




'''simple docstring'''






import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowerCAmelCase				     = logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowerCAmelCase				     = {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowerCAmelCase				     = {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowerCAmelCase				     = sorted(arg_to_scheduler.keys())
_lowerCAmelCase				     = '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class  A		(			pl.LightningModule		):




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase=None			,  _UpperCAmelCase="base"			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  _UpperCAmelCase=None			,  **_UpperCAmelCase			,  )     ->	Tuple:
							super().__init__()
							# TODO: move to self.save_hyperparameters()
							# self.save_hyperparameters()
							# can also expand arguments into trainer signature for easier reading

							self.save_hyperparameters(_UpperCAmelCase      )
							__UpperCamelCase				:      List[Any]									=       0
							__UpperCamelCase				:      List[Any]									=       Path(self.hparams.output_dir      )
							__UpperCamelCase				:      Tuple									=       self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
										__UpperCamelCase				:      List[Any]									=       AutoConfig.from_pretrained(
										    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,  **({"num_labels": num_labels} if num_labels is not None else {})			,  cache_dir=_UpperCAmelCase			,  **_UpperCAmelCase			,  )
							else:
										__UpperCamelCase				:      PretrainedConfig									=       config

							__UpperCamelCase				:      Optional[Any]									=       ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
							for p in extra_model_params:
										if getattr(self.hparams			,  _UpperCAmelCase			,  _UpperCAmelCase      ):
													assert hasattr(self.config			,  _UpperCAmelCase      ), f"model config doesn't have a `{p}` attribute"
													setattr(self.config			,  _UpperCAmelCase			,  getattr(self.hparams			,  _UpperCAmelCase      )      )

							if tokenizer is None:
										__UpperCamelCase				:      Tuple									=       AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,  cache_dir=_UpperCAmelCase			,  )
							else:
										__UpperCamelCase				:      PreTrainedTokenizer									=       tokenizer
							__UpperCamelCase				:      List[Any]									=       MODEL_MODES[mode]
							if model is None:
										__UpperCamelCase				:      Tuple									=       self.model_type.from_pretrained(
										    self.hparams.model_name_or_path			,  from_tf=bool(".ckpt" in self.hparams.model_name_or_path      )			,  config=self.config			,  cache_dir=_UpperCAmelCase			,  )
							else:
										__UpperCamelCase				:      Any									=       model
				def 	a_	(self			,  *_UpperCAmelCase			,  **_UpperCAmelCase      )     ->	Any:
							__UpperCamelCase				:      List[Any]									=       self.model_type.from_pretrained(*_UpperCAmelCase			,  **_UpperCAmelCase      )
				def 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      Any									=       arg_to_scheduler[self.hparams.lr_scheduler]
							__UpperCamelCase				:      Any									=       get_schedule_func(
							    self.opt			,  num_warmup_steps=self.hparams.warmup_steps			,  num_training_steps=self.total_steps()      )
							__UpperCamelCase				:      List[str]									=       {"scheduler": scheduler, "interval": "step", "frequency": 1}
							return scheduler
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      Union[str, Any]									=       self.model
							__UpperCamelCase				:      Optional[int]									=       ["bias", "LayerNorm.weight"]
							__UpperCamelCase				:      int									=       [
							    {
							        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
							        "weight_decay": self.hparams.weight_decay,
							    },
							    {
							        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )],
							        "weight_decay": 0.0,
							    },
							]
							if self.hparams.adafactor:
										__UpperCamelCase				:      List[Any]									=       Adafactor(
										    _UpperCAmelCase			,  lr=self.hparams.learning_rate			,  scale_parameter=_UpperCAmelCase			,  relative_step=_UpperCAmelCase      )

							else:
										__UpperCamelCase				:      Dict									=       AdamW(
										    _UpperCAmelCase			,  lr=self.hparams.learning_rate			,  eps=self.hparams.adam_epsilon      )
							__UpperCamelCase				:      Dict									=       optimizer

