


'''simple docstring'''





import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


lowerCamelCase		=	logging.getLogger(__name__)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		random.seed(_a )
		np.random.seed(_a )
		torch.manual_seed(_a )
		torch.cuda.manual_seed_all(_a )






def   lowerCamelCase_					( _a			,     _a			,     _a ):




		"""simple docstring"""






		model.eval()
		lowerCAmelCase__     :    Any					  =      next(model.parameters() ).device
		lowerCAmelCase__     :    Union[str, Any]					  =      1
		lowerCAmelCase__     :    Tuple					  =      torch.zeros((eval_batch_size, context_len)			,     dtype=torch.long			,     device=_a )
		lowerCAmelCase__     :    Optional[Any]					  =      DataLoader(_a			,     shuffle=_a			,     batch_size=_a )
		lowerCAmelCase__     :    Optional[int]					  =      torch.zeros(1			,     device=_a )
		lowerCAmelCase__     :    List[Any]					  =      0
		for batch in eval_dataloader:
				batch.to(_a )
				# pad
				context.zero_()
				for i in range(_a ):
						lowerCAmelCase__     :    Any					  =      batch[i]
				lowerCAmelCase__     :    Optional[Any]					  =      model(_a			,     labels=_a )
				eval_loss += outputs[0].sum().item()
				nb_eval_examples += batch.size(0 )
		lowerCAmelCase__     :    List[str]					  =      eval_loss / nb_eval_examples
		lowerCAmelCase__     :    Any					  =      torch.exp(_a )
		model.train()
		return perplexity






def   lowerCamelCase_					( _a="gpt2" ):




		"""simple docstring"""






		lowerCAmelCase__     :    Dict					  =      GPTaLMHeadModel.from_pretrained(_a			,     output_hidden_states=_a )
		torch.save(model.state_dict()			,     model_name + '''local.pt''' )
		return model






def   lowerCamelCase_					( _a			,     _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Dict					  =      copy.deepcopy(_a )
		model.to(_a )

		lowerCAmelCase__     :    Optional[int]					  =      ['''bias''', '''LayerNorm.weight''']
		lowerCAmelCase__     :    Any					  =      [
		    {
		        '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
		        '''weight_decay''': 0.0,
		    },
		    {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )], '''weight_decay''': 0.0},
		]
		lowerCAmelCase__     :    Optional[Any]					  =      AdamW(_a			,     lr=5e-5			,     eps=1e-8 )
		lowerCAmelCase__     :    Tuple					  =      get_linear_schedule_with_warmup(_a			,     0			,     _a )
		torch.cuda.empty_cache()
		return model, lm_optimizer, lm_scheduler






def   lowerCamelCase_					( _a			,     _a			,     _a			,     _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    str					  =      np.array(_a ).mean()
		lowerCAmelCase__     :    Tuple					  =      np.array(_a ).std()
		lowerCAmelCase__     :    Any					  =      (real_perps - avg) / std
		lowerCAmelCase__     :    Optional[Any]					  =      list(zip(_a			,     _a			,     _a ) )
		joblib.dump(_a			,     _a )






def   lowerCamelCase_					( _a			,     _a			,     _a			,     _a			,     _a			,     _a			,     _a			,     _a="dev.jbl"			,     _a=recopy_gpta			,     ):




		"""simple docstring"""






		lowerCAmelCase__     :    Union[str, Any]					  =      []
		lowerCAmelCase__     :    Optional[int]					  =      []
		lowerCAmelCase__     :    str					  =      []

		# Initialize the transformer model
		lowerCAmelCase__     :    Union[str, Any]					  =      copy.deepcopy(_a )
		orig_model.to(device='''cpu''' )
		torch.cuda.empty_cache()

