



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

from utils import logger



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			params

    UpperCAmelCase			:      Union[str, Any]			=			np.array(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			np.array([len(_SCREAMING_SNAKE_CASE		) 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   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




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

   def __len__(     self		)						->   Any:





    '''simple docstring'''




    return len(self.lengths		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[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 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.params.max_model_input_size
    UpperCAmelCase			:      Optional[int]			=			self.lengths > max_len
    logger.info(F"Splitting {sum(_SCREAMING_SNAKE_CASE		)} too long sequences."		)

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

    UpperCAmelCase			:      Union[str, Any]			=			[]
    UpperCAmelCase			:      Union[str, Any]			=			[]
    if self.params.mlm:
     UpperCAmelCase			:      Dict			=			self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
    else:
     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			:      Dict			=			np.insert(_SCREAMING_SNAKE_CASE   ,  0   ,  _SCREAMING_SNAKE_CASE		)
       if sub_s[-1] != sep_id:
        UpperCAmelCase			:      Dict			=			np.insert(_SCREAMING_SNAKE_CASE   ,  len(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)
       assert len(_SCREAMING_SNAKE_CASE		) <= max_len
       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
       sub_seqs.append(_SCREAMING_SNAKE_CASE		)

      new_tok_ids.extend(_SCREAMING_SNAKE_CASE		)
      new_lengths.extend([len(_SCREAMING_SNAKE_CASE		) for l in sub_seqs]		)

    UpperCAmelCase			:      str			=			np.array(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			np.array(_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     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 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			[t[0] for t in batch]
    UpperCAmelCase			:      List[str]			=			[t[1] for t in batch]
    assert len(_SCREAMING_SNAKE_CASE		) == len(_SCREAMING_SNAKE_CASE		)

    # Max for paddings
    UpperCAmelCase			:      List[str]			=			max(_SCREAMING_SNAKE_CASE		)

    # Pad token ids
    if self.params.mlm:
     UpperCAmelCase			:      str			=			self.params.special_tok_ids['pad_token']
    else:
     UpperCAmelCase			:      Optional[int]			=			self.params.special_tok_ids['unk_token']
    UpperCAmelCase			:      Tuple			=			[list(t.astype(_SCREAMING_SNAKE_CASE		)		) + [pad_idx] * (max_seq_len_ - len(_SCREAMING_SNAKE_CASE		)) for t in token_ids]
    assert len(tk_		) == len(_SCREAMING_SNAKE_CASE		)
    assert all(len(_SCREAMING_SNAKE_CASE		) == max_seq_len_ for t in tk_		)

    UpperCAmelCase			:      Union[str, Any]			=			torch.tensor(tk_		)  # (bs, max_seq_len_)
    UpperCAmelCase			:      List[str]			=			torch.tensor(_SCREAMING_SNAKE_CASE		)  # (bs)
    return tk_t, lg_t





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


A:     Dict										=	{"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
      from .tokenization_bertweet import BertweetTokenizer

else:
      import sys

      A:     int										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
