
'''simple docstring'''



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__A          =    16
__A          =    32





def  _A		(				lowercase__	):
						return int(x / 2**20	)





class 			A							:



			def __enter__(					self       )			->		Optional[int]:




									'''simple docstring'''



									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									lowercase__							 =       torch.cuda.memory_allocated()
									lowercase__							 =       psutil.Process()

									lowercase__							 =       self.cpu_mem_used()
									lowercase__							 =       True
									lowercase__							 =       threading.Thread(target=self.peak_monitor_func       )
									lowercase__							 =       True
									peak_monitor_thread.start()
									return self



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return self.process.memory_info().rss



			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									lowercase__							 =       -1

									while True:
															lowercase__							 =       max(self.cpu_mem_used()       ,     self.cpu_peak       )

															# can't sleep or will not catch the peak right (this comment is here on purpose)
															# time.sleep(0.001) # 1msec

															if not self.peak_monitoring:
																					break



			def __exit__(					self       ,     *lowerCamelCase__       )			->		str:




									'''simple docstring'''



									lowercase__							 =       False

									gc.collect()
									torch.cuda.empty_cache()
									lowercase__							 =       torch.cuda.memory_allocated()
									lowercase__							 =       torch.cuda.max_memory_allocated()
									lowercase__							 =       bamb(self.end - self.begin       )
									lowercase__							 =       bamb(self.peak - self.begin       )

									lowercase__							 =       self.cpu_mem_used()
									lowercase__							 =       bamb(self.cpu_end - self.cpu_begin       )
									lowercase__							 =       bamb(self.cpu_peak - self.cpu_begin       )
									# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				__A          =    mocked_dataloaders  # noqa: F811





def  _A		(				lowercase__		,		lowercase__	):


						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,		SCREAMING_SNAKE_CASE__	) == "1":
												lowercase__							 =       2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						lowercase__							 =       FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__		,		ranka_only=SCREAMING_SNAKE_CASE__	)		,		optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__		,		ranka_only=SCREAMING_SNAKE_CASE__	)		,		)

						# Initialize accelerator
						if args.with_tracking:
												lowercase__							 =       Accelerator(
												    cpu=args.cpu		,		mixed_precision=args.mixed_precision		,		log_with="""wandb"""		,		project_dir=args.logging_dir		,		fsdp_plugin=SCREAMING_SNAKE_CASE__		,		)
						else:
												lowercase__							 =       Accelerator(fsdp_plugin=SCREAMING_SNAKE_CASE__	)
						accelerator.print(accelerator.distributed_type	)

						if hasattr(args.checkpointing_steps		,		"""isdigit"""	):
												if args.checkpointing_steps == "epoch":
																		lowercase__							 =       args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		lowercase__							 =       int(args.checkpointing_steps	)
												else:
																		raise ValueError(
																		    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
						else:
												lowercase__							 =       None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase__							 =       config['''lr''']
						lowercase__							 =       int(config["""num_epochs"""]	)
						lowercase__							 =       int(config["""seed"""]	)
						lowercase__							 =       int(config["""batch_size"""]	)

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												lowercase__							 =       vars(SCREAMING_SNAKE_CASE__	)
												accelerator.init_trackers("""fsdp_glue_no_trainer"""		,		SCREAMING_SNAKE_CASE__	)

						lowercase__							 =       AutoTokenizer.from_pretrained(args.model_name_or_path	)
						lowercase__							 =       load_dataset("""glue"""		,		"""mrpc"""	)
						lowercase__							 =       evaluate.load("""glue"""		,		"""mrpc"""	)

						def tokenize_function(lowercase__	):
												# max_length=None => use the model max length (it's actually the default)
												lowercase__							 =       tokenizer(examples["""sentence1"""]		,		examples["""sentence2"""]		,		truncation=SCREAMING_SNAKE_CASE__		,		max_length=SCREAMING_SNAKE_CASE__	)
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												lowercase__							 =       datasets.map(
												    SCREAMING_SNAKE_CASE__		,		batched=SCREAMING_SNAKE_CASE__		,		remove_columns=["""idx""", """sentence1""", """sentence2"""]		,		)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						lowercase__							 =       tokenized_datasets.rename_column("""label"""		,		"""labels"""	)

