

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

from utils import logger



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__			):
						A__							:    Union[str, Any]        =		params

						A__							:    Dict        =		np.array(A__			)
						A__							:    int        =		np.array([len(A__			) 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    ,       A__			):
						return (self.token_ids[index], self.lengths[index])






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






					def   __A			( self			):
						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   __A			( self			):
						A__							:    Optional[int]        =		self.params.max_model_input_size
						A__							:    Tuple        =		self.lengths > max_len
						logger.info(F"""Splitting {sum(A__			)} too long sequences."""			)

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

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

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

						A__							:    List[Any]        =		np.array(A__			)
						A__							:    Union[str, Any]        =		np.array(A__			)






					def   __A			( self			):
						A__							:    Optional[int]        =		len(self			)
						A__							:    Dict        =		self.lengths > 11
						A__							:    str        =		self.token_ids[indices]
						A__							:    Tuple        =		self.lengths[indices]
						A__							:    int        =		len(self			)
						logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""			)






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






					def   __A			( self			):
						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   __A			( self    ,       A__			):
						A__							:    List[str]        =		[t[0] for t in batch]
						A__							:    Optional[int]        =		[t[1] for t in batch]
						assert len(A__			) == len(A__			)

						# Max for paddings
						A__							:    str        =		max(A__			)

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

						A__							:    int        =		torch.tensor(tk_			)  # (bs, max_seq_len_)
						A__							:    str        =		torch.tensor(A__			)  # (bs)
						return tk_t, lg_t






def 						UpperCamelCase					(lowercase_: int = 10					)     ->      str:
	if not isinstance(lowercase_    ,      lowercase_					) or n < 0:
		raise ValueError("""Invalid input"""					)
	A__							:    List[str]        =		10**n
	A__							:    Any        =		28433 * (pow(2    ,      7830457    ,      lowercase_					)) + 1
	return str(number % modulus					)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	print(f'''{solution(10) = }''')