		# Compute perplexity of initial transformer model for comparison
		model.train()
		lowerCAmelCase__     :    Any					  =      recopy_model(_a			,     _a			,     _a )

		for step in tqdm(range(_a ) ):
				lowerCAmelCase__     :    List[Any]					  =      torch.zeros((1, context_len)			,     dtype=torch.long			,     device=_a )
				lowerCAmelCase__     :    Any					  =      random.choice(_a )
				lowerCAmelCase__     :    int					  =      random.randint(0			,     len(story[0] ) - context_len - 1 )
				lowerCAmelCase__     :    Optional[int]					  =      story[0][start : start + context_len]
				lm_optimizer.zero_grad()
				lowerCAmelCase__     :    Union[str, Any]					  =      model(_a			,     labels=_a )
				lowerCAmelCase__     :    Optional[Any]					  =      outputs[0]
				lowerCAmelCase__     :    Tuple					  =      compute_perplexity(_a			,     _a			,     _a )
				model.train()
				lm_loss.backward()
				# Do LM backprop
				torch.nn.utils.clip_grad_norm_(model.parameters()			,     3.0 )
				lm_optimizer.step()
				lm_scheduler.step()  # Update learning rate schedule

				# Compute perplexity after back-propagating on the selected context
				lowerCAmelCase__     :    int					  =      compute_perplexity(_a			,     _a			,     _a )

				# Periodically save the stored (X, IG(X)) pairs
				if step % 1_000 == 0 and step > 1:
						intermittent_save(_a			,     _a			,     _a			,     _a )

				# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
				lowerCAmelCase__     :    Union[str, Any]					  =      recopy_model(_a			,     _a			,     _a )

				past_perps.append(past_perp.item() )
				real_perps.append(orig_perp - real_perp.item() )
				contexts.append(np.array(context.cpu() ) )

		intermittent_save(_a			,     _a			,     _a			,     _a )






def   lowerCamelCase_					( _a			,     _a="data/tokenized_stories_train_wikitext103.jbl"			,     _a=100			,     _a=1_026			,     _a=True ):




		"""simple docstring"""






		lowerCAmelCase__     :    Tuple					  =      joblib.load(_a )
		print('''data loaded''' )
		lowerCAmelCase__     :    str					  =      []
		if trim:
				for i, example in enumerate(_a ):
						if len(example[0] ) > min_len:
								lowerCAmelCase__     :    List[str]					  =      random.randint(0			,     len(example[0] ) - context_len - 1 )
								objective_set.append(example[0, start : start + context_len] )
						if len(_a ) >= number:
								break
				lowerCAmelCase__     :    Optional[int]					  =      []
				for j in range(i + 1			,     len(_a ) ):
						if len(data[j][0] ) > min_len:
								train_data.append(data[j] )
		else:
				lowerCAmelCase__     :    Dict					  =      data[0:number]
				lowerCAmelCase__     :    int					  =      data[number:]

		joblib.dump(_a			,     '''objective_set.jbl''' )
		print('''objective set saved''' )
		return train_data, objective_set






def   lowerCamelCase_					( _a			,     _a			,     _a			,     _a			,     _a=50			,     _a="secondary_learner.pt" ):




		"""simple docstring"""






		lowerCAmelCase__     :    int					  =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
		# We will use the first 512 pairs from our dataset as a test set for
		# our secondary learner and the rest to train
		lowerCAmelCase__     :    List[str]					  =      train_dataset[:512]
		lowerCAmelCase__     :    Tuple					  =      train_dataset[512:]
		lowerCAmelCase__     :    Dict					  =      DataLoader(_a			,     shuffle=_a			,     batch_size=_a )
		lowerCAmelCase__     :    List[str]					  =      DataLoader(_a			,     shuffle=_a			,     batch_size=_a )

		# secondary learner model set up
		lowerCAmelCase__     :    Union[str, Any]					  =      nn.MSELoss()
		lowerCAmelCase__     :    Optional[Any]					  =      nn.MSELoss(reduction='''sum''' )
		secondary_learner.to(_a )
		lowerCAmelCase__     :    Union[str, Any]					  =      torch.optim.Adam(secondary_learner.parameters()			,     lr=0.0_00_01 )
		secondary_learner.train()

