
'''simple docstring'''



def  _A		(				lowercase__		,		lowercase__		,		lowercase__	):
						if right <= left:
												return
						lowercase__							 =       lowercase__							 =       left
						lowercase__							 =       right
						lowercase__							 =       sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__		,		lowercase__		,		a - 1	)
						quick_sort_apartition(lowercase__		,		b + 1		,		lowercase__	)





def  _A		(				lowercase__		,		lowercase__		,		lowercase__	):
						if left < right:
												lowercase__							 =       lomuto_partition(lowercase__		,		lowercase__		,		lowercase__	)
												quick_sort_lomuto_partition(lowercase__		,		lowercase__		,		pivot_index - 1	)
												quick_sort_lomuto_partition(lowercase__		,		pivot_index + 1		,		lowercase__	)





def  _A		(				lowercase__		,		lowercase__		,		lowercase__	):
						lowercase__							 =       sorting[right]
						lowercase__							 =       left
						for i in range(lowercase__		,		lowercase__	):
												if sorting[i] < pivot:
																		lowercase__ ,						lowercase__							 =       sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__ ,						lowercase__							 =       sorting[store_index], sorting[right]
						return store_index





def  _A		(				lowercase__	):
						if len(lowercase__	) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]	)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]	)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A          =    input("Enter numbers separated by a comma:\n").strip()
				__A          =    [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)








'''simple docstring'''



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
				import torch

				from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
				import pyanvml.pyanvml as nvml


__A          =    logging.get_logger(__name__)





class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							PyTorchBenchmarkArguments
			lowerCamelCase							:							PretrainedConfig
			lowerCamelCase							:							str       		=  "PyTorch"



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return torch.__version__



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									if self.args.torchscript:
															lowercase__							 =       True

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )

									model.eval()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
									else:
															lowercase__							 =       model

									def encoder_decoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       )
															return outputs

									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									lowercase__							 =       (
									    hasattr(lowerCamelCase__       ,     """architectures"""       )
									    and isinstance(config.architectures       ,     lowerCamelCase__       )
									    and len(config.architectures       ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					lowercase__							 =       config.architectures[0]
																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
																					lowercase__							 =       model_cls(lowerCamelCase__       )
															except ImportError:
																					raise ImportError(
																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
									else:
															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )

									if self.args.torchscript:
															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
									else:
															lowercase__							 =       model

									model.train()
									model.to(self.args.device       )

									# encoder-decoder has vocab size saved differently
									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )

									if self.args.fpaa:
															logger.info("""Running training in Mixed Precision..."""       )
															if not self.args.is_gpu:
																					raise ValueError("""Mixed precision is possible only for GPU."""       )

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
															loss.backward()
															return loss

									lowercase__							 =       (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train



			def A__ (					self       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
																					timeit.repeat(
																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															lowercase__							 =       timeit.repeat(
															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report()       )

															return min(lowerCamelCase__       ) / 10.0
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A"



			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:




									'''simple docstring'''



									try:
															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       start_memory_tracing("""transformers"""       )

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
																					    """ `--no-memory` or `args.memory=False`"""       )
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    """py3nvml not installed, we won't log GPU memory usage. """
																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
																											lowercase__							 =       """N/A"""
																					else:
																											logger.info(
																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
																											    """ on the same GPU."""       )
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
																											lowercase__							 =       meminfo.used
																											lowercase__							 =       Memory(lowerCamelCase__       )
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
															else:
																					lowercase__							 =       None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
															return "N/A", None






