






"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
			from transformers.trainer import DEFAULT_CALLBACKS

			from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel







class     UpperCamelCase_ (lowercase__  ):





	def __init__(   self      :				List[Any]		)		->    int:
				UpperCAmelCase_  :				Optional[int]      =      []





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				str			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Dict:
				self.events.append("on_init_end"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				List[str]		)		->    List[str]:
				self.events.append("on_train_begin"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				List[Any]		)		->    int:
				self.events.append("on_train_end"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Union[str, Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Union[str, Any]:
				self.events.append("on_epoch_begin"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				List[str]			,			**lowerCAmelCase_      :				Tuple		)		->    Union[str, Any]:
				self.events.append("on_epoch_end"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				Any		)		->    List[Any]:
				self.events.append("on_step_begin"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				Optional[int]		)		->    Optional[int]:
				self.events.append("on_step_end"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Dict:
				self.events.append("on_evaluate"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				str			,			**lowerCAmelCase_      :				List[Any]		)		->    List[Any]:
				self.events.append("on_predict"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				List[str]			,			**lowerCAmelCase_      :				Tuple		)		->    List[str]:
				self.events.append("on_save"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Union[str, Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Optional[Any]:
				self.events.append("on_log"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Optional[Any]:
				self.events.append("on_prediction_step"		)







@require_torch
class     UpperCamelCase_ (unittest.TestCase  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      tempfile.mkdtemp()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[int]:
				shutil.rmtree(self.output_dir		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				int=0			,			lowerCAmelCase_      :				Optional[Any]=0			,			lowerCAmelCase_      :				Union[str, Any]=64			,			lowerCAmelCase_      :				int=64			,			lowerCAmelCase_      :				str=None			,			lowerCAmelCase_      :				Union[str, Any]=False			,			**lowerCAmelCase_      :				Tuple		)		->    str:
				# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
				# its set to False since the tests later on depend on its value.
				UpperCAmelCase_  :				Dict      =      RegressionDataset(length=__lowercase		)
				UpperCAmelCase_  :				Tuple      =      RegressionDataset(length=__lowercase		)
				UpperCAmelCase_  :				Tuple      =      RegressionModelConfig(a=__lowercase			,			b=__lowercase		)
				UpperCAmelCase_  :				Any      =      RegressionPreTrainedModel(__lowercase		)

				UpperCAmelCase_  :				Optional[Any]      =      TrainingArguments(self.output_dir			,			disable_tqdm=__lowercase			,			report_to=[]			,			**__lowercase		)
				return Trainer(
				    __lowercase			,			__lowercase			,			train_dataset=__lowercase			,			eval_dataset=__lowercase			,			callbacks=__lowercase			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Any		)		->    List[Any]:
				self.assertEqual(len(__lowercase		)			,			len(__lowercase		)		)

				# Order doesn't matter
				UpperCAmelCase_  :				str      =      sorted(__lowercase			,			key=lambda lowerCAmelCase_		: cb.__name__ if isinstance(__lowercase			,			__lowercase		) else cb.__class__.__name__		)
				UpperCAmelCase_  :				Any      =      sorted(__lowercase			,			key=lambda lowerCAmelCase_		: cb.__name__ if isinstance(__lowercase			,			__lowercase		) else cb.__class__.__name__		)

				for cba, cba in zip(__lowercase			,			__lowercase		):
							if isinstance(__lowercase			,			__lowercase		) and isinstance(__lowercase			,			__lowercase		):
										self.assertEqual(__lowercase			,			__lowercase		)
							elif isinstance(__lowercase			,			__lowercase		) and not isinstance(__lowercase			,			__lowercase		):
										self.assertEqual(__lowercase			,			cba.__class__		)
							elif not isinstance(__lowercase			,			__lowercase		) and isinstance(__lowercase			,			__lowercase		):
										self.assertEqual(cba.__class__			,			__lowercase		)
							else:
										self.assertEqual(__lowercase			,			__lowercase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				List[Any]      =      ["on_init_end", "on_train_begin"]
				UpperCAmelCase_  :				List[str]      =      0
				UpperCAmelCase_  :				Any      =      len(trainer.get_eval_dataloader()		)
				UpperCAmelCase_  :				str      =      ["on_prediction_step"] * len(trainer.get_eval_dataloader()		) + ["on_log", "on_evaluate"]
				for _ in range(trainer.state.num_train_epochs		):
							expected_events.append("on_epoch_begin"		)
							for _ in range(__lowercase		):
										step += 1
										expected_events += ["on_step_begin", "on_step_end"]
										if step % trainer.args.logging_steps == 0:
													expected_events.append("on_log"		)
										if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
													expected_events += evaluation_events.copy()
										if step % trainer.args.save_steps == 0:
													expected_events.append("on_save"		)
							expected_events.append("on_epoch_end"		)
							if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
										expected_events += evaluation_events.copy()
				expected_events += ["on_log", "on_train_end"]
				return expected_events





