






'''simple docstring'''




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

from utils import logger







class       __lowercase					(__lowerCamelCase    ):

	def __init__(      self   :						Dict  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						List[Any]):
			UpperCamelCase__				:					Tuple					=					params

			UpperCamelCase__				:					str					=					np.array(UpperCAmelCase_)
			UpperCamelCase__				:					Tuple					=					np.array([len(UpperCAmelCase_) 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   :						int  ,					UpperCAmelCase_   :						List[Any]):
			return (self.token_ids[index], self.lengths[index])

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

	def      __UpperCamelCase							(      self   :						Union[str, 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   :						List[Any]):
			UpperCamelCase__				:					str					=					self.params.max_model_input_size
			UpperCamelCase__				:					List[Any]					=					self.lengths > max_len
			logger.info(F'Splitting {sum(UpperCAmelCase_)} too long sequences.')

			def divide_chunks(UpperCAmelCase_   :						Dict  ,					UpperCAmelCase_   :						Optional[int]):
					return [l[i : i + n] for i in range(0  ,					len(UpperCAmelCase_)  ,					UpperCAmelCase_)]

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

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

			UpperCamelCase__				:					List[Any]					=					np.array(UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					np.array(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Tuple					=					len(self)
			UpperCamelCase__				:					Union[str, Any]					=					self.lengths > 11
			UpperCamelCase__				:					List[Any]					=					self.token_ids[indices]
			UpperCamelCase__				:					str					=					self.lengths[indices]
			UpperCamelCase__				:					Optional[int]					=					len(self)
			logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.')

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

	def      __UpperCamelCase							(      self   :						Any):
			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   :						str  ,					UpperCAmelCase_   :						Dict):
			UpperCamelCase__				:					Optional[Any]					=					[t[0] for t in batch]
			UpperCamelCase__				:					Optional[int]					=					[t[1] for t in batch]
			assert len(UpperCAmelCase_) == len(UpperCAmelCase_)

			# Max for paddings
			UpperCamelCase__				:					List[str]					=					max(UpperCAmelCase_)

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

			UpperCamelCase__				:					List[str]					=					torch.tensor(tk_)  # (bs, max_seq_len_)
			UpperCamelCase__				:					Union[str, Any]					=					torch.tensor(UpperCAmelCase_)  # (bs)
			return tk_t, lg_t












'''simple docstring'''




import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize







def       __UpperCAmelCase						(  lowerCamelCase_)							->     float:
		return np.dot(lowerCamelCase_     ,      lowerCamelCase_)







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					*,
	    UpperCAmelCase_   :						float = np.inf  ,					UpperCAmelCase_   :						str = "linear"  ,					UpperCAmelCase_   :						float = 0.0  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					regularization
			UpperCamelCase__				:					Optional[int]					=					gamma
			if kernel == "linear":
					UpperCamelCase__				:					List[str]					=					self.__linear
			elif kernel == "rbf":
					if self.gamma == 0:
							raise ValueError('rbf kernel requires gamma')
					if not isinstance(self.gamma  ,					(float, int)):
							raise ValueError('gamma must be float or int')
					if not self.gamma > 0:
							raise ValueError('gamma must be > 0')
					UpperCamelCase__				:					Union[str, Any]					=					self.__rbf
					# in the future, there could be a default value like in sklearn
					# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
					# previously it was 1/(n_features)
			else:
					UpperCamelCase__				:					Optional[int]					=					F'Unknown kernel: {kernel}'
					raise ValueError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.dot(UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						ndarray  ,					UpperCAmelCase_   :						ndarray):
			return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						list[ndarray]  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Any					=					observations
			UpperCamelCase__				:					Tuple					=					classes

			# using Wolfe's Dual to calculate w.
			# Primal problem: minimize 1/2*norm_squared(w)
			#   constraint: yn(w . xn + b) >= 1
			#
			# With l a vector
			# Dual problem: maximize sum_n(ln) -
			#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
			#   constraint: self.C >= ln >= 0
			#           and sum_n(ln*yn) = 0
			# Then we get w using w = sum_n(ln*yn*xn)
			# At the end we can get b ~= mean(yn - w . xn)
			#
			# Since we use kernels, we only need l_star to calculate b
			# and to classify observations

			((UpperCamelCase__),	)				:					Optional[Any]					=					np.shape(UpperCAmelCase_)

			def to_minimize(UpperCAmelCase_   :						ndarray) -> float:
					UpperCamelCase__				:					Union[str, Any]					=					0
					((UpperCamelCase__),	)				:					int					=					np.shape(UpperCAmelCase_)
					for i in range(UpperCAmelCase_):
							for j in range(UpperCAmelCase_):
									s += (
									    candidate[i]
									    * candidate[j]
									    * classes[i]
									    * classes[j]
									    * self.kernel(observations[i]  ,					observations[j])
									)
					return 1 / 2 * s - sum(UpperCAmelCase_)

			UpperCamelCase__				:					List[str]					=					LinearConstraint(UpperCAmelCase_  ,					0  ,					0)
			UpperCamelCase__				:					Dict					=					Bounds(0  ,					self.regularization)

			UpperCamelCase__				:					Any					=					minimize(
			    UpperCAmelCase_  ,					np.ones(UpperCAmelCase_)  ,					bounds=UpperCAmelCase_  ,					constraints=[ly_contraint]).x
			UpperCamelCase__				:					str					=					l_star

			# calculating mean offset of separation plane to points
			UpperCamelCase__				:					Any					=					0
			for i in range(UpperCAmelCase_):
					for j in range(UpperCAmelCase_):
							s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
							    observations[i]  ,					observations[j])
			UpperCamelCase__				:					List[str]					=					s / n





	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						ndarray):
			UpperCamelCase__				:					Optional[int]					=					sum(
			    self.optimum[n]
			    * self.classes[n]
			    * self.kernel(self.observations[n]  ,					UpperCAmelCase_)
			    for n in range(len(self.classes)))
			return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()




