



'''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


UpperCAmelCase			=    logging.get_logger(__name__)

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    PyTorchBenchmarkArguments
						UpperCAmelCase							:    PretrainedConfig
						UpperCAmelCase							:    str           =		"PyTorch"


						@property
						def 				__snake_case    (					self      )	->	Any:
							return torch.__version__


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	float:
							lowerCAmelCase    =	self._prepare_inference_func(A_	,       A_	,       A_      )
							return self._measure_speed(_inference      )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	[Memory, Optional[MemorySummary]]:
							lowerCAmelCase    =	self._prepare_inference_func(A_	,       A_	,       A_      )
							return self._measure_memory(_inference      )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	float:
							lowerCAmelCase    =	self._prepare_train_func(A_	,       A_	,       A_      )
							return self._measure_speed(_train      )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	[Memory, Optional[MemorySummary]]:
							lowerCAmelCase    =	self._prepare_train_func(A_	,       A_	,       A_      )
							return self._measure_memory(_train      )


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	Callable[[], None]:
							lowerCAmelCase    =	self.config_dict[model_name]

							if self.args.torchscript:
								lowerCAmelCase    =	True

							lowerCAmelCase    =	(
							    hasattr(A_	,       """architectures"""      )
							    and isinstance(config.architectures	,       A_      )
							    and len(config.architectures      ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
									lowerCAmelCase    =	config.architectures[0]
									lowerCAmelCase    =	__import__("""transformers"""	,       fromlist=[model_class]      )
									lowerCAmelCase    =	getattr(A_	,       A_      )
									lowerCAmelCase    =	model_cls(A_      )
								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:
								lowerCAmelCase    =	MODEL_MAPPING[config.__class__](A_      )

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

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase    =	config.vocab_size if hasattr(A_	,       """vocab_size"""      ) else config.encoder.vocab_size
							lowerCAmelCase    =	torch.randint(A_	,       (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():
									lowerCAmelCase    =	torch.jit.trace(A_	,       A_      )
							else:
								lowerCAmelCase    =	model

							def encoder_decoder_forward():
								with torch.no_grad():
									lowerCAmelCase    =	inference_model(A_	,       decoder_input_ids=A_      )
								return outputs

							def encoder_forward():
								with torch.no_grad():
									lowerCAmelCase    =	inference_model(A_      )
								return outputs

							lowerCAmelCase    =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
							return _forward


						def 				__snake_case    (					self	,       A_	,       A_	,       A_      )	->	Callable[[], None]:
							lowerCAmelCase    =	self.config_dict[model_name]

							lowerCAmelCase    =	(
							    hasattr(A_	,       """architectures"""      )
							    and isinstance(config.architectures	,       A_      )
							    and len(config.architectures      ) > 0
							)
							if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
									lowerCAmelCase    =	config.architectures[0]
									lowerCAmelCase    =	__import__("""transformers"""	,       fromlist=[model_class]      )
									lowerCAmelCase    =	getattr(A_	,       A_      )
									lowerCAmelCase    =	model_cls(A_      )
								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:
								lowerCAmelCase    =	MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A_      )

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

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

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase    =	config.vocab_size if hasattr(A_	,       """vocab_size"""      ) else config.encoder.vocab_size
							lowerCAmelCase    =	torch.randint(A_	,       (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():
								lowerCAmelCase    =	train_model(A_	,       labels=A_      )[0]
								loss.backward()
								return loss

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

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


						def 				__snake_case    (					self	,       A_      )	->	float:
							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(
									    A_	,       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
								lowerCAmelCase    =	timeit.repeat(
								    A_	,       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(A_      ) / 1_0.0
							except RuntimeError as e:
								self.print_fn(f'Doesn\'t fit on GPU. {e}'      )
								return "N/A"




						def 				__snake_case    (					self	,       A_      )	->	[Memory, MemorySummary]:
							try:
								if self.args.trace_memory_line_by_line:
									lowerCAmelCase    =	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."""      )
										lowerCAmelCase    =	"""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()
										lowerCAmelCase    =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
										lowerCAmelCase    =	nvml.nvmlDeviceGetMemoryInfo(A_      )
										lowerCAmelCase    =	meminfo.used
										lowerCAmelCase    =	Memory(A_      )
										# shutdown nvml
										nvml.nvmlShutdown()
								else:
									# cpu
									lowerCAmelCase    =	measure_peak_memory_cpu(A_      )
									lowerCAmelCase    =	Memory(A_      ) if isinstance(A_	,       A_      ) else memory_bytes

								if self.args.trace_memory_line_by_line:
									lowerCAmelCase    =	stop_memory_tracing(A_      )
								else:
									lowerCAmelCase    =	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 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int = 4_000_000							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	1
	lowerCAmelCase    =	2
	lowerCAmelCase    =	0
	while j <= n:
		if j % 2 == 0:
			total += j
		lowerCAmelCase,   lowerCAmelCase    =	j, i + j

	return total


if __name__ == "__main__":
				print(F'''{solution() = }''')