						# If the batch size is too big we use gradient accumulation
						lowercase__							 =       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase__							 =       batch_size // MAX_GPU_BATCH_SIZE
												lowercase__							 =       MAX_GPU_BATCH_SIZE

						def collate_fn(lowercase__	):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase__							 =       128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		lowercase__							 =       16
												elif accelerator.mixed_precision != "no":
																		lowercase__							 =       8
												else:
																		lowercase__							 =       None

												return tokenizer.pad(
												    SCREAMING_SNAKE_CASE__		,		padding="""longest"""		,		max_length=SCREAMING_SNAKE_CASE__		,		pad_to_multiple_of=SCREAMING_SNAKE_CASE__		,		return_tensors="""pt"""		,		)

						# Instantiate dataloaders.
						lowercase__							 =       DataLoader(
						    tokenized_datasets["""train"""]		,		shuffle=SCREAMING_SNAKE_CASE__		,		collate_fn=SCREAMING_SNAKE_CASE__		,		batch_size=SCREAMING_SNAKE_CASE__	)
						lowercase__							 =       DataLoader(
						    tokenized_datasets["""validation"""]		,		shuffle=SCREAMING_SNAKE_CASE__		,		collate_fn=SCREAMING_SNAKE_CASE__		,		batch_size=SCREAMING_SNAKE_CASE__	)

						set_seed(SCREAMING_SNAKE_CASE__	)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						lowercase__							 =       AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path		,		return_dict=SCREAMING_SNAKE_CASE__		,		low_cpu_mem_usage=SCREAMING_SNAKE_CASE__	)

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						lowercase__							 =       accelerator.prepare(SCREAMING_SNAKE_CASE__	)
						accelerator.print(SCREAMING_SNAKE_CASE__	)

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						lowercase__							 =       torch.optim.AdamW(params=model.parameters()		,		lr=SCREAMING_SNAKE_CASE__		,		weight_decay=2e-4	)

						# Instantiate scheduler
						lowercase__							 =       get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE__		,		num_warmup_steps=10		,		num_training_steps=(len(SCREAMING_SNAKE_CASE__	) * num_epochs) // gradient_accumulation_steps		,		)

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						lowercase__							 =       accelerator.prepare(
						    SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__	)

						lowercase__							 =       0

						# Potentially 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__							 =       os.path.basename(args.resume_from_checkpoint	)
												else:
																		# Get the most recent checkpoint
																		lowercase__							 =       [f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
																		dirs.sort(key=os.path.getctime	)
																		lowercase__							 =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												lowercase__							 =       os.path.splitext(SCREAMING_SNAKE_CASE__	)[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace("""epoch_"""		,		""""""	)	)
																		lowercase__							 =       None
												else:
																		lowercase__							 =       int(training_difference.replace("""step_"""		,		""""""	)	)
																		num_epochs -= resume_step // len(SCREAMING_SNAKE_CASE__	)
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		lowercase__							 =       (num_epochs * len(SCREAMING_SNAKE_CASE__	)) - resume_step

    # Now we train the model
						for epoch in range(SCREAMING_SNAKE_CASE__	):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								lowercase__							 =       0
																		for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device	)
																								lowercase__							 =       model(**SCREAMING_SNAKE_CASE__	)
																								lowercase__							 =       outputs.loss
																								lowercase__							 =       loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(SCREAMING_SNAKE_CASE__	)
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__	):
																														lowercase__							 =       f'''step_{overall_step}'''
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										lowercase__							 =       os.path.join(args.output_dir		,		SCREAMING_SNAKE_CASE__	)
																																				accelerator.save_state(SCREAMING_SNAKE_CASE__	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin	)	)	)
												accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used	)	)
												accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked	)	)
												accelerator.print(
												    """Total Peak Memory consumed during the train (max): {}""".format(
												        tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
																		    }		,		step=SCREAMING_SNAKE_CASE__		,		)

