



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   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   PyTorchBenchmarkArguments
							UpperCamelCase__					:   PretrainedConfig
							UpperCamelCase__					:   str       =       "PyTorch"


							@property
							def _a   (				self		):
											return torch.__version__


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_speed(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_inference_func(a_	,	a_	,	a_		)
											return self._measure_memory(_inference		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_speed(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self._prepare_train_func(a_	,	a_	,	a_		)
											return self._measure_memory(_train		)


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											if self.args.torchscript:
															a__					   =		True

											a__					   =		(
											    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:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		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:
															a__					   =		MODEL_MAPPING[config.__class__](a_		)

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

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

											def encoder_decoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_	,	decoder_input_ids=a_		)
															return outputs

											def encoder_forward():
															with torch.no_grad():
																			a__					   =		inference_model(a_		)
															return outputs

											a__					   =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		self.config_dict[model_name]

											a__					   =		(
											    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:
																			a__					   =		config.architectures[0]
																			a__					   =		__import__("""transformers"""	,	fromlist=[model_class]		)
																			a__					   =		getattr(a_	,	a_		)
																			a__					   =		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:
															a__					   =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a_		)

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

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

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

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

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


							def _a   (				self	,	a_		):
											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
															a__					   =		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_		) / 10.0
											except RuntimeError as e:
															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
															return "N/A"







							def _a   (				self	,	a_		):
											try:
															if self.args.trace_memory_line_by_line:
																			a__					   =		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."""		)
																							a__					   =		"""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()
																							a__					   =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
																							a__					   =		nvml.nvmlDeviceGetMemoryInfo(a_		)
																							a__					   =		meminfo.used
																							a__					   =		Memory(a_		)
																							# shutdown nvml
																							nvml.nvmlShutdown()
															else:
																			# cpu
																			a__					   =		measure_peak_memory_cpu(a_		)
																			a__					   =		Memory(a_		) if isinstance(a_	,	a_		) else memory_bytes

															if self.args.trace_memory_line_by_line:
																			a__					   =		stop_memory_tracing(a_		)
															else:
																			a__					   =		None

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


'''simple docstring'''




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments




def 				lowerCamelCase      ( )     -> List[Any]:
							lowercase_   :     Tuple									=			HfArgumentParser(UpperCAmelCase__					)
							try:
														lowercase_   :     List[Any]									=			parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowercase_   :     int									=			"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
														lowercase_   :     Any									=			""" """.join(str(UpperCAmelCase__					).split(""" """					)[:-1]					)
														lowercase_   :     Optional[Any]									=			""""""
														lowercase_   :     List[str]									=			eval(str(UpperCAmelCase__					).split(""" """					)[-1]					)
														lowercase_   :     Dict									=			[]
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]					)
																					else:
																												wrong_args.append(UpperCAmelCase__					)
														if len(UpperCAmelCase__					) > 0:
																					lowercase_   :     Dict									=			full_error_msg + begin_error_msg + str(UpperCAmelCase__					)
														raise ValueError(UpperCAmelCase__					)

							lowercase_   :     Optional[int]									=			PyTorchBenchmark(args=UpperCAmelCase__					)
							benchmark.run()


if __name__ == "__main__":
						main()





