



import sys
from typing import Dict

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


__magic_name__       =     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__       (       a__       ):


							"""simple docstring"""



							def __init__(		self     :int      ,  lowercase__     :int = 101    ):
												lowercase						   =       length

							def __len__(		self     :Any    ):
												return self.length



							def __getitem__(		self     :str      ,  lowercase__     :List[Any]    ):
												return i




		class 	a__       :


							"""simple docstring"""



							def __call__(		self     :str      ,  lowercase__     :str    ):
												return {"input_ids": torch.tensor(_lowerCamelCase    ), "labels": torch.tensor(_lowerCamelCase    )}




		class 	a__       (       nn.Module       ):


							"""simple docstring"""



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



							def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :int      ,  lowercase__     :Any=None    ):







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





def 		__snake_case						(    ):
					"""simple docstring"""






					lowercase						   =       HfArgumentParser((TrainingArguments,)				)
					sys.argv += ["--output_dir", "./examples"]
					lowercase						   =       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 [10_01, 2_56, 15]:
										lowercase						   =       DummyDataset(lowerCamelCase__				)

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

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

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

										lowercase						   =       2

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

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

										lowercase						   =       None

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





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					main()


if __name__ == "__main__":
		main()












'''simple docstring'''




from __future__ import annotations

from statistics import mean




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  Dict			=				2
					A_			:  List[str]			=				list(lowerCamelCase__      )
					A_			:  int			=				[0] * len(lowerCamelCase__      )
					A_			:  int			=				0
					while True:
										A_			:  List[str]			=				True
										for i, burst_time in enumerate(lowerCamelCase__      ):
															if rem_burst_times[i] > 0:
																				A_			:  Any			=				False
																				if rem_burst_times[i] > quantum:
																									t += quantum
																									rem_burst_times[i] -= quantum
																				else:
																									t += rem_burst_times[i]
																									A_			:  Any			=				t - burst_time
																									A_			:  Optional[int]			=				0
										if done is True:
															return waiting_times




def     snake_case__     (			lowerCamelCase__					: list[int]      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					return [burst + waiting for burst, waiting in zip(lowerCamelCase__      ,		lowerCamelCase__      )]


if __name__ == "__main__":
							snake_case__ 							=    [3, 5, 7]
							snake_case__ 							=    calculate_waiting_times(burst_times)
							snake_case__ 							=    calculate_turn_around_times(burst_times, waiting_times)
							print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
							for i, burst_time in enumerate(burst_times):
														print(
														    F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
														    F'{turn_around_times[i]}'
														)
							print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
							print(F'Average turn around time = {mean(turn_around_times):.5f}')



