

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

from utils import logger
class  _A		(  __magic_name__):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     params

        SCREAMING_SNAKE_CASE_				:  Any           =     np.array(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     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					):





        """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"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.params.max_model_input_size
        SCREAMING_SNAKE_CASE_				:  Dict           =     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					)]

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     []
        SCREAMING_SNAKE_CASE_				:  Tuple           =     []
        if self.params.mlm:
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Tuple           =     self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  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:
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     []
                    for sub_s in divide_chunks(seq_ ,					max_len - 2					):
                          if sub_s[0] != cls_id:
                                SCREAMING_SNAKE_CASE_				:  Dict           =     np.insert(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE					)
                          if sub_s[-1] != sep_id:
                                SCREAMING_SNAKE_CASE_				:  Tuple           =     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]					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.array(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array(_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     len(self					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.lengths > 11
        SCREAMING_SNAKE_CASE_				:  int           =     self.token_ids[indices]
        SCREAMING_SNAKE_CASE_				:  int           =     self.lengths[indices]
        SCREAMING_SNAKE_CASE_				:  Dict           =     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:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.params.special_tok_ids['unk_token']
        SCREAMING_SNAKE_CASE_				:  int           =     len(self					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (unk_occs / self.lengths) < 0.5
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.token_ids[indices]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.lengths[indices]
        SCREAMING_SNAKE_CASE_				:  Any           =     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 ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

        # Max for paddings
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     max(_SCREAMING_SNAKE_CASE					)

        # Pad token ids
        if self.params.mlm:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     self.params.special_tok_ids['pad_token']
        else:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.params.special_tok_ids['unk_token']
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     [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_					)

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









from math import pi

def    A_  (							a					,   a  ):

      """simple docstring"""



      return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
    print(arc_length(90, 10))