		# TODO in original code this is written as number of actual batches seen
		# not number of items seen but other places it is number of items instead.
		# improve consistency! changed this to epochs for clarity
		lowerCAmelCase__     :    str					  =      float('''inf''' )
		# Iterate through batches until we've used max_steps batches
		for epoch in range(int(_a ) ):
				lowerCAmelCase__     :    Any					  =      0.0
				secondary_learner.train()
				for step, batch in enumerate(_a ):
						lowerCAmelCase__     :    str					  =      batch[0].to(_a )
						lowerCAmelCase__     :    str					  =      batch[1].to(_a )
						lowerCAmelCase__     :    Optional[Any]					  =      secondary_learner(_a )
						q_optimizer.zero_grad()
						lowerCAmelCase__     :    Tuple					  =      loss(_a			,     real_q.float() )
						q_loss.backward()
						q_optimizer.step()
						tr_q_loss += q_loss.item()

						# model trains fairly quickly so we won't wait for a full epoch
						# eval is triggered at eval_freq and end of epochs
						if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_a )):
								lowerCAmelCase__     :    Tuple					  =      tr_q_loss / (step + 1)

								secondary_learner.eval()
								lowerCAmelCase__     :    List[str]					  =      0.0
								lowerCAmelCase__     :    str					  =      0.0
								lowerCAmelCase__     :    Tuple					  =      []
								lowerCAmelCase__     :    List[Any]					  =      []
								# Compute performance of the secondary learner after this batch
								for stepa, batcha in enumerate(_a ):
										lowerCAmelCase__     :    Optional[int]					  =      batcha[0].to(_a )
										lowerCAmelCase__     :    str					  =      batcha[1].to(_a )
										lowerCAmelCase__     :    int					  =      secondary_learner(_a )
										q_lossa += test_loss(_a			,     _a ).item()
										sum_qa += torch.sum(_a ).item()
										for ei, i in enumerate(predicted_qa.cpu().detach().numpy() ):
												predicted.append(i.item() )
										for ei, i in enumerate(real_qa.cpu().detach().numpy() ):
												actual.append(i.item() )

								q_lossa /= len(_a )
								print(
								    '''Epoch: '''			,     _a			,     '''step: '''			,     _a			,     '''Avg. q:'''			,     sum_qa / len(_a )			,     '''Train Loss: '''			,     _a			,     '''Test Loss: '''			,     _a			,     )
								if q_lossa < best_test_loss:
										joblib.dump((predicted, actual)			,     '''pred_vs_actual.jbl''' )
										torch.save(secondary_learner.state_dict()			,     _a )
										lowerCAmelCase__     :    Optional[int]					  =      q_lossa

						secondary_learner.train()
		return secondary_learner





class       _a							(       nn.Module):




	def __init__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	Tuple	)-> str:
			super(_SCREAMING_SNAKE_CASE	,      self	).__init__()
			lowerCAmelCase__     :    Dict					  =      model.transformer.wte
			lowerCAmelCase__     :    Optional[int]					  =      copy.deepcopy(model.transformer.wte.weight	)
			lowerCAmelCase__     :    str					  =      nn.Convad(self.embeddings.weight.size(1	)	,      256	,      3	,      padding=1	)
			lowerCAmelCase__     :    List[Any]					  =      nn.Sequential(nn.Linear(256	,      32	)	,      nn.Dropout(p=0.1	)	,      nn.Linear(32	,      32	)	,      nn.Linear(32	,      1	)	)




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			lowerCAmelCase__     :    int					  =      torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE	).squeeze(1	).transpose(1	,      2	)	)	,      2	)[0]
			lowerCAmelCase__     :    str					  =      self.fc(_SCREAMING_SNAKE_CASE	)
			return qs.squeeze(1	)