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(SCREAMING_SNAKE_CASE__	):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device	)
																								with torch.no_grad():
																														lowercase__							 =       model(**SCREAMING_SNAKE_CASE__	)
																								lowercase__							 =       outputs.logits.argmax(dim=-1	)
																								lowercase__							 =       accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
																								metric.add_batch(
																								    predictions=SCREAMING_SNAKE_CASE__		,		references=SCREAMING_SNAKE_CASE__		,		)

																		lowercase__							 =       metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(f'''epoch {epoch}:'''		,		SCREAMING_SNAKE_CASE__	)
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        """accuracy""": eval_metric["""accuracy"""],
																								        """f1""": eval_metric["""f1"""],
																								        """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE__	),
																								    }		,		step=SCREAMING_SNAKE_CASE__		,		)

																		if checkpointing_steps == "epoch":
																								lowercase__							 =       f'''epoch_{epoch}'''
																								if args.output_dir is not None:
																														lowercase__							 =       os.path.join(args.output_dir		,		SCREAMING_SNAKE_CASE__	)
																								accelerator.save_state(SCREAMING_SNAKE_CASE__	)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin	)	)	)
												accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used	)	)
												accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked	)	)
												accelerator.print(
												    """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin	)	)	)
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin	),
																		    }		,		step=SCREAMING_SNAKE_CASE__		,		)

						if args.with_tracking:
												accelerator.end_training()





def  _A		(				):
						lowercase__							 =       argparse.ArgumentParser(description="""Simple example of training script."""	)
						parser.add_argument(
						    """--mixed_precision"""		,		type=SCREAMING_SNAKE_CASE__		,		default=SCREAMING_SNAKE_CASE__		,		choices=["""no""", """fp16""", """bf16""", """fp8"""]		,		help="""Whether to use mixed precision. Choose"""
						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
						    """and an Nvidia Ampere GPU."""		,		)
						parser.add_argument("""--cpu"""		,		action="""store_true"""		,		help="""If passed, will train on the CPU."""	)
						parser.add_argument(
						    """--checkpointing_steps"""		,		type=SCREAMING_SNAKE_CASE__		,		default=SCREAMING_SNAKE_CASE__		,		help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""		,		)
						parser.add_argument(
						    """--resume_from_checkpoint"""		,		type=SCREAMING_SNAKE_CASE__		,		default=SCREAMING_SNAKE_CASE__		,		help="""If the training should continue from a checkpoint folder."""		,		)
						parser.add_argument(
						    """--with_tracking"""		,		action="""store_true"""		,		help="""Whether to load in all available experiment trackers from the environment and use them for logging."""		,		)
						parser.add_argument(
						    """--output_dir"""		,		type=SCREAMING_SNAKE_CASE__		,		default="""."""		,		help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,		)
						parser.add_argument(
						    """--logging_dir"""		,		type=SCREAMING_SNAKE_CASE__		,		default="""logs"""		,		help="""Location on where to store experiment tracking logs`"""		,		)
						parser.add_argument(
						    """--model_name_or_path"""		,		type=SCREAMING_SNAKE_CASE__		,		help="""Path to pretrained model or model identifier from huggingface.co/models."""		,		required=SCREAMING_SNAKE_CASE__		,		)
						lowercase__							 =       parser.parse_args()
						lowercase__							 =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				main()








'''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


__A          =    logging.get_logger(__name__)





class 			A							(       __UpperCAmelCase				):
			lowerCamelCase							:							PyTorchBenchmarkArguments
			lowerCamelCase							:							PretrainedConfig
			lowerCamelCase							:							str       		=  "PyTorch"



			@property
			def A__ (					self       )			->		Union[str, Any]:




									'''simple docstring'''



									return torch.__version__



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_inference       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_speed(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:




									'''simple docstring'''



									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
									return self._measure_memory(_train       )



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

									if self.args.torchscript:
															lowercase__							 =       True

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

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

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

									def encoder_decoder_forward():
															with torch.no_grad():
																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
															return outputs

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

									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:




									'''simple docstring'''



									lowercase__							 =       self.config_dict[model_name]

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

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

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

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

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

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



			def A__ (					self       ,     lowerCamelCase__       )			->		float:




									'''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(
																					    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
															lowercase__							 =       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 A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:




									'''simple docstring'''



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

															if self.args.trace_memory_line_by_line:
																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
															else:
																					lowercase__							 =       None

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






