





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

from utils import logger



class    A__  (			__snake_case     ):





      def __init__(					self			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  params

       UpperCamelCase      :   Tuple			      =  np.array(A_  )
       UpperCamelCase      :   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_  ):
       '''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[Any]			      =  self.params.max_model_input_size
       UpperCamelCase      :   List[str]			      =  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_  )]

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

       UpperCamelCase      :   List[str]			      =  np.array(A_  )
       UpperCamelCase      :   int			      =  np.array(A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  len(self  )
       UpperCamelCase      :   List[Any]			      =  self.lengths > 11
       UpperCamelCase      :   Optional[Any]			      =  self.token_ids[indices]
       UpperCamelCase      :   Optional[Any]			      =  self.lengths[indices]
       UpperCamelCase      :   int			      =  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      :   Dict			      =  len(self  )
       UpperCamelCase      :   str			      =  np.array([np.count_nonzero(a == unk_token_id  ) for a in self.token_ids]  )
       UpperCamelCase      :   str			      =  (unk_occs / self.lengths) < 0.5
       UpperCamelCase      :   List[Any]			      =  self.token_ids[indices]
       UpperCamelCase      :   Tuple			      =  self.lengths[indices]
       UpperCamelCase      :   List[str]			      =  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'''




       UpperCamelCase      :   str			      =  [t[0] for t in batch]
       UpperCamelCase      :   Tuple			      =  [t[1] for t in batch]
       assert len(A_  ) == len(A_  )

       # Max for paddings
       UpperCamelCase      :   List[str]			      =  max(A_  )

       # Pad token ids
       if self.params.mlm:
        UpperCamelCase      :   List[Any]			      =  self.params.special_tok_ids["pad_token"]
       else:
        UpperCamelCase      :   str			      =  self.params.special_tok_ids["unk_token"]
       UpperCamelCase      :   Optional[int]			      =  [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_  )

       UpperCamelCase      :   Optional[int]			      =  torch.tensor(tk_  )  # (bs, max_seq_len_)
       UpperCamelCase      :   int			      =  torch.tensor(A_  )  # (bs)
       return tk_t, lg_t







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCamelCase			:	Optional[Any]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):





      def __init__(					self			,				*A_			,				**A_  ):
       '''simple docstring'''




       warnings.warn(
           "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use BeitImageProcessor instead."			,				A_			,				)
       super().__init__(*A_			,				**A_  )