							__UpperCamelCase				:      Union[str, Any]									=       self.get_lr_scheduler()

							return [optimizer], [scheduler]
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[Any]:
							return self.validation_step(_UpperCAmelCase			,  _UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	Optional[int]:
							return self.validation_end(_UpperCAmelCase      )
				def 	a_	(self      )     ->	int:
							__UpperCamelCase				:      List[Any]									=       max(1			,  self.hparams.gpus      )  # TODO: consider num_tpu_cores
							__UpperCamelCase				:      Optional[Any]									=       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
				def 	a_	(self			,  _UpperCAmelCase      )     ->	int:
							if mode == "test":
										__UpperCamelCase				:      Optional[int]									=       len(self.test_dataloader().dataset      )
							else:
										__UpperCamelCase				:      Tuple									=       self.get_dataloader("train"			,  self.hparams.train_batch_size			,  shuffle=_UpperCAmelCase      )
										__UpperCamelCase				:      Dict									=       len(self.train_dataloader().dataset      )
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase = False      )     ->	List[Any]:
							raise NotImplementedError("You must implement this for your task"      )
				def 	a_	(self      )     ->	Any:
							return self.train_loader
				def 	a_	(self      )     ->	Any:
							return self.get_dataloader("dev"			,  self.hparams.eval_batch_size			,  shuffle=_UpperCAmelCase      )
				def 	a_	(self      )     ->	Union[str, Any]:
							return self.get_dataloader("test"			,  self.hparams.eval_batch_size			,  shuffle=_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase      )     ->	Optional[int]:
							return os.path.join(
							    self.hparams.data_dir			,  "cached_{}_{}_{}".format(
							        _UpperCAmelCase			,  list(filter(_UpperCAmelCase			,  self.hparams.model_name_or_path.split("/"      )      )      ).pop()			,  str(self.hparams.max_seq_length      )			,  )			,  )
				@pl.utilities.rank_zero_only
				def 	a_	(self			,  _UpperCAmelCase      )     ->	None:
							__UpperCamelCase				:      Union[str, Any]									=       self.output_dir.joinpath("best_tfmr"      )
							__UpperCamelCase				:      Tuple									=       self.step_count
							self.model.save_pretrained(_UpperCAmelCase      )
							self.tokenizer.save_pretrained(_UpperCAmelCase      )





				@staticmethod
				def 	a_	(_UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[str]:
							parser.add_argument(
							    "--model_name_or_path"			,  default=_UpperCAmelCase			,  type=_UpperCAmelCase			,  required=_UpperCAmelCase			,  help="Path to pretrained model or model identifier from huggingface.co/models"			,  )
							parser.add_argument(
							    "--config_name"			,  default=""			,  type=_UpperCAmelCase			,  help="Pretrained config name or path if not the same as model_name"      )
							parser.add_argument(
							    "--tokenizer_name"			,  default=_UpperCAmelCase			,  type=_UpperCAmelCase			,  help="Pretrained tokenizer name or path if not the same as model_name"			,  )
							parser.add_argument(
							    "--cache_dir"			,  default=""			,  type=_UpperCAmelCase			,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,  )
							parser.add_argument(
							    "--encoder_layerdrop"			,  type=_UpperCAmelCase			,  help="Encoder layer dropout probability (Optional). Goes into model.config"			,  )
							parser.add_argument(
							    "--decoder_layerdrop"			,  type=_UpperCAmelCase			,  help="Decoder layer dropout probability (Optional). Goes into model.config"			,  )
							parser.add_argument(
							    "--dropout"			,  type=_UpperCAmelCase			,  help="Dropout probability (Optional). Goes into model.config"			,  )
							parser.add_argument(
							    "--attention_dropout"			,  type=_UpperCAmelCase			,  help="Attention dropout probability (Optional). Goes into model.config"			,  )
							parser.add_argument("--learning_rate"			,  default=5E-5			,  type=_UpperCAmelCase			,  help="The initial learning rate for Adam."      )
							parser.add_argument(
							    "--lr_scheduler"			,  default="linear"			,  choices=_UpperCAmelCase			,  metavar=_UpperCAmelCase			,  type=_UpperCAmelCase			,  help="Learning rate scheduler"			,  )
							parser.add_argument("--weight_decay"			,  default=0.0			,  type=_UpperCAmelCase			,  help="Weight decay if we apply some."      )
							parser.add_argument("--adam_epsilon"			,  default=1E-8			,  type=_UpperCAmelCase			,  help="Epsilon for Adam optimizer."      )
							parser.add_argument("--warmup_steps"			,  default=0			,  type=_UpperCAmelCase			,  help="Linear warmup over warmup_steps."      )
							parser.add_argument("--num_workers"			,  default=4			,  type=_UpperCAmelCase			,  help="kwarg passed to DataLoader"      )
							parser.add_argument("--num_train_epochs"			,  dest="max_epochs"			,  default=3			,  type=_UpperCAmelCase      )
							parser.add_argument("--train_batch_size"			,  default=3_2			,  type=_UpperCAmelCase      )
							parser.add_argument("--eval_batch_size"			,  default=3_2			,  type=_UpperCAmelCase      )
							parser.add_argument("--adafactor"			,  action="store_true"      )







class  A		(			pl.Callback		):




