'''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


__lowerCAmelCase	:       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 						A   (   UpperCAmelCase   ):





									def __init__(      self				:							Optional[Any]     ,		__a				:							int = 1_0_1	)							->	str:
										__UpperCAmelCase							=			length





									def __len__(      self				:							Tuple	)							->	List[str]:
										return self.length






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






		class 						A   :





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






		class 						A   (   nn.Module   ):





									def __init__(      self				:							str	)							->	Any:
										super().__init__()
										# Add some (unused) params otherwise DDP will complain.
										__UpperCAmelCase							=			nn.Linear(1_2_0     ,		8_0	)






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






class 						A   (   UpperCAmelCase   ):





							@require_torch_neuroncore
							def       snake_case__						(      self				:							Optional[Any]	)							->	Tuple:
								__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   ):





							@require_torch_multi_gpu
							def       snake_case__						(      self				:							int	)							->	Any:
								__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

		__lowerCAmelCase	:       Optional[Any]						       =	HfArgumentParser((TrainingArguments,))
		__lowerCAmelCase	:       Dict						       =	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]:
				__lowerCAmelCase	:       List[Any]						       =	DummyDataset(dataset_length)





				def       lowerCAmelCase					(		UpperCamelCase__				:   EvalPrediction       ):


					"""simple docstring"""
					__UpperCAmelCase							=			list(range(len(UpperCamelCase__       )       )       )
					__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}

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

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

				__lowerCAmelCase	:       Union[str, Any]						       =	2

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

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





				__lowerCAmelCase	:       str						       =	None

'''simple docstring'''


from __future__ import annotations

from statistics import mean





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	__UpperCAmelCase							=			[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i]
	__UpperCAmelCase							=			[]

	__UpperCAmelCase							=			0
	__UpperCAmelCase							=			0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			-1

		for i in range(UpperCamelCase__       ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(UpperCamelCase__       )

		if len(UpperCamelCase__       ) > 0:
			__UpperCAmelCase							=			ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					__UpperCAmelCase							=			i
			total_time += burst_time[target_process]
			completed += 1
			__UpperCAmelCase							=			0
			__UpperCAmelCase							=			(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time





def       lowerCAmelCase					(		UpperCamelCase__				:   list[int]    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   list[int]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[0] * no_of_processes
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
					print("[TEST CASE 01]")

					__lowerCAmelCase	:       List[Any]						       =	4
					__lowerCAmelCase	:       List[Any]						       =	[2, 5, 3, 7]
					__lowerCAmelCase	:       Tuple						       =	[0, 0, 0, 0]
					__lowerCAmelCase	:       Optional[int]						       =	calculate_waitingtime(arrival_time, burst_time, no_of_processes)
					__lowerCAmelCase	:       Dict						       =	calculate_turnaroundtime(
					    burst_time, no_of_processes, waiting_time
					)

					# Printing the Result
					print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
					for i, process_id in enumerate(list(range(1, 5))):
										print(
										    F"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
										    F"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
										)
					print(F"""\nAverage waiting time = {mean(waiting_time):.5f}""")
					print(F"""Average turnaround time = {mean(turn_around_time):.5f}""")
