code: 
  
  
  
  
  
  '''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__				: Tuple									=    logging.get_logger(__name__)
  
  
  
  
  
  
  class 							__snake_case    (				_UpperCAmelCase       ):
  	__lowerCAmelCase						=     42
  	__lowerCAmelCase						=     42
  	__lowerCAmelCase						=     '''PyTorch'''
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		List[str]:
  		return torch.__version__
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[int]:
  		snake_case__  					=      self._prepare_inference_func(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  		return self._measure_speed(_inference		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		str:
  		snake_case__  					=      self._prepare_inference_func(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  		return self._measure_memory(_inference		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[int]:
  		snake_case__  					=      self._prepare_train_func(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  		return self._measure_speed(_train		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Optional[int]:
  		snake_case__  					=      self._prepare_train_func(UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)
  		return self._measure_memory(_train		)
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		Dict:
  		snake_case__  					=      self.config_dict[model_name]
  
  		if self.args.torchscript:
  			snake_case__  					=      True
  
  		snake_case__  					=      (
  		    hasattr(UpperCamelCase_			,					'architectures'		)
  		    and isinstance(config.architectures			,					UpperCamelCase_		)
  		    and len(config.architectures		) > 0
  		)
  		if not self.args.only_pretrain_model and has_model_class_in_config:
  			try:
  				snake_case__  					=      config.architectures[0]
  				snake_case__  					=      __import__('transformers'			,					fromlist=[model_class]		)
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  				snake_case__  					=      model_cls(UpperCamelCase_		)
  			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:
  			snake_case__  					=      MODEL_MAPPING[config.__class__](UpperCamelCase_		)
  
  		model.eval()
  		model.to(self.args.device		)
  
  		# encoder-decoder has vocab size saved differently
  		snake_case__  					=      config.vocab_size if hasattr(UpperCamelCase_			,					'vocab_size'		) else config.encoder.vocab_size
  		snake_case__  					=      torch.randint(UpperCamelCase_			,					(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():
  				snake_case__  					=      torch.jit.trace(UpperCamelCase_			,					UpperCamelCase_		)
  		else:
  			snake_case__  					=      model
  
  		def encoder_decoder_forward():
  			with torch.no_grad():
  				snake_case__  					=      inference_model(UpperCamelCase_			,					decoder_input_ids=UpperCamelCase_		)
  			return outputs
  
  		def encoder_forward():
  			with torch.no_grad():
  				snake_case__  					=      inference_model(UpperCamelCase_		)
  			return outputs
  
  		snake_case__  					=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  		return _forward
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_		)			->		int:
  		snake_case__  					=      self.config_dict[model_name]
  
  		snake_case__  					=      (
  		    hasattr(UpperCamelCase_			,					'architectures'		)
  		    and isinstance(config.architectures			,					UpperCamelCase_		)
  		    and len(config.architectures		) > 0
  		)
  		if not self.args.only_pretrain_model and has_model_class_in_config:
  			try:
  				snake_case__  					=      config.architectures[0]
  				snake_case__  					=      __import__('transformers'			,					fromlist=[model_class]		)
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  				snake_case__  					=      model_cls(UpperCamelCase_		)
  			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:
  			snake_case__  					=      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase_		)
  
  		if self.args.torchscript:
  			raise NotImplementedError('Training for torchscript is currently not implemented'		)
  		else:
  			snake_case__  					=      model
  
  		model.train()
  		model.to(self.args.device		)
  
  		# encoder-decoder has vocab size saved differently
  		snake_case__  					=      config.vocab_size if hasattr(UpperCamelCase_			,					'vocab_size'		) else config.encoder.vocab_size
  		snake_case__  					=      torch.randint(UpperCamelCase_			,					(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():
  			snake_case__  					=      train_model(UpperCamelCase_			,					labels=UpperCamelCase_		)[0]
  			loss.backward()
  			return loss
  
  		def compute_loss_and_backprob_encoder_decoder():
  			snake_case__  					=      train_model(UpperCamelCase_			,					decoder_input_ids=UpperCamelCase_			,					labels=UpperCamelCase_		)[0]
  			loss.backward()
  			return loss
  
  		snake_case__  					=      (
  		    compute_loss_and_backprob_encoder_decoder
  		    if config.is_encoder_decoder
  		    else compute_loss_and_backprob_encoder
  		)
  		return _train
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		Dict:
  
  
  
  
  
  
  		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(
  				    UpperCamelCase_			,					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
  			snake_case__  					=      timeit.repeat(
  			    UpperCamelCase_			,					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(UpperCamelCase_		) / 1_0.0
  		except RuntimeError as e:
  			self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
  			return "N/A"
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		int:
  
  
  
  
  
  
  		try:
  			if self.args.trace_memory_line_by_line:
  				snake_case__  					=      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.'		)
  					snake_case__  					=      '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()
  					snake_case__  					=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
  					snake_case__  					=      nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase_		)
  					snake_case__  					=      meminfo.used
  					snake_case__  					=      Memory(UpperCamelCase_		)
  					# shutdown nvml
  					nvml.nvmlShutdown()
  			else:
  				# cpu
  				snake_case__  					=      measure_peak_memory_cpu(UpperCamelCase_		)
  				snake_case__  					=      Memory(UpperCamelCase_		) if isinstance(UpperCamelCase_			,					UpperCamelCase_		) else memory_bytes
  
  			if self.args.trace_memory_line_by_line:
  				snake_case__  					=      stop_memory_tracing(UpperCamelCase_		)
  			else:
  				snake_case__  					=      None
  
  			return memory, summary
  		except RuntimeError as e:
  			self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
  			return "N/A", None
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->str:
  	snake_case__  					=      len(UpperCAmelCase_       )
  	for i in range(length - 1       ):
  		snake_case__  					=      False
  		for j in range(length - 1 - i       ):
  			if collection[j] > collection[j + 1]:
  				snake_case__  					=      True
  				snake_case__					,     snake_case__  					=      collection[j + 1], collection[j]
  		if not swapped:
  			break  # Stop iteration if the collection is sorted.
  	return collection
  
  
  if __name__ == "__main__":
  			import doctest
  			import time
  
  			doctest.testmod()
  
  			a__				: Dict									=    input('''Enter numbers separated by a comma:''').strip()
  			a__				: Dict									=    [int(item) for item in user_input.split(''',''')]
  			a__				: Tuple									=    time.process_time()
  			print(*bubble_sort(unsorted), sep=''',''')
  			print(f"""Processing time: {(time.process_time() - start)%1e9 + 7}""")
  
  
  
  