	@classmethod
	def 							UpperCAmelCase__(      cls		:	Any	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	int	)-> List[Any]:
			lowerCAmelCase__     :    int					  =      cls(_SCREAMING_SNAKE_CASE	)  # this calls __init__
			lowerCAmelCase__     :    str					  =      torch.load(_SCREAMING_SNAKE_CASE	)
			secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      model.transformer.wte
			lowerCAmelCase__     :    Dict					  =      copy.deepcopy(model.transformer.wte.weight	)
			return secondary_learner








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							(       _lowercase):
	_a						:						PyTorchBenchmarkArguments
	_a						:						PretrainedConfig
	_a						:						str   					=							"PyTorch"




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




	def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> float:
			lowerCAmelCase__     :    Optional[Any]					  =      self._prepare_inference_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_speed(_inference	)




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> [Memory, Optional[MemorySummary]]:
			lowerCAmelCase__     :    List[Any]					  =      self._prepare_inference_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_memory(_inference	)




	def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> float:
			lowerCAmelCase__     :    Any					  =      self._prepare_train_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_speed(_train	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> [Memory, Optional[MemorySummary]]:
			lowerCAmelCase__     :    Tuple					  =      self._prepare_train_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_memory(_train	)




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> Callable[[], None]:
			lowerCAmelCase__     :    int					  =      self.config_dict[model_name]

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

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

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

			# encoder-decoder has vocab size saved differently
			lowerCAmelCase__     :    str					  =      config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE	,      '''vocab_size'''	) else config.encoder.vocab_size
			lowerCAmelCase__     :    Dict					  =      torch.randint(_SCREAMING_SNAKE_CASE	,      (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(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    Optional[int]					  =      model

			def encoder_decoder_forward():
					with torch.no_grad():
							lowerCAmelCase__     :    str					  =      inference_model(_SCREAMING_SNAKE_CASE	,      decoder_input_ids=_SCREAMING_SNAKE_CASE	)
					return outputs

			def encoder_forward():
					with torch.no_grad():
							lowerCAmelCase__     :    List[Any]					  =      inference_model(_SCREAMING_SNAKE_CASE	)
					return outputs

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




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> Callable[[], None]:
			lowerCAmelCase__     :    Optional[int]					  =      self.config_dict[model_name]

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

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

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

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

			def compute_loss_and_backprob_encoder_decoder():
					lowerCAmelCase__     :    List[str]					  =      train_model(_SCREAMING_SNAKE_CASE	,      decoder_input_ids=_SCREAMING_SNAKE_CASE	,      labels=_SCREAMING_SNAKE_CASE	)[0]
					loss.backward()
					return loss

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




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[str]	)-> 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(
							    _SCREAMING_SNAKE_CASE	,      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__     :    Tuple					  =      timeit.repeat(
					    _SCREAMING_SNAKE_CASE	,      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(_SCREAMING_SNAKE_CASE	) / 10.0
			except RuntimeError as e:
					self.print_fn(F'Doesn\'t fit on GPU. {e}'	)
					return "N/A"




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	Callable[[], None]	)-> [Memory, MemorySummary]:
			try:
					if self.args.trace_memory_line_by_line:
							lowerCAmelCase__     :    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__     :    Union[str, Any]					  =      '''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__     :    Optional[int]					  =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
									lowerCAmelCase__     :    Any					  =      nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE	)
									lowerCAmelCase__     :    int					  =      meminfo.used
									lowerCAmelCase__     :    Tuple					  =      Memory(_SCREAMING_SNAKE_CASE	)
									# shutdown nvml
									nvml.nvmlShutdown()
					else:
							# cpu
							lowerCAmelCase__     :    Union[str, Any]					  =      measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    Union[str, Any]					  =      Memory(_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	) else memory_bytes

					if self.args.trace_memory_line_by_line:
							lowerCAmelCase__     :    int					  =      stop_memory_tracing(_SCREAMING_SNAKE_CASE	)
					else:
							lowerCAmelCase__     :    str					  =      None

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




