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

from utils import logger







class        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def __init__(       self     ,A__     ,A__):
												lowercase					=		params

												lowercase					=		np.array(A__)
												lowercase					=		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):
												lowercase					=		self.params.max_model_input_size
												lowercase					=		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__)]

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

												lowercase					=		np.array(A__)
												lowercase					=		np.array(A__)




							def 				A__   (       self):
												lowercase					=		len(self)
												lowercase					=		self.lengths > 1_1
												lowercase					=		self.token_ids[indices]
												lowercase					=		self.lengths[indices]
												lowercase					=		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:
																	lowercase					=		self.params.special_tok_ids['''unk_token''']
												lowercase					=		len(self)
												lowercase					=		np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
												lowercase					=		(unk_occs / self.lengths) < 0.5
												lowercase					=		self.token_ids[indices]
												lowercase					=		self.lengths[indices]
												lowercase					=		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__):
												lowercase					=		[t[0] for t in batch]
												lowercase					=		[t[1] for t in batch]
												assert len(A__) == len(A__)

												# Max for paddings
												lowercase					=		max(A__)

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

												lowercase					=		torch.tensor(tk_)  # (bs, max_seq_len_)
												lowercase					=		torch.tensor(A__)  # (bs)
												return tk_t, lg_t






from numpy import exp, pi, sqrt



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0	):
					'''simple docstring'''
					return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




