



"""simple docstring"""





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

from utils import logger
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       def __init__(				self     :    Optional[int]						,							_UpperCAmelCase     :    Any						,							_UpperCAmelCase     :    Optional[int]       )					->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_     					=				params

             UpperCAmelCase_     					=				np.array(_UpperCAmelCase       )
             UpperCAmelCase_     					=				np.array([len(_UpperCAmelCase       ) 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     :    str						,							_UpperCAmelCase     :    Optional[int]       )					->		Any:


             '''simple docstring'''




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



       def __len__(				self     :    Union[str, Any]       )					->		Optional[int]:


             '''simple docstring'''




             return len(self.lengths       )



       def 	lowercase__							(				self     :    Optional[Any]       )					->		Optional[int]:


             '''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 	lowercase__							(				self     :    str       )					->		Any:


             '''simple docstring'''




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

             def divide_chunks(_UpperCAmelCase     :    Tuple						,							_UpperCAmelCase     :    Union[str, Any]       ):
                   return [l[i : i + n] for i in range(0						,							len(_UpperCAmelCase       )						,							_UpperCAmelCase       )]

             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(_UpperCAmelCase						,							0						,							_UpperCAmelCase       )
                               if sub_s[-1] != sep_id:
                                     UpperCAmelCase_     					=				np.insert(_UpperCAmelCase						,							len(_UpperCAmelCase       )						,							_UpperCAmelCase       )
                               assert len(_UpperCAmelCase       ) <= max_len
                               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                               sub_seqs.append(_UpperCAmelCase       )

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

             UpperCAmelCase_     					=				np.array(_UpperCAmelCase       )
             UpperCAmelCase_     					=				np.array(_UpperCAmelCase       )



       def 	lowercase__							(				self     :    Any       )					->		Dict:


             '''simple docstring'''




             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 	lowercase__							(				self     :    Optional[Any]       )					->		Optional[int]:


             '''simple docstring'''




             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 	lowercase__							(				self     :    Any       )					->		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 	lowercase__							(				self     :    Optional[int]						,							_UpperCAmelCase     :    Optional[Any]       )					->		Any:


             '''simple docstring'''




             UpperCAmelCase_     					=				[t[0] for t in batch]
             UpperCAmelCase_     					=				[t[1] for t in batch]
             assert len(_UpperCAmelCase       ) == len(_UpperCAmelCase       )

             # Max for paddings
             UpperCAmelCase_     					=				max(_UpperCAmelCase       )

             # 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(_UpperCAmelCase       )       ) + [pad_idx] * (max_seq_len_ - len(_UpperCAmelCase       )) for t in token_ids]
             assert len(tk_       ) == len(_UpperCAmelCase       )
             assert all(len(_UpperCAmelCase       ) == max_seq_len_ for t in tk_       )

             UpperCAmelCase_     					=				torch.tensor(tk_       )  # (bs, max_seq_len_)
             UpperCAmelCase_     					=				torch.tensor(_UpperCAmelCase       )  # (bs)
             return tk_t, lg_t












from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


