
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


lowerCamelCase__        =						logging.get_logger(__name__)







class 	A__				(	__magic_name__      ):
				lowercase            =    42
				lowercase            =    42
				lowercase            =    "PyTorch"






				@property
				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							return torch.__version__






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : str   =						self._prepare_inference_func(a       ,	a       ,	a  )
							return self._measure_speed(_inference  )






				def 				_lowerCamelCase						(		self   : str       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[int]   =						self._prepare_inference_func(a       ,	a       ,	a  )
							return self._measure_memory(_inference  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						self._prepare_train_func(a       ,	a       ,	a  )
							return self._measure_speed(_train  )






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						self._prepare_train_func(a       ,	a       ,	a  )
							return self._measure_memory(_train  )






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.config_dict[model_name]

							if self.args.torchscript:
										lowerCAmelCase__ : List[str]   =						True

							lowerCAmelCase__ : Dict   =						(
							    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__ : Optional[int]   =						config.architectures[0]
													lowerCAmelCase__ : Optional[int]   =						__import__('transformers'       ,	fromlist=[model_class]  )
													lowerCAmelCase__ : Dict   =						getattr(a       ,	a  )
													lowerCAmelCase__ : List[str]   =						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__ : Tuple   =						MODEL_MAPPING[config.__class__](a  )

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

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase__ : int   =						config.vocab_size if hasattr(a       ,	'vocab_size'  ) else config.encoder.vocab_size
							lowerCAmelCase__ : Optional[Any]   =						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__ : int   =						torch.jit.trace(a       ,	a  )
							else:
										lowerCAmelCase__ : Any   =						model

							def encoder_decoder_forward():
										with torch.no_grad():
													lowerCAmelCase__ : Optional[int]   =						inference_model(a       ,	decoder_input_ids=a  )
										return outputs

							def encoder_forward():
										with torch.no_grad():
													lowerCAmelCase__ : Any   =						inference_model(a  )
										return outputs

							lowerCAmelCase__ : Any   =						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
							return _forward






				def 				_lowerCamelCase						(		self   : str       ,	a   : str       ,	a   : int       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						self.config_dict[model_name]

							lowerCAmelCase__ : Optional[Any]   =						(
							    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__ : Any   =						config.architectures[0]
													lowerCAmelCase__ : List[str]   =						__import__('transformers'       ,	fromlist=[model_class]  )
													lowerCAmelCase__ : List[Any]   =						getattr(a       ,	a  )
													lowerCAmelCase__ : Dict   =						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__ : int   =						MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a  )

							if self.args.torchscript:
										raise NotImplementedError('Training for torchscript is currently not implemented'  )
							else:
										lowerCAmelCase__ : Optional[Any]   =						model

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

							# encoder-decoder has vocab size saved differently
							lowerCAmelCase__ : int   =						config.vocab_size if hasattr(a       ,	'vocab_size'  ) else config.encoder.vocab_size
							lowerCAmelCase__ : Any   =						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__ : List[Any]   =						train_model(a       ,	labels=a  )[0]
										loss.backward()
										return loss

							def compute_loss_and_backprob_encoder_decoder():
										lowerCAmelCase__ : Any   =						train_model(a       ,	decoder_input_ids=a       ,	labels=a  )[0]
										loss.backward()
										return loss

							lowerCAmelCase__ : List[str]   =						(
							    compute_loss_and_backprob_encoder_decoder
							    if config.is_encoder_decoder
							    else compute_loss_and_backprob_encoder
							)
							return _train






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : Tuple  ):






							'''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(
													    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__ : List[str]   =						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 				_lowerCamelCase						(		self   : Dict       ,	a   : Callable[[], None]  ):






							'''simple docstring'''







							try:
										if self.args.trace_memory_line_by_line:
													lowerCAmelCase__ : Union[str, Any]   =						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__ : List[str]   =						'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__ : Any   =						nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
																lowerCAmelCase__ : Dict   =						nvml.nvmlDeviceGetMemoryInfo(a  )
																lowerCAmelCase__ : Tuple   =						meminfo.used
																lowerCAmelCase__ : Optional[int]   =						Memory(a  )
																# shutdown nvml
																nvml.nvmlShutdown()
										else:
													# cpu
													lowerCAmelCase__ : Optional[Any]   =						measure_peak_memory_cpu(a  )
													lowerCAmelCase__ : Union[str, Any]   =						Memory(a  ) if isinstance(a       ,	a  ) else memory_bytes

										if self.args.trace_memory_line_by_line:
													lowerCAmelCase__ : Tuple   =						stop_memory_tracing(a  )
										else:
													lowerCAmelCase__ : List[str]   =						None

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

import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )