



"""simple docstring"""






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					def __init__(			self				:    Optional[Any],    lowerCamelCase				:    List[Any],    lowerCamelCase				:    Dict,    lowerCamelCase				:    str=False,    lowerCamelCase				:    Any=1024,    lowerCamelCase				:    Optional[Any]=1024,    lowerCamelCase				:    Any=3.6,    lowerCamelCase				:    Union[str, Any]=False,    )->   List[Any]:
								lowerCamelCase__						:						List[Any]						      =tokenizer
								lowerCamelCase__						:						str						      =tokenizer.bos_token_id
								lowerCamelCase__						:						Tuple						      =dataset
								lowerCamelCase__						:						List[Any]						      =seq_length
								lowerCamelCase__						:						Dict						      =0
								lowerCamelCase__						:						int						      =infinite
								lowerCamelCase__						:						int						      =0
								lowerCamelCase__						:						int						      =tokenized

								if self.tokenized:
											lowerCamelCase__						:						int						      =seq_length * num_of_sequences
											lowerCamelCase__						:						List[Any]						      ='''input_ids'''
								else:
											lowerCamelCase__						:						List[Any]						      =seq_length * chars_per_token * num_of_sequences
											lowerCamelCase__						:						Tuple						      ='''content'''



					def __iter__(			self				:    List[str]	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
								lowerCamelCase__						:						Tuple						      =True
								while more_examples:
											lowerCamelCase__      ,  lowerCamelCase__						:						str						      =[], 0
											while True:
														if buffer_len >= self.max_buffer_size:
																	break
														try:
																	buffer.append(next(lowerCamelCase	)[self.content_field]	)
																	buffer_len += len(buffer[-1]	)
														except StopIteration:
																	if self.infinite:
																				lowerCamelCase__						:						Optional[Any]						      =iter(self.dataset	)
																				self.epoch += 1
																				logger.info(F'''Dataset epoch: {self.epoch}'''	)
																	else:
																				lowerCamelCase__						:						List[str]						      =False
																				break
											if self.tokenized:
														lowerCamelCase__						:						Union[str, Any]						      =buffer
											else:
														lowerCamelCase__						:						Any						      =self.tokenizer(lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
											lowerCamelCase__						:						Optional[int]						      =[]
											for tokenized_input in tokenized_inputs:
														all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
											for i in range(0,    len(lowerCamelCase	),    self.seq_length	):
														lowerCamelCase__						:						Tuple						      =all_token_ids[i : i + self.seq_length]
														if len(lowerCamelCase	) == self.seq_length:
																	self.current_size += 1
																	yield torch.tensor(lowerCamelCase	)




					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    Optional[int]=1000	)->   Optional[int]:
								return ShufflerIterDataPipe(self,    buffer_size=lowerCamelCase	)






def 	snake_case__  (						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =args.model_ckpt.split('''/'''      )[-1]
			lowerCamelCase__						:						Tuple						      =logging.getLogger(__name__      )
			lowerCamelCase__						:						Dict						      =Path(args.save_dir      ) / '''log/'''
			log_dir.mkdir(exist_ok=__lowerCamelCase      )
			lowerCamelCase__						:						str						      =f'''debug_{accelerator.process_index}.log'''
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,						datefmt='''%m/%d/%Y %H:%M:%S'''				,						level=logging.INFO				,						handlers=[logging.FileHandler(log_dir / filename      ), logging.StreamHandler()]				,						)
			if accelerator.is_main_process:  # we only want to setup logging once
						accelerator.init_trackers(__lowerCamelCase				,						vars(__lowerCamelCase      )      )
						lowerCamelCase__						:						Any						      =accelerator.trackers[0].run.name
						logger.setLevel(logging.INFO      )
						datasets.utils.logging.set_verbosity_info()
						transformers.utils.logging.set_verbosity_info()
			else:
						lowerCamelCase__						:						List[str]						      =''''''
						logger.setLevel(logging.ERROR      )
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()
			return logger, run_name






