 			__a  (    Generic[T]       ):
 _lowerCAmelCase       :       deque[T]  # Cache store of keys
 _lowerCAmelCase       :       set[T]  # References of the keys in cache
 _lowerCAmelCase       :       int        = 1_0  # Maximum capacity of cache







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  deque()
      UpperCamelCase__			:   Optional[int]       				=  set()
      if not n:
           UpperCamelCase__			:   Tuple       				=  sys.maxsize
      elif n < 0:
           raise ValueError("n should be an integer greater than 0."						)
      else:
           UpperCamelCase__			:   Dict       				=  n







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   T						):


      '''simple docstring'''






      if x not in self.key_reference:
           if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
                UpperCamelCase__			:   int       				=  self.dq_store.pop()
                self.key_reference.remove(SCREAMING_SNAKE_CASE						)
      else:
           self.dq_store.remove(SCREAMING_SNAKE_CASE						)

      self.dq_store.appendleft(SCREAMING_SNAKE_CASE						)
      self.key_reference.add(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for k in self.dq_store:
           print(SCREAMING_SNAKE_CASE						)







 def __repr__(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase	:							LRUCache[str | int]				=LRUCache(4)
 lru_cache.refer('''A''')
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer('''A''')
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"