



'''simple docstring'''



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

from utils import logger
class 	lowerCamelCase_							(					snake_case_			):







						def __init__(  self   :      Union[str, Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			params

													SCREAMING_SNAKE_CASE					:      List[str]     					=			np.array(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			np.array([len(lowerCAmelCase__				) 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				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													return (self.token_ids[index], self.lengths[index])







						def __len__(  self   :      List[str]				):

													"""simple docstring"""
													return len(self.lengths				)







						def 					__lowercase    (  self   :      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   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.params.max_model_input_size
													SCREAMING_SNAKE_CASE					:      str     					=			self.lengths > max_len
													logger.info(F"""Splitting {sum(lowerCAmelCase__				)} too long sequences."""				)

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

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

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

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			np.array(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			np.array(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Any				):

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







						def 					__lowercase    (  self   :      Union[str, Any]				):

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







						def 					__lowercase    (  self   :      Dict				):

													"""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   :      List[str]				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[t[0] for t in batch]
													SCREAMING_SNAKE_CASE					:      Dict     					=			[t[1] for t in batch]
													assert len(lowerCAmelCase__				) == len(lowerCAmelCase__				)

													# Max for paddings
													SCREAMING_SNAKE_CASE					:      Tuple     					=			max(lowerCAmelCase__				)

													# Pad token ids
													if self.params.mlm:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			self.params.special_tok_ids['''pad_token''']
													else:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.params.special_tok_ids['''unk_token''']
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[list(t.astype(lowerCAmelCase__				)				) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__				)) for t in token_ids]
													assert len(tk_				) == len(lowerCAmelCase__				)
													assert all(len(lowerCAmelCase__				) == max_seq_len_ for t in tk_				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			torch.tensor(tk_				)  # (bs, max_seq_len_)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			torch.tensor(lowerCAmelCase__				)  # (bs)
													return tk_t, lg_t









'''simple docstring'''



from bisect import bisect
from itertools import accumulate
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Optional[Any]					,		A  :				Dict					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			sorted(zip(A					,		A				)					,		key=lambda A				: x[0] / x[1]					,		reverse=A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			[i[0] for i in r], [i[1] for i in r]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			list(accumulate(A				)				)
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			bisect(A					,		A				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




