
'''simple docstring'''







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

from utils import logger


class 		__UpperCAmelCase   (      _UpperCamelCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	params

								_snake_case     		=	np.array(lowerCAmelCase_						)
								_snake_case     		=	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					,     lowerCAmelCase_						):





								"""simple docstring"""






								return (self.token_ids[index], self.lengths[index])


							def __len__( self						):





								"""simple docstring"""






								return len(self.lengths						)


							def 				lowerCamelCase       ( 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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.params.max_model_input_size
								_snake_case     		=	self.lengths > max_len
								logger.info(F'Splitting {sum(lowerCAmelCase_						)} too long sequences.'						)

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

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

								_snake_case     		=	np.array(lowerCAmelCase_						)
								_snake_case     		=	np.array(lowerCAmelCase_						)


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( 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 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	[t[0] for t in batch]
								_snake_case     		=	[t[1] for t in batch]
								assert len(lowerCAmelCase_						) == len(lowerCAmelCase_						)

								# Max for paddings
								_snake_case     		=	max(lowerCAmelCase_						)

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

								_snake_case     		=	torch.tensor(tk_						)  # (bs, max_seq_len_)
								_snake_case     		=	torch.tensor(lowerCAmelCase_						)  # (bs)
								return tk_t, lg_t



'''simple docstring'''







from typing import Any

import numpy as np







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bool:
	return np.array_equal(__A    ,       matrix.conjugate().T			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Any:
	_snake_case     		=	v.conjugate().T
	_snake_case     		=	v_star.dot(__A			)
	assert isinstance(__A    ,       np.ndarray			)
	return (v_star_dot.dot(__A			)) / (v_star.dot(__A			))







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case     		=	np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]			)
	_snake_case     		=	np.array([[1], [2], [3]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	print(rayleigh_quotient(__A    ,       __A			)			)

	_snake_case     		=	np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	assert rayleigh_quotient(__A    ,       __A			) == float(3			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					tests()

