

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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							return np.dot(UpperCamelCase__       ,							UpperCamelCase__				)







class 			lowercase    :




				def __init__( self				,       *,
				    snake_case = np.inf				,       snake_case = "linear"				,       snake_case = 0.0				,       ):
											snake_case_          =   regularization
											snake_case_          =   gamma
											if kernel == "linear":
																		snake_case_          =   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'	)
																		snake_case_          =   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:
																		snake_case_          =   F'''Unknown kernel: {kernel}'''
																		raise ValueError(snake_case	)




				def    a							( self				,       snake_case				,       snake_case	):
											return np.dot(snake_case				,       snake_case	)




				def    a							( self				,       snake_case				,       snake_case	):
											return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)




				def    a							( self				,       snake_case				,       snake_case	):
											snake_case_          =   observations
											snake_case_          =   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

											((snake_case_   )    ,      )          =   np.shape(snake_case	)

											def to_minimize(snake_case	) -> float:
																		snake_case_          =   0
																		((snake_case_   )    ,      )          =   np.shape(snake_case	)
																		for i in range(snake_case	):
																									for j in range(snake_case	):
																																s += (
																																    candidate[i]
																																    * candidate[j]
																																    * classes[i]
																																    * classes[j]
																																    * self.kernel(observations[i]				,       observations[j]	)
																																)
																		return 1 / 2 * s - sum(snake_case	)

											snake_case_          =   LinearConstraint(snake_case				,       0				,       0	)
											snake_case_          =   Bounds(0				,       self.regularization	)

											snake_case_          =   minimize(
											    snake_case				,       np.ones(snake_case	)				,       bounds=snake_case				,       constraints=[ly_contraint]	).x
											snake_case_          =   l_star

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




				def    a							( self				,       snake_case	):
											snake_case_          =   sum(
											    self.optimum[n]
											    * self.classes[n]
											    * self.kernel(self.observations[n]				,       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()








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




