
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


_snake_case      :							List[Any]     	=    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   UpperCamelCase_      (      __a		):







												'''simple docstring'''



												def __init__(			self							:Any		,    lowerCAmelCase__							:int = 101     )  ->Tuple:
																lowercase         =      length





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





												def __getitem__(			self							:Optional[int]		,    lowerCAmelCase__							:Any     )  ->int:
																return i





						class   UpperCamelCase_      :







												'''simple docstring'''



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





						class   UpperCamelCase_      (      nn.Module		):







												'''simple docstring'''



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





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





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@require_torch_neuroncore
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->int:
										lowercase         =      F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
										lowercase         =      self.get_auto_remove_tmp_dir()
										lowercase         =      F'''--output_dir {output_dir}'''.split()
										lowercase         =      ["torchrun"] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__		,    env=self.get_env()     )
										# successful return here == success - any errors would have caused an error in the sub-call





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@require_torch_multi_gpu
						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Union[str, Any]:
										lowercase         =      F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
										lowercase         =      self.get_auto_remove_tmp_dir()
										lowercase         =      F'''--output_dir {output_dir}'''.split()
										lowercase         =      ["torchrun"] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__		,    env=self.get_env()     )
										# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
						# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
						#
						# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

						_snake_case      :							List[str]     	=    HfArgumentParser((TrainingArguments,))
						_snake_case      :							Tuple     	=    parser.parse_args_into_dataclasses()[0]

						logger.warning(
						    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
						    F"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
						)

						# 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_01, 40, 7]:
												_snake_case      :							Any     	=    DummyDataset(dataset_length)

												def  __snake_case							(	__magic_name__						):







																'''simple docstring'''


																lowercase         =      list(range(len(__magic_name__						)						)						)
																lowercase         =      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																if not success and training_args.local_rank == 0:
																				logger.warning(
																				    "Predictions and/or labels do not match expected results:\n  - predictions: "
																				    F'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''						)
																return {"success": success}

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

												_snake_case      :							Optional[int]     	=    trainer.predict(dataset)
												logger.info(p.metrics)
												if p.metrics["test_success"] is not True:
																		logger.error(p.metrics)
																		exit(1)

												_snake_case      :							Dict     	=    2

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

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




												_snake_case      :							int     	=    None


def  __snake_case							(	__magic_name__		,    __magic_name__ = False						):







				'''simple docstring'''


				if n == 2:
								return True
				if not n % 2 or n < 2:
								return False
				if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
								return False
				if n > 3317044064679887385961981 and not allow_probable:
								raise ValueError(
								    "Warning: upper bound of deterministic test is exceeded. "
								    "Pass allow_probable=True to allow probabilistic test. "
								    "A return value of True indicates a probable prime."						)
				# array bounds provided by analysis
				lowercase         =      [
				    2047,
				    1373653,
				    25326001,
				    3215031751,
				    2152302898747,
				    3474749660383,
				    341550071728321,
				    1,
				    3825123056546413051,
				    1,
				    1,
				    318665857834031151167461,
				    3317044064679887385961981,
				]

				lowercase         =      [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
				for idx, _p in enumerate(__magic_name__		,    1						):
								if n < _p:
												# then we have our last prime to check
												lowercase         =      primes[:idx]
												break
				lowercase     ,	lowercase         =      n - 1, 0
				# break up n -1 into a power of 2 (s) and
				# remaining odd component
				# essentially, solve for d * 2 ** s == n - 1
				while d % 2 == 0:
								d //= 2
								s += 1
				for prime in plist:
								lowercase         =      False
								for r in range(__magic_name__						):
												lowercase         =      pow(__magic_name__		,    d * 2**r		,    __magic_name__						)
												# see article for analysis explanation for m
												if (r == 0 and m == 1) or ((m + 1) % n == 0):
																lowercase         =      True
																# this loop will not determine compositeness
																break
								if pr:
												continue
								# if pr is False, then the above loop never evaluated to true,
								# and the n MUST be composite
								return False
				return True

def  __snake_case							(	):







				'''simple docstring'''


				assert not miller_rabin(561						)
				assert miller_rabin(563						)
				# 2047

				assert not miller_rabin(838201						)
				assert miller_rabin(838207						)
				# 1_373_653

				assert not miller_rabin(17316001						)
				assert miller_rabin(17316017						)
				# 25_326_001

				assert not miller_rabin(3078386641						)
				assert miller_rabin(3078386653						)
				# 3_215_031_751

				assert not miller_rabin(1713045574801						)
				assert miller_rabin(1713045574819						)
				# 2_152_302_898_747

				assert not miller_rabin(2779799728307						)
				assert miller_rabin(2779799728327						)
				# 3_474_749_660_383

				assert not miller_rabin(113850023909441						)
				assert miller_rabin(113850023909527						)
				# 341_550_071_728_321

				assert not miller_rabin(1275041018848804351						)
				assert miller_rabin(1275041018848804391						)
				# 3_825_123_056_546_413_051

				assert not miller_rabin(79666464458507787791867						)
				assert miller_rabin(79666464458507787791951						)
				# 318_665_857_834_031_151_167_461

				assert not miller_rabin(552840677446647897660333						)
				assert miller_rabin(552840677446647897660359						)
				# 3_317_044_064_679_887_385_961_981
				# upper limit for probabilistic test


if __name__ == "__main__":
						test_miller_rabin()