				'''simple docstring'''





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	List[str]:
							__UpperCamelCase				:      List[Any]									=       trainer.lr_schedulers[0]["scheduler"]
							__UpperCamelCase				:      List[Any]									=       {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
							pl_module.logger.log_metrics(_UpperCAmelCase      )
				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Dict:
							rank_zero_info("***** Validation results *****"      )
							__UpperCamelCase				:      str									=       trainer.callback_metrics
							# Log results
							for key in sorted(_UpperCAmelCase      ):
										if key not in ["log", "progress_bar"]:
													rank_zero_info("{} = {}\n".format(_UpperCAmelCase			,  str(metrics[key]      )      )      )





				def 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Tuple:
							rank_zero_info("***** Test results *****"      )
							__UpperCamelCase				:      Optional[Any]									=       trainer.callback_metrics
							# Log and save results to file
							__UpperCamelCase				:      Optional[Any]									=       os.path.join(pl_module.hparams.output_dir			,  "test_results.txt"      )
							with open(_UpperCAmelCase			,  "w"      ) as writer:
										for key in sorted(_UpperCAmelCase      ):
													if key not in ["log", "progress_bar"]:
																rank_zero_info("{} = {}\n".format(_UpperCAmelCase			,  str(metrics[key]      )      )      )
																writer.write("{} = {}\n".format(_UpperCAmelCase			,  str(metrics[key]      )      )      )







def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    "--output_dir" ,				default=snake_case__ ,				type=snake_case__ ,				required=snake_case__ ,				help="The output directory where the model predictions and checkpoints will be written." ,				)
			parser.add_argument(
			    "--fp16" ,				action="store_true" ,				help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ,				)

			parser.add_argument(
			    "--fp16_opt_level" ,				type=snake_case__ ,				default="O2" ,				help=(
			        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
			        "See details at https://nvidia.github.io/apex/amp.html"
			    ) ,				)
			parser.add_argument("--n_tpu_cores" ,				dest="tpu_cores" ,				type=snake_case__						)
			parser.add_argument("--max_grad_norm" ,				dest="gradient_clip_val" ,				default=1.0 ,				type=snake_case__ ,				help="Max gradient norm"						)
			parser.add_argument("--do_train" ,				action="store_true" ,				help="Whether to run training."						)
			parser.add_argument("--do_predict" ,				action="store_true" ,				help="Whether to run predictions on the test set."						)
			parser.add_argument(
			    "--gradient_accumulation_steps" ,				dest="accumulate_grad_batches" ,				type=snake_case__ ,				default=1 ,				help="Number of updates steps to accumulate before performing a backward/update pass." ,				)
			parser.add_argument("--seed" ,				type=snake_case__ ,				default=42 ,				help="random seed for initialization"						)
			parser.add_argument(
			    "--data_dir" ,				default=snake_case__ ,				type=snake_case__ ,				required=snake_case__ ,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,				)




def       __lowerCAmelCase (   snake_case__ ,				snake_case__ ,				snake_case__=None ,				snake_case__=True ,				snake_case__=[] ,				snake_case__=None ,				snake_case__=None ,				**snake_case__ ,				):
			pl.seed_everything(args.seed						)

			# init model
			__UpperCamelCase				:      Dict									=       Path(model.hparams.output_dir						)
			odir.mkdir(exist_ok=snake_case__						)

			# add custom checkpoints
			if checkpoint_callback is None:
						__UpperCamelCase				:      Optional[int]									=       pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir ,				prefix="checkpoint" ,				monitor="val_loss" ,				mode="min" ,				save_top_k=1						)
			if early_stopping_callback:
						extra_callbacks.append(snake_case__						)
			if logging_callback is None:
						__UpperCamelCase				:      Optional[Any]									=       LoggingCallback()

			__UpperCamelCase				:      List[str]									=       {}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						__UpperCamelCase				:      Optional[Any]									=       16
						__UpperCamelCase				:      Union[str, Any]									=       args.fpaa_opt_level

			if args.gpus > 1:
						__UpperCamelCase				:      Optional[int]									=       "ddp"

			__UpperCamelCase				:      Tuple									=       args.accumulate_grad_batches
			__UpperCamelCase				:      int									=       extra_train_kwargs.get("accelerator" ,				snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       extra_train_kwargs.get("profiler" ,				snake_case__						)

