'''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'''


def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	# if the collection is empty, returns empty
	if collection == []:
		return []

	# get some information about the collection
	__UpperCAmelCase							=			len(UpperCamelCase__       )
	__UpperCAmelCase							=			max(UpperCamelCase__       )
	__UpperCAmelCase							=			min(UpperCamelCase__       )

	# create the counting array
	__UpperCAmelCase							=			coll_max + 1 - coll_min
	__UpperCAmelCase							=			[0] * counting_arr_length

	# count how much a number appears in the collection
	for number in collection:
		counting_arr[number - coll_min] += 1

	# sum each position with it's predecessors. now, counting_arr[i] tells
	# us how many elements <= i has in the collection
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			counting_arr[i] + counting_arr[i - 1]

	# create the output collection
	__UpperCAmelCase							=			[0] * coll_len

	# place the elements in the output, respecting the original order (stable
	# sort) from end to begin, updating counting_arr
	for i in reversed(range(0    ,					UpperCamelCase__       )       ):
		__UpperCAmelCase							=			collection[i]
		counting_arr[collection[i] - coll_min] -= 1

	return ordered





def       lowerCAmelCase					(		UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	return "".join([chr(UpperCamelCase__       ) for i in counting_sort([ord(UpperCamelCase__       ) for c in string]       )]       )


if __name__ == "__main__":
					# Test string sort
					assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

					__lowerCAmelCase	:       str						       =	input("Enter numbers separated by a comma:\n").strip()
					__lowerCAmelCase	:       List[Any]						       =	[int(item) for item in user_input.split(",")]
					print(counting_sort(unsorted))
