



import math


class 	lowerCamelCase__ :


						def __init__( self						:  Tuple      ,       _a						:  int      ,       _a						:  int      ,       _a						:  int      ,       _a						:  List[str]			):
													a__:       Any											=x
													a__:       Any											=y
													a__:       List[str]											=step_size
													a__:       Dict											=function_to_optimize


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													return self.function(self.x      ,       self.y			)


						def   _lowerCamelCase			( self						:  Tuple			):
													a__:       str											=self.step_size
													return [
													    SearchProblem(_a      ,       _a      ,       _a      ,       self.function			)
													    for x, y in (
													        (self.x - step_size, self.y - step_size),
													        (self.x - step_size, self.y),
													        (self.x - step_size, self.y + step_size),
													        (self.x, self.y - step_size),
													        (self.x, self.y + step_size),
													        (self.x + step_size, self.y - step_size),
													        (self.x + step_size, self.y),
													        (self.x + step_size, self.y + step_size),
													    )
													]


						def __hash__( self						:  Tuple			):
													return hash(str(self			)			)


						def __eq__( self						:  Optional[Any]      ,       _a						:  Optional[int]			):
													if isinstance(_a      ,       _a			):
																				return hash(str(self			)			) == hash(str(_a			)			)
													return False







						def __str__( self						:  List[str]			):
													return F"x: {self.x} y: {self.y}"





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  bool = True     , __magic_name__	:  float = math.inf     , __magic_name__	:  float = -math.inf     , __magic_name__	:  float = math.inf     , __magic_name__	:  float = -math.inf     , __magic_name__	:  bool = False     , __magic_name__	:  int = 10_000     , ):
							a__:       Union[str, Any]											=search_prob
							a__:       Optional[int]											=[]  # list to store the current score at each iteration
							a__:       List[str]											=0
							a__:       List[Any]											=False
							a__:       Optional[int]											=set()
							while not solution_found and iterations < max_iter:
														visited.add(__magic_name__						)
														iterations += 1
														a__:       List[Any]											=current_state.score()
														scores.append(__magic_name__						)
														a__:       Optional[Any]											=current_state.get_neighbors()
														a__:       List[Any]											=-math.inf
														a__:       Optional[int]											=math.inf
														a__:       Optional[int]											=None  # to hold the next best neighbor
														for neighbor in neighbors:
																					if neighbor in visited:
																												continue  # do not want to visit the same state again
																					if (
																					    neighbor.x > max_x
																					    or neighbor.x < min_x
																					    or neighbor.y > max_y
																					    or neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds
																					a__:       Tuple											=neighbor.score() - current_score
																					if find_max:  # finding max
																												# going to direction with greatest ascent
																												if change > max_change and change > 0:
																																			a__:       Optional[Any]											=change
																																			a__:       Optional[int]											=neighbor
																					else:  # finding min
																												# to direction with greatest descent
																												if change < min_change and change < 0:
																																			a__:       List[Any]											=change
																																			a__:       Dict											=neighbor
														if next_state is not None:
																					# we found at least one neighbor which improved the current state
																					a__:       Union[str, Any]											=next_state
														else:
																					# since we have no neighbor that improves the solution we stop the search
																					a__:       Tuple											=True

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(__magic_name__						)     , __magic_name__						)
														plt.xlabel("Iterations"						)
														plt.ylabel("Function values"						)
														plt.show()

							return current_state


if __name__ == "__main__":
		import doctest

		doctest.testmod()





		def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Tuple						):
									return (x**2) + (y**2)

		# starting the problem with initial coordinates (3, 4)
		__UpperCAmelCase						  =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__UpperCAmelCase						  =     hill_climbing(prob, find_max=False)
		print(
		    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
		    f"""{local_min.score()}"""
		)

		# starting the problem with initial coordinates (12, 47)
		__UpperCAmelCase						  =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__UpperCAmelCase						  =     hill_climbing(
		    prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
		)





		def 						__lowerCamelCase       (      __magic_name__	:  str     , __magic_name__	:  Optional[int]						):
									return (3 * x**2) - (6 * y)

		__UpperCAmelCase						  =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__UpperCAmelCase						  =     hill_climbing(prob, find_max=True)







		print(
		    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
		    f"""{local_min.score()}"""
		)










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





