


import mpmath  # for roots of unity
import numpy as np







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None    ):
       # Input as list
       a						:int 							=       list(poly_a or [0]    )[:]
       a						:List[Any] 							=       list(poly_b or [0]    )[:]

       # Remove leading zero coefficients
       while self.polyA[-1] == 0:
             self.polyA.pop()
       a						:Tuple 							=       len(self.polyA    )

       while self.polyB[-1] == 0:
             self.polyB.pop()
       a						:Any 							=       len(self.polyB    )

       # Add 0 to make lengths equal a power of 2
       a						:Dict 							=       int(
           2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

       while len(self.polyA    ) < self.c_max_length:
             self.polyA.append(0    )
       while len(self.polyB    ) < self.c_max_length:
             self.polyB.append(0    )
       # A complex root used for the fourier transform
       a						:Union[str, Any] 							=       complex(mpmath.root(x=1					,  n=self.c_max_length					,  k=1    )    )

       # The product
       a						:Tuple 							=       self.__multiply()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
       # Corner case
       if len(_lowerCamelCase    ) <= 1:
             return dft[0]
       #
       a						:Dict 							=       self.c_max_length // 2
       while next_ncol > 0:
             a						:Union[str, Any] 							=       [[] for i in range(_lowerCamelCase    )]
             a						:Union[str, Any] 							=       self.root**next_ncol

             # First half of next step
             a						:str 							=       1
             for j in range(self.c_max_length // (next_ncol * 2)    ):
                   for i in range(_lowerCamelCase    ):
                         new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
                   current_root *= root
             # Second half of next step
             a						:int 							=       1
             for j in range(self.c_max_length // (next_ncol * 2)    ):
                   for i in range(_lowerCamelCase    ):
                         new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
                   current_root *= root
             # Update
             a						:Tuple 							=       new_dft
             a						:Optional[Any] 							=       next_ncol // 2
       return dft[0]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       self.__dft('''A'''    )
       a						:List[Any] 							=       self.__dft('''B'''    )
       a						:Dict 							=       [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
       del dft_a
       del dft_b

       # Corner Case
       if len(inverce_c[0]    ) <= 1:
             return inverce_c[0]
       # Inverse DFT
       a						:Dict 							=       2
       while next_ncol <= self.c_max_length:
             a						:str 							=       [[] for i in range(_lowerCamelCase    )]
             a						:List[Any] 							=       self.root ** (next_ncol // 2)
             a						:List[str] 							=       1
             # First half of next step
             for j in range(self.c_max_length // next_ncol    ):
                   for i in range(next_ncol // 2    ):
                         # Even positions
                         new_inverse_c[i].append(
                             (
                                 inverce_c[i][j]
                                 + inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / 2    )
                         # Odd positions
                         new_inverse_c[i + next_ncol // 2].append(
                             (
                                 inverce_c[i][j]
                                 - inverce_c[i][j + self.c_max_length // next_ncol]
                             )
                             / (2 * current_root)    )
                   current_root *= root
             # Update
             a						:int 							=       new_inverse_c
             next_ncol *= 2
       # Unpack
       a						:Any 							=       [round(x[0].real					,  8    ) + round(x[0].imag					,  8    ) * 1j for x in inverce_c]

       # Remove leading 0's
       while inverce_c[-1] == 0:
             inverce_c.pop()
       return inverce_c




 def __str__(		self    ):
       a						:Dict 							=       '''A = ''' + ''' + '''.join(
           F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
       a						:Any 							=       '''B = ''' + ''' + '''.join(
           F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
       a						:Tuple 							=       '''A*B = ''' + ''' + '''.join(
           F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

       return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""

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

from utils import logger




class        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Optional[int]  ,     lowercase_		:       str  ,     lowercase_		:       int				):
     snake_case_ :     Dict				=	params

     snake_case_ :     Union[str, Any]				=	np.array(lowercase_				)
     snake_case_ :     str				=	np.array([len(lowercase_				) 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		:       Dict  ,     lowercase_		:       Union[str, Any]				):
     return (self.token_ids[index], self.lengths[index])

 def __len__(     self		:       List[Any]				):
     return len(self.lengths				)

 def 						_snake_case					(     self		:       Tuple				):
     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 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     str				=	self.params.max_model_input_size
     snake_case_ :     Dict				=	self.lengths > max_len
     logger.info(f"Splitting {sum(lowercase_				)} too long sequences."				)

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

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

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

     snake_case_ :     List[str]				=	np.array(lowercase_				)
     snake_case_ :     Optional[Any]				=	np.array(lowercase_				)

 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     List[Any]				=	len(self				)
     snake_case_ :     List[str]				=	self.lengths > 11
     snake_case_ :     Dict				=	self.token_ids[indices]
     snake_case_ :     Dict				=	self.lengths[indices]
     snake_case_ :     str				=	len(self				)
     logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."				)

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

 def 						_snake_case					(     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 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict				):
     snake_case_ :     Optional[int]				=	[t[0] for t in batch]
     snake_case_ :     str				=	[t[1] for t in batch]
     assert len(lowercase_				) == len(lowercase_				)

     # Max for paddings
     snake_case_ :     str				=	max(lowercase_				)

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

     snake_case_ :     str				=	torch.tensor(tk_				)  # (bs, max_seq_len_)
     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_				)  # (bs)
     return tk_t, lg_t





