




"""simple docstring"""


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

from utils import logger






class 					lowercase(     __a ):







 '''simple docstring'''





 def __init__(				self:		Any,      a_:		Any,      a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Any								=  params

   _snake_case							:		int								=  np.array(a_		)
   _snake_case							:		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:		List[str],      a_:		Dict		):




   '''simple docstring'''



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


 def __len__(				self:		Optional[Any]		):




   '''simple docstring'''



   return len(self.lengths		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''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:		Dict		):




   '''simple docstring'''



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

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

   _snake_case							:		List[Any]								=  []
   _snake_case							:		Optional[int]								=  []
   if self.params.mlm:
     _snake_case		,					_snake_case							:		Optional[int]								=  self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
   else:
     _snake_case		,					_snake_case							:		Optional[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:
       _snake_case							:		Optional[Any]								=  []
       for sub_s in divide_chunks(seq_,      max_len - 2		):
         if sub_s[0] != cls_id:
           _snake_case							:		Optional[int]								=  np.insert(a_,      0,      a_		)
         if sub_s[-1] != sep_id:
           _snake_case							:		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]		)

   _snake_case							:		Union[str, Any]								=  np.array(a_		)
   _snake_case							:		List[Any]								=  np.array(a_		)


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  len(self		)
   _snake_case							:		int								=  self.lengths > 11
   _snake_case							:		str								=  self.token_ids[indices]
   _snake_case							:		int								=  self.lengths[indices]
   _snake_case							:		int								=  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:
     _snake_case							:		Dict								=  self.params.special_tok_ids["""unk_token"""]
   _snake_case							:		Optional[int]								=  len(self		)
   _snake_case							:		List[str]								=  np.array([np.count_nonzero(a == unk_token_id		) for a in self.token_ids]		)
   _snake_case							:		Dict								=  (unk_occs / self.lengths) < 0.5
   _snake_case							:		int								=  self.token_ids[indices]
   _snake_case							:		Optional[Any]								=  self.lengths[indices]
   _snake_case							:		Dict								=  len(self		)
   logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."		)


 def  UpperCamelCase_   (				self:		Any		):




   '''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:		Dict,      a_:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  [t[0] for t in batch]
   _snake_case							:		int								=  [t[1] for t in batch]
   assert len(a_		) == len(a_		)

   # Max for paddings
   _snake_case							:		int								=  max(a_		)

   # Pad token ids
   if self.params.mlm:
     _snake_case							:		Any								=  self.params.special_tok_ids["""pad_token"""]
   else:
     _snake_case							:		Optional[Any]								=  self.params.special_tok_ids["""unk_token"""]
   _snake_case							:		Dict								=  [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_		)

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








"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


