



# 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 				a_    (						_snake_case							):

													def __init__(       self      :Union[str, Any]  ,		_lowercase      :int = 101)						->   Tuple:
																			UpperCAmelCase_					=	length

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




													def __getitem__(       self      :Union[str, Any]  ,		_lowercase      :List[str])						->   int:
																			return i







							class 				a_    :

													def __call__(       self      :Optional[Any]  ,		_lowercase      :Dict)						->   List[str]:
																			return {"input_ids": torch.tensor(_lowercase), "labels": torch.tensor(_lowercase)}







							class 				a_    (						nn.Module							):

													def __init__(       self      :Tuple)						->   str:
																			super().__init__()
																			# Add some (unused) params otherwise DDP will complain.
																			UpperCAmelCase_					=	nn.Linear(120  ,		80)




													def       __a			(       self      :Optional[Any]  ,		_lowercase      :str  ,		_lowercase      :Optional[int]=None)						->   str:
																			if labels is not None:
																									return torch.tensor(0.0  ,		device=input_ids.device), input_ids
																			else:
																									return input_ids







def 							A     (						)			->			Optional[int]:



						'''simple docstring'''






						UpperCAmelCase_					=	HfArgumentParser((TrainingArguments,)      )
						sys.argv += ["--output_dir", "./examples"]
						UpperCAmelCase_					=	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 [1001, 256, 15]:
												UpperCAmelCase_					=	DummyDataset(__UpperCAmelCase      )

												def compute_metrics(__UpperCAmelCase      ) -> Dict:
																		UpperCAmelCase_					=	list(range(len(__UpperCAmelCase      )      )      )
																		UpperCAmelCase_					=	p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																		return {"success": success}

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

												UpperCAmelCase_					=	trainer.predict(__UpperCAmelCase      )
												logger.info(p.metrics      )
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics      )
																		exit(1      )

												UpperCAmelCase_					=	2

												UpperCAmelCase_					=	trainer.evaluate()
												logger.info(__UpperCAmelCase      )
												if metrics["eval_success"] is not True:
																		logger.error(__UpperCAmelCase      )
																		exit(1      )

												UpperCAmelCase_					=	trainer.predict(__UpperCAmelCase      )
												logger.info(p.metrics      )
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics      )
																		exit(1      )

												UpperCAmelCase_					=	None

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







def 							A     (						__UpperCAmelCase      )			->			Union[str, Any]:



						'''simple docstring'''






						main()


if __name__ == "__main__":
							main()










import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






						if number_of_simulations < 1:
												raise ValueError('''At least one simulation is necessary to estimate PI.'''      )

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	Point.random_unit_square()

												if random_point.is_in_unit_circle():
																		number_in_unit_circle += 1

						return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							UpperCamelCase_      			=  "Please enter the desired number of Monte Carlo simulations: "
							UpperCamelCase_      			=  estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")





