


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

from utils import logger



class A__     ( A__  ):



  """simple docstring"""



  def __init__( self			:  Dict				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  Any       ):
        a__	:  str     =   params

        a__	:  Any     =   np.array(lowerCamelCase__       )
        a__	:  Dict     =   np.array([len(lowerCamelCase__       ) 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			:  Optional[int]				,				lowerCamelCase__			:  Union[str, Any]       ):
        return (self.token_ids[index], self.lengths[index])

  def __len__( self			:  Any       ):
        return len(self.lengths       )

  def    _UpperCamelCase( self			:  Dict       ):
        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			:  int       ):
        a__	:  List[Any]     =   self.params.max_model_input_size
        a__	:  str     =   self.lengths > max_len
        logger.info(f'''Splitting {sum(lowerCamelCase__       )} too long sequences.'''       )

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

        a__	:  Union[str, Any]     =   []
        a__	:  str     =   []
        if self.params.mlm:
              a__,	a__	:  List[str]     =   self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
        else:
              a__,	a__	:  Dict     =   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:
                    a__	:  Tuple     =   []
                    for sub_s in divide_chunks(seq_				,				max_len - 2       ):
                          if sub_s[0] != cls_id:
                                a__	:  Optional[Any]     =   np.insert(lowerCamelCase__				,				0				,				lowerCamelCase__       )
                          if sub_s[-1] != sep_id:
                                a__	:  int     =   np.insert(lowerCamelCase__				,				len(lowerCamelCase__       )				,				lowerCamelCase__       )
                          assert len(lowerCamelCase__       ) <= max_len
                          assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                          sub_seqs.append(lowerCamelCase__       )

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

        a__	:  Dict     =   np.array(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   np.array(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str       ):
        a__	:  Tuple     =   len(self       )
        a__	:  str     =   self.lengths > 11
        a__	:  Union[str, Any]     =   self.token_ids[indices]
        a__	:  List[Any]     =   self.lengths[indices]
        a__	:  Tuple     =   len(self       )
        logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''       )

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

  def    _UpperCamelCase( self			:  Dict       ):
        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				,				lowerCamelCase__			:  Tuple       ):
        a__	:  str     =   [t[0] for t in batch]
        a__	:  Any     =   [t[1] for t in batch]
        assert len(lowerCamelCase__       ) == len(lowerCamelCase__       )

        # Max for paddings
        a__	:  Dict     =   max(lowerCamelCase__       )

        # Pad token ids
        if self.params.mlm:
              a__	:  Optional[int]     =   self.params.special_tok_ids["pad_token"]
        else:
              a__	:  int     =   self.params.special_tok_ids["unk_token"]
        a__	:  str     =   [list(t.astype(lowerCamelCase__       )       ) + [pad_idx] * (max_seq_len_ - len(lowerCamelCase__       )) for t in token_ids]
        assert len(tk_       ) == len(lowerCamelCase__       )
        assert all(len(lowerCamelCase__       ) == max_seq_len_ for t in tk_       )

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








import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

   def is_too_big(A     ):
      return tok(A				,							return_tensors="pt"     ).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=new_tgt + " " + tgt
      if is_too_big(A     ) or is_too_big(A     ):  # cant fit, finalize example
         finished_src.append(A     )
         finished_tgt.append(A     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(A     )
      finished_tgt.append(A     )
   return finished_src, finished_tgt






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=pack_examples(A				,							A				,							A				,							A     )
      print(F"""packed {split} split from {len(A     )} examples -> {len(A     )}."""     )
      Path(save_path / F"""{split}.source"""     ).open("w"     ).write("\n".join(A     )     )
      Path(save_path / F"""{split}.target"""     ).open("w"     ).write("\n".join(A     )     )
   for split in ["val", "test"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      shutil.copyfile(A				,							save_path / F"""{split}.source"""     )
      shutil.copyfile(A				,							save_path / F"""{split}.target"""     )






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("--tok_name"				,							type=A				,							help="like facebook/bart-large-cnn,t5-base, etc."     )
   parser.add_argument("--max_seq_len"				,							type=A				,							default=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=AutoTokenizer.from_pretrained(args.tok_name     )
   return pack_data_dir(A				,							Path(args.data_dir     )				,							args.max_seq_len				,							args.save_path     )


if __name__ == "__main__":
    packer_cli()



