




"""simple docstring"""



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

from utils import logger




class      UpperCAmelCase_  (						A_							):




    def __init__(   self						:	Union[str, Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]				)				->							str:




     '''simple docstring'''

     A__	=    params

     A__	=    np.array(snake_case_				)
     A__	=    np.array([len(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						:	int     ,			snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

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




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




     '''simple docstring'''

     return len(self.lengths				)




    def 							__magic_name__							(   self						:	Tuple				)				->							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 							__magic_name__							(   self						:	Dict				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.params.max_model_input_size
     A__	=    self.lengths > max_len
     logger.info(F"""Splitting {sum(snake_case_				)} too long sequences."""				)

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

     A__	=    []
     A__	=    []
     if self.params.mlm:
      A__,				A__	=    self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
     else:
      A__,				A__	=    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__	=    []
       for sub_s in divide_chunks(seq_     ,			max_len - 2				):
        if sub_s[0] != cls_id:
         A__	=    np.insert(snake_case_     ,			0     ,			snake_case_				)
        if sub_s[-1] != sep_id:
         A__	=    np.insert(snake_case_     ,			len(snake_case_				)     ,			snake_case_				)
        assert len(snake_case_				) <= max_len
        assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
        sub_seqs.append(snake_case_				)

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

     A__	=    np.array(snake_case_				)
     A__	=    np.array(snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    len(self				)
     A__	=    self.lengths > 11
     A__	=    self.token_ids[indices]
     A__	=    self.lengths[indices]
     A__	=    len(self				)
     logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	int				)				->							Dict:




     '''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 							__magic_name__							(   self						:	str     ,			snake_case_						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    [t[0] for t in batch]
     A__	=    [t[1] for t in batch]
     assert len(snake_case_				) == len(snake_case_				)

     # Max for paddings
     A__	=    max(snake_case_				)

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

     A__	=    torch.tensor(tk_				)  # (bs, max_seq_len_)
     A__	=    torch.tensor(snake_case_				)  # (bs)
     return tk_t, lg_t











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





