



"""simple docstring"""






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


def 			_lowercase				(  __snake_case	)						->   float:
			return np.dot(__snake_case					,__snake_case	)



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Dict       ,			*,
	    _SCREAMING_SNAKE_CASE:		float = np.inf       ,			_SCREAMING_SNAKE_CASE:		str = "linear"       ,			_SCREAMING_SNAKE_CASE:		float = 0.0       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       regularization
				__lowerCAmelCase      :  str												=       gamma
				if kernel == "linear":
							__lowerCAmelCase      :  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")
							__lowerCAmelCase      :  Dict												=       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:
							__lowerCAmelCase      :  List[Any]												=       F"""Unknown kernel: {kernel}"""
							raise ValueError(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		ndarray       ,			_SCREAMING_SNAKE_CASE:		ndarray)				->       float:

				"""simple docstring"""

				return np.dot(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		ndarray       ,			_SCREAMING_SNAKE_CASE:		ndarray)				->       float:

				"""simple docstring"""

				return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		list[ndarray]       ,			_SCREAMING_SNAKE_CASE:		ndarray)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       observations
				__lowerCAmelCase      :  List[str]												=       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

				((__lowerCAmelCase)						,	)      :  Dict												=       np.shape(_SCREAMING_SNAKE_CASE)

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

				__lowerCAmelCase      :  Tuple												=       LinearConstraint(_SCREAMING_SNAKE_CASE       ,			0       ,			0)
				__lowerCAmelCase      :  List[Any]												=       Bounds(0       ,			self.regularization)

				__lowerCAmelCase      :  Optional[int]												=       minimize(
				    _SCREAMING_SNAKE_CASE       ,			np.ones(_SCREAMING_SNAKE_CASE)       ,			bounds=_SCREAMING_SNAKE_CASE       ,			constraints=[ly_contraint]).x
				__lowerCAmelCase      :  str												=       l_star

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		ndarray)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       sum(
				    self.optimum[n]
				    * self.classes[n]
				    * self.kernel(self.observations[n]       ,			_SCREAMING_SNAKE_CASE)
				    for n in range(len(self.classes)))
				return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)