
'''simple docstring'''







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

from utils import logger






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	params

											lowercase_     :					Union[str, Any]													=	np.array(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	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      _snake_case   (       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      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	self.params.max_model_input_size
											lowercase_     :					Optional[int]													=	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		)]

											lowercase_     :					Any													=	[]
											lowercase_     :					Dict													=	[]
											if self.params.mlm:
																	lowercase_						,			lowercase_     :					List[Any]													=	self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
											else:
																	lowercase_						,			lowercase_     :					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:
																							lowercase_     :					Optional[Any]													=	[]
																							for sub_s in divide_chunks(seq_    ,							max_len - 2		):
																													if sub_s[0] != cls_id:
																																			lowercase_     :					int													=	np.insert(__SCREAMING_SNAKE_CASE    ,							0    ,							__SCREAMING_SNAKE_CASE		)
																													if sub_s[-1] != sep_id:
																																			lowercase_     :					Optional[int]													=	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]		)

											lowercase_     :					Any													=	np.array(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	np.array(__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					str													=	len(self		)
											lowercase_     :					str													=	self.lengths > 11
											lowercase_     :					List[str]													=	self.token_ids[indices]
											lowercase_     :					List[Any]													=	self.lengths[indices]
											lowercase_     :					str													=	len(self		)
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					str													=	[t[0] for t in batch]
											lowercase_     :					Dict													=	[t[1] for t in batch]
											assert len(__SCREAMING_SNAKE_CASE		) == len(__SCREAMING_SNAKE_CASE		)

											# Max for paddings
											lowercase_     :					Any													=	max(__SCREAMING_SNAKE_CASE		)

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

											lowercase_     :					Any													=	torch.tensor(tk_		)  # (bs, max_seq_len_)
											lowercase_     :					int													=	torch.tensor(__SCREAMING_SNAKE_CASE		)  # (bs)
											return tk_t, lg_t





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						assert column_title.isupper()
						lowercase_     :					Dict													=	0
						lowercase_     :					Tuple													=	len(__SCREAMING_SNAKE_CASE							) - 1
						lowercase_     :					Optional[int]													=	0

						while index >= 0:
												lowercase_     :					Optional[Any]													=	(ord(column_title[index]							) - 64) * pow(26				,				__SCREAMING_SNAKE_CASE							)
												answer += value
												power += 1
												index -= 1

						return answer


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

							testmod()