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				int      =      self.get_trainer()
				UpperCAmelCase_  :				Any      =      DEFAULT_CALLBACKS.copy() + [ProgressCallback]
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				# Callbacks passed at init are added to the default callbacks
				UpperCAmelCase_  :				Optional[Any]      =      self.get_trainer(callbacks=[MyTestTrainerCallback]		)
				expected_callbacks.append(__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
				UpperCAmelCase_  :				List[Any]      =      self.get_trainer(disable_tqdm=__lowercase		)
				UpperCAmelCase_  :				Any      =      DEFAULT_CALLBACKS.copy() + [PrinterCallback]
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				Any      =      DEFAULT_CALLBACKS.copy() + [ProgressCallback]
				UpperCAmelCase_  :				int      =      self.get_trainer()

				# We can add, pop, or remove by class name
				trainer.remove_callback(__lowercase		)
				expected_callbacks.remove(__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				UpperCAmelCase_  :				Optional[int]      =      self.get_trainer()
				UpperCAmelCase_  :				Optional[int]      =      trainer.pop_callback(__lowercase		)
				self.assertEqual(cb.__class__			,			__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				trainer.add_callback(__lowercase		)
				expected_callbacks.insert(0			,			__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				# We can also add, pop, or remove by instance
				UpperCAmelCase_  :				List[str]      =      self.get_trainer()
				UpperCAmelCase_  :				List[Any]      =      trainer.callback_handler.callbacks[0]
				trainer.remove_callback(__lowercase		)
				expected_callbacks.remove(__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				UpperCAmelCase_  :				Any      =      self.get_trainer()
				UpperCAmelCase_  :				List[str]      =      trainer.callback_handler.callbacks[0]
				UpperCAmelCase_  :				Union[str, Any]      =      trainer.pop_callback(__lowercase		)
				self.assertEqual(__lowercase			,			__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)

				trainer.add_callback(__lowercase		)
				expected_callbacks.insert(0			,			__lowercase		)
				self.check_callbacks_equality(trainer.callback_handler.callbacks			,			__lowercase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				import warnings

				# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
				warnings.simplefilter(action="ignore"			,			category=__lowercase		)

				UpperCAmelCase_  :				Optional[int]      =      self.get_trainer(callbacks=[MyTestTrainerCallback]		)
				trainer.train()
				UpperCAmelCase_  :				Optional[Any]      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				# Independent log/save/eval
				UpperCAmelCase_  :				int      =      self.get_trainer(callbacks=[MyTestTrainerCallback]			,			logging_steps=5		)
				trainer.train()
				UpperCAmelCase_  :				Union[str, Any]      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				UpperCAmelCase_  :				List[Any]      =      self.get_trainer(callbacks=[MyTestTrainerCallback]			,			save_steps=5		)
				trainer.train()
				UpperCAmelCase_  :				int      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				UpperCAmelCase_  :				List[str]      =      self.get_trainer(callbacks=[MyTestTrainerCallback]			,			eval_steps=5			,			evaluation_strategy="steps"		)
				trainer.train()
				UpperCAmelCase_  :				int      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				UpperCAmelCase_  :				Dict      =      self.get_trainer(callbacks=[MyTestTrainerCallback]			,			evaluation_strategy="epoch"		)
				trainer.train()
				UpperCAmelCase_  :				Tuple      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				# A bit of everything
				UpperCAmelCase_  :				List[Any]      =      self.get_trainer(
				    callbacks=[MyTestTrainerCallback]			,			logging_steps=3			,			save_steps=10			,			eval_steps=5			,			evaluation_strategy="steps"			,			)
				trainer.train()
				UpperCAmelCase_  :				List[str]      =      trainer.callback_handler.callbacks[-2].events
				self.assertEqual(__lowercase			,			self.get_expected_events(__lowercase		)		)

				# warning should be emitted for duplicated callbacks
				with patch("transformers.trainer_callback.logger.warning"		) as warn_mock:
							UpperCAmelCase_  :				Tuple      =      self.get_trainer(
							    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]			,			)
							assert str(__lowercase		) in warn_mock.call_args[0][0]











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

def        snake_case   (     A__		):
			return np.dot(A__			,A__		)







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			*,
	    lowerCAmelCase_      :				float = np.inf			,			lowerCAmelCase_      :				str = "linear"			,			lowerCAmelCase_      :				float = 0.0			,			)		->    None:
				UpperCAmelCase_  :				List[str]      =      regularization
				UpperCAmelCase_  :				Tuple      =      gamma
				if kernel == "linear":
							UpperCAmelCase_  :				List[Any]      =      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_  :				Tuple      =      f"""Unknown kernel: {kernel}"""
							raise ValueError(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.dot(lowerCAmelCase_			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				ndarray			,			lowerCAmelCase_      :				ndarray		)		->    float:
				return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				list[ndarray]			,			lowerCAmelCase_      :				ndarray		)		->    None:
				UpperCAmelCase_  :				int      =      observations
				UpperCAmelCase_  :				Optional[Any]      =      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[int]      =      np.shape(lowerCAmelCase_		)

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

				UpperCAmelCase_  :				int      =      LinearConstraint(lowerCAmelCase_			,			0			,			0		)
				UpperCAmelCase_  :				Dict      =      Bounds(0			,			self.regularization		)

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

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





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


if __name__ == "__main__":
			import doctest

			doctest.testmod()