def 	snake_case__  (						__lowerCamelCase							:       List[Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      ={'''streaming''': True}
			lowerCamelCase__						:						Any						      =load_dataset(args.dataset_name_train				,						split='''train'''				,						**__lowerCamelCase      )
			lowerCamelCase__						:						Dict						      =train_data.shuffle(buffer_size=args.shuffle_buffer				,						seed=args.seed      )
			lowerCamelCase__						:						Tuple						      =load_dataset(args.dataset_name_valid				,						split='''train'''				,						**__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =ConstantLengthDataset(
			    __lowerCamelCase				,						__lowerCamelCase				,						infinite=__lowerCamelCase				,						seq_length=args.seq_length				,						tokenized=args.tokenized      )
			lowerCamelCase__						:						List[Any]						      =ConstantLengthDataset(
			    __lowerCamelCase				,						__lowerCamelCase				,						infinite=__lowerCamelCase				,						seq_length=args.seq_length				,						tokenized=args.tokenized      )
			lowerCamelCase__						:						str						      =train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
			lowerCamelCase__						:						Union[str, Any]						      =DataLoader(__lowerCamelCase				,						batch_size=args.train_batch_size				,						shuffle=__lowerCamelCase      )
			lowerCamelCase__						:						List[Any]						      =DataLoader(__lowerCamelCase				,						batch_size=args.valid_batch_size      )
			return train_dataloader, eval_dataloader






def 	snake_case__  (						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
			"""simple docstring"""
			lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =[], []
			for n, p in model.named_parameters():
						if any(nd in n for nd in no_decay      ):
									params_without_wd.append(__lowerCamelCase      )
						else:
									params_with_wd.append(__lowerCamelCase      )
			return [
			    {"params": params_with_wd, "weight_decay": args.weight_decay},
			    {"params": params_without_wd, "weight_decay": 0.0},
			]






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Optional[int]      ):
			"""simple docstring"""
			logger.info(f'''Step {step}: {metrics}'''      )
			if accelerator.is_main_process:
						accelerator.log(__lowerCamelCase				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
			lowerCamelCase__						:						Tuple						      =accelerator.unwrap_model(__lowerCamelCase      ).config
			lowerCamelCase__						:						Any						      =4 if args.gradient_checkpointing else 3
			lowerCamelCase__						:						Any						      =args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
			lowerCamelCase__						:						str						      =24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
			lowerCamelCase__						:						Dict						      =factor * (
			    1.0
			    + (args.seq_length / (6.0 * config_model.n_embd))
			    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
			)
			lowerCamelCase__						:						Dict						      =flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
			return tflops






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			model.eval()
			lowerCamelCase__						:						str						      =[]
			for step, batch in enumerate(__lowerCamelCase      ):
						with torch.no_grad():
									lowerCamelCase__						:						Optional[Any]						      =model(__lowerCamelCase				,						labels=__lowerCamelCase      )
						lowerCamelCase__						:						int						      =outputs.loss.repeat(args.valid_batch_size      )
						losses.append(accelerator.gather(__lowerCamelCase      )      )
						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			lowerCamelCase__						:						str						      =torch.cat(__lowerCamelCase      )
			lowerCamelCase__						:						str						      =losses[: eval_dataloader.dataset.current_size].mean()
			try:
						lowerCamelCase__						:						Optional[Any]						      =torch.exp(__lowerCamelCase      )
			except OverflowError:
						lowerCamelCase__						:						Dict						      =float('''inf'''      )
			return loss.item(), perplexity.item()


# Settings
_lowercase					:					Dict 		=		HfArgumentParser(TrainingArguments)
_lowercase					:					Optional[Any] 		=		parser.parse_args()

# Accelerator
_lowercase					:					Optional[int] 		=		ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
_lowercase					:					str 		=		Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
_lowercase					:					List[str] 		=		{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

_lowercase					:					Union[str, Any] 		=		Namespace(**vars(args), **acc_state)
_lowercase					:					List[str] 		=		accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		_lowercase					:					List[Any] 		=		Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
_lowercase		,      _lowercase					:					Optional[Any] 		=		setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
_lowercase					:					int 		=		AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_lowercase					:					List[Any] 		=		AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowercase		,      _lowercase					:					Union[str, Any] 		=		create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowercase					:					Tuple 		=		AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowercase					:					List[str] 		=		get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)






def 	snake_case__  (						):
			"""simple docstring"""
			return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_lowercase		,      _lowercase		,      _lowercase		,      _lowercase					:					int 		=		accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
				accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}')
				accelerator.load_state(args.resume_from_checkpoint)
				_lowercase					:					Dict 		=		os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				_lowercase					:					Optional[Any] 		=		[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
				dirs.sort(key=os.path.getctime)
				_lowercase					:					int 		=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		_lowercase					:					int 		=		os.path.splitext(path)[0]
		_lowercase					:					List[str] 		=		int(training_difference.replace("step_", ""))