			__UpperCamelCase				:      int									=       pl.Trainer.from_argparse_args(
			    snake_case__ ,				weights_summary=snake_case__ ,				callbacks=[logging_callback] + extra_callbacks ,				logger=snake_case__ ,				checkpoint_callback=snake_case__ ,				**snake_case__ ,				)

			if args.do_train:
						trainer.fit(snake_case__						)

			return trainer









'''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
#
########################################################################


_lowerCAmelCase				     = 16
_lowerCAmelCase				     = 32

def       __lowerCAmelCase (   snake_case__						):
			return int(x / 2**20						)



class  A		:




				'''simple docstring'''





				def __enter__(self      )     ->	List[Any]:
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							__UpperCamelCase				:      str									=       torch.cuda.memory_allocated()
							__UpperCamelCase				:      Dict									=       psutil.Process()

							__UpperCamelCase				:      List[str]									=       self.cpu_mem_used()
							__UpperCamelCase				:      str									=       True
							__UpperCamelCase				:      Tuple									=       threading.Thread(target=self.peak_monitor_func      )
							__UpperCamelCase				:      int									=       True
							peak_monitor_thread.start()
							return self
				def 	a_	(self      )     ->	Any:
							return self.process.memory_info().rss
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[Any]									=       -1

							while True:
										__UpperCamelCase				:      List[str]									=       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			,  *_UpperCAmelCase      )     ->	Dict:
							__UpperCamelCase				:      List[str]									=       False

							gc.collect()
							torch.cuda.empty_cache()
							__UpperCamelCase				:      Tuple									=       torch.cuda.memory_allocated()
							__UpperCamelCase				:      Any									=       torch.cuda.max_memory_allocated()
							__UpperCamelCase				:      List[Any]									=       bamb(self.end - self.begin      )
							__UpperCamelCase				:      Optional[int]									=       bamb(self.peak - self.begin      )

							__UpperCamelCase				:      Tuple									=       self.cpu_mem_used()
							__UpperCamelCase				:      str									=       bamb(self.cpu_end - self.cpu_begin      )
							__UpperCamelCase				:      Dict									=       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

				_lowerCAmelCase				     = mocked_dataloaders  # noqa: F811

def       __lowerCAmelCase (   snake_case__ ,				snake_case__						):
			# For testing only
			if os.environ.get("TESTING_MOCKED_DATALOADERS" ,				snake_case__						) == "1":
						__UpperCamelCase				:      Optional[int]									=       2

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

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

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

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

			__UpperCamelCase				:      Any									=       AutoTokenizer.from_pretrained(args.model_name_or_path						)
			__UpperCamelCase				:      List[str]									=       load_dataset("glue" ,				"mrpc"						)
			__UpperCamelCase				:      Any									=       evaluate.load("glue" ,				"mrpc"						)

			def tokenize_function(snake_case__						):
						# max_length=None => use the model max length (it's actually the default)
						__UpperCamelCase				:      List[str]									=       tokenizer(examples["sentence1"] ,				examples["sentence2"] ,				truncation=snake_case__ ,				max_length=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():
						__UpperCamelCase				:      Union[str, Any]									=       datasets.map(
						    snake_case__ ,				batched=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
			__UpperCamelCase				:      List[Any]									=       tokenized_datasets.rename_column("label" ,				"labels"						)

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

			def collate_fn(snake_case__						):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__UpperCamelCase				:      List[str]									=       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":
									__UpperCamelCase				:      Optional[Any]									=       16
						elif accelerator.mixed_precision != "no":
									__UpperCamelCase				:      str									=       8
						else:
									__UpperCamelCase				:      Any									=       None

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

			# Instantiate dataloaders.
			__UpperCamelCase				:      Dict									=       DataLoader(
			    tokenized_datasets["train"] ,				shuffle=snake_case__ ,				collate_fn=snake_case__ ,				batch_size=snake_case__						)
			__UpperCamelCase				:      Optional[Any]									=       DataLoader(
			    tokenized_datasets["validation"] ,				shuffle=snake_case__ ,				collate_fn=snake_case__ ,				batch_size=snake_case__						)

			set_seed(snake_case__						)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCamelCase				:      Union[str, Any]									=       AutoModelForSequenceClassification.from_pretrained(
			    args.model_name_or_path ,				return_dict=snake_case__ ,				low_cpu_mem_usage=snake_case__						)

			# New Code #
			# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
			__UpperCamelCase				:      List[Any]									=       accelerator.prepare(snake_case__						)
			accelerator