





'''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









'''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}""")