# Train model
model.train()
_lowercase					:					Optional[int] 		=		0
_lowercase					:					List[Any] 		=		time.time()
_lowercase					:					Dict 		=		0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		_lowercase					:					int 		=		model(batch, labels=batch, use_cache=False).loss
		_lowercase					:					Union[str, Any] 		=		accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
		_lowercase					:					Dict 		=		loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				_lowercase					:					List[Any] 		=		get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				_lowercase					:					str 		=		time.time() - t_start
				_lowercase					:					List[str] 		=		compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        "steps": completed_steps,
				        "loss/train": loss_tracking,
				        "lr": lr,
				        "tflops": tflops,
				        "time_per_iteration": elapsed_time,
				    },
				)
				_lowercase					:					List[Any] 		=		time.time()
				_lowercase					:					Union[str, Any] 		=		0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("Evaluating and saving model checkpoint")
				_lowercase		,      _lowercase					:					Union[str, Any] 		=		evaluate(args)
				log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
				accelerator.wait_for_everyone()
				_lowercase					:					Union[str, Any] 		=		os.path.join(args.save_dir, f'step_{step}')
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=f'step {step}')
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_lowercase		,      _lowercase					:					List[str] 		=		evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_lowercase					:					List[str] 		=		accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_lowercase					:					Optional[int] 		=		os.path.join(args.save_dir, f'step_{step}')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="final model")









"""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


_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				"PyTorch"



					@property
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								return torch.__version__



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_inference	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_inference	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						int						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						str						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						List[str]						      =self.config_dict[model_name]

								if self.args.torchscript:
											lowerCamelCase__						:						int						      =True

								lowerCamelCase__						:						Optional[Any]						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						int						      =config.architectures[0]
														lowerCamelCase__						:						Dict						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						str						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Tuple						      =model_cls(lowerCamelCase	)
											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__						:						List[str]						      =MODEL_MAPPING[config.__class__](lowerCamelCase	)

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

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

								def encoder_decoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =inference_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase	)
											return outputs

								def encoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Any						      =inference_model(lowerCamelCase	)
											return outputs

								lowerCamelCase__						:						Optional[int]						      =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						Any						      =self.config_dict[model_name]

								lowerCamelCase__						:						Tuple						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						List[Any]						      =config.architectures[0]
														lowerCamelCase__						:						int						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						int						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Dict						      =model_cls(lowerCamelCase	)
											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__						:						List[str]						      =MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

								if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
								else:
											lowerCamelCase__						:						str						      =model

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

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Dict						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (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__						:						Optional[int]						      =train_model(lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								def compute_loss_and_backprob_encoder_decoder():
											lowerCamelCase__						:						Dict						      =train_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								lowerCamelCase__						:						Optional[Any]						      =(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]	)->   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(
														    lowerCamelCase,    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(
											    lowerCamelCase,    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(lowerCamelCase	) / 10.0
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Callable[[], None]	)->   [Memory, MemorySummary]:
								try:
											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						Optional[int]						      =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__						:						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__						:						int						      =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																	lowerCamelCase__						:						Optional[Any]						      =nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
																	lowerCamelCase__						:						str						      =meminfo.used
																	lowerCamelCase__						:						List[str]						      =Memory(lowerCamelCase	)
																	# shutdown nvml
																	nvml.nvmlShutdown()
											else:
														# cpu
														lowerCamelCase__						:						List[Any]						      =measure_peak_memory_cpu(lowerCamelCase	)
														lowerCamelCase__						:						List[Any]						      =Memory(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						List[str]						      =stop_memory_tracing(lowerCamelCase	)
											else:
														lowerCamelCase__						:						Optional[int]						      =None

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




