



"""simple docstring"""


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_    					=	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_      (					UpperCamelCase_    ):



										"""simple docstring"""






										def __init__( self			:int       ,  lowercase_			:Optional[Any] = 1_01 )    ->      Optional[int]:
																UpperCAmelCase					=			length






										def __len__( self			:Union[str, Any] )    ->      Union[str, Any]:
																return self.length






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







				class 				A_      :



										"""simple docstring"""






										def __call__( self			:str       ,  lowercase_			:Optional[Any] )    ->      int:
																return {"input_ids": torch.tensor(_a ), "labels": torch.tensor(_a )}







				class 				A_      (					nn.Module    ):



										"""simple docstring"""






										def __init__( self			:Tuple )    ->      Tuple:
																super().__init__()
																# Add some (unused) params otherwise DDP will complain.
																UpperCAmelCase					=			nn.Linear(1_20       ,  80 )






										def      UpperCAmelCase__					( self			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Tuple=None )    ->      Union[str, Any]:


																if labels is not None:
																						return torch.tensor(0.0       ,  device=input_ids.device ), input_ids
																else:
																						return input_ids







class 				A_      (					UpperCamelCase_    ):



						"""simple docstring"""






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







class 				A_      (					UpperCamelCase_    ):



						"""simple docstring"""






						@require_torch_multi_gpu
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
												UpperCAmelCase					=			self.get_auto_remove_tmp_dir()
												UpperCAmelCase					=			f"""--output_dir {output_dir}""".split()
												UpperCAmelCase					=			["""torchrun"""] + distributed_args + args
												execute_subprocess_async(_a       ,  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_    					=	HfArgumentParser((TrainingArguments,))
				snake_case_    					=	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 [101, 40, 7]:
								snake_case_    					=	DummyDataset(dataset_length)

								def 			_lowerCAmelCase				(       lowercase_						):
														UpperCAmelCase					=			list(range(len(__lowerCAmelCase						)						)						)
														UpperCAmelCase					=			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_    					=	Trainer(
								    model=DummyModel(),
								    args=training_args,
								    data_collator=DummyDataCollator(),
								    eval_dataset=dataset,
								    compute_metrics=compute_metrics,
								)
								snake_case_    					=	trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

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

								snake_case_    					=	2

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

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






								snake_case_    					=	None









"""simple docstring"""


from math import factorial, radians

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = 18		,     lowercase_ = 10						):
						UpperCAmelCase					=			angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

						# Converting from degrees to radians
						UpperCAmelCase					=			radians(lowercase_						)

						UpperCAmelCase					=			angle_in_radians
						UpperCAmelCase					=			3
						UpperCAmelCase					=			-1

						for _ in range(lowercase_						):
												result += (b * (angle_in_radians**a)) / factorial(lowercase_						)

												UpperCAmelCase					=			-b  # One positive term and the next will be negative and so on...
												a += 2  # Increased by 2 for every term.

						return round(lowercase_		,     lowercase_						)


if __name__ == "__main__":
				__import__("""doctest""").testmod()





