


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

from utils import logger






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=params

         __UpperCamelCase    							=np.array(A_    )
         __UpperCamelCase    							=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_    )  ->       List[str]:
         return (self.token_ids[index], self.lengths[index])






    def __len__(			self    )  ->       Optional[Any]:
         return len(self.lengths    )






    def _a					(			self    )  ->       Any:
         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 _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.params.max_model_input_size
         __UpperCamelCase    							=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    							=[]
         __UpperCamelCase    							=[]
         if self.params.mlm:
              __UpperCamelCase							,      __UpperCamelCase    							=self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
         else:
              __UpperCamelCase							,      __UpperCamelCase    							=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    							=[]
                   for sub_s in divide_chunks(seq_					,   max_len - 2    ):
                        if sub_s[0] != cls_id:
                             __UpperCamelCase    							=np.insert(A_					,   0					,   A_    )
                        if sub_s[-1] != sep_id:
                             __UpperCamelCase    							=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    							=np.array(A_    )
         __UpperCamelCase    							=np.array(A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=len(self    )
         __UpperCamelCase    							=self.lengths > 11
         __UpperCamelCase    							=self.token_ids[indices]
         __UpperCamelCase    							=self.lengths[indices]
         __UpperCamelCase    							=len(self    )
         logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'    )






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






    def _a					(			self    )  ->       int:
         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 _a					(			self					,   A_    )  ->       int:
         __UpperCamelCase    							=[t[0] for t in batch]
         __UpperCamelCase    							=[t[1] for t in batch]
         assert len(A_    ) == len(A_    )

         # Max for paddings
         __UpperCamelCase    							=max(A_    )

         # Pad token ids
         if self.params.mlm:
              __UpperCamelCase    							=self.params.special_tok_ids['pad_token']
         else:
              __UpperCamelCase    							=self.params.special_tok_ids['unk_token']
         __UpperCamelCase    							=[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    							=torch.tensor(tk_    )  # (bs, max_seq_len_)
         __UpperCamelCase    							=torch.tensor(A_    )  # (bs)
         return tk_t, lg_t









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





