


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

from utils import logger




class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											a__      =params

											a__      =np.array(lowercase_)
											a__      =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       ,  lowercase_)  -> List[str]:
											return (self.token_ids[index], self.lengths[index])





				def __len__(				self)  -> Any:
											return len(self.lengths)





				def 						__UpperCamelCase    (				self)  -> Any:
											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)  -> Union[str, Any]:
											a__      =self.params.max_model_input_size
											a__      =self.lengths > max_len
											logger.info(F"""Splitting {sum(lowercase_)} too long sequences.""")

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

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

											a__      =np.array(lowercase_)
											a__      =np.array(lowercase_)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =len(self)
											a__      =self.lengths > 11
											a__      =self.token_ids[indices]
											a__      =self.lengths[indices]
											a__      =len(self)
											logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""")





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





				def 						__UpperCamelCase    (				self)  -> List[str]:
											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       ,  lowercase_)  -> str:
											a__      =[t[0] for t in batch]
											a__      =[t[1] for t in batch]
											assert len(lowercase_) == len(lowercase_)

											# Max for paddings
											a__      =max(lowercase_)

											# Pad token ids
											if self.params.mlm:
																		a__      =self.params.special_tok_ids['pad_token']
											else:
																		a__      =self.params.special_tok_ids['unk_token']
											a__      =[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_)

											a__      =torch.tensor(tk_)  # (bs, max_seq_len_)
											a__      =torch.tensor(lowercase_)  # (bs)
											return tk_t, lg_t





from __future__ import annotations






def 						a__			(				A__,      A__ = None,      A__ = None				):
			if start is None:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0

			if end is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(A__				) - 1

			if start >= end:
						return

			SCREAMING_SNAKE_CASE_		:     Tuple					=					(start + end) // 2

			slowsort(A__,      A__,      A__				)
			slowsort(A__,      mid + 1,      A__				)

			if sequence[end] < sequence[mid]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sequence[mid], sequence[end]

			slowsort(A__,      A__,      end - 1				)


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

				testmod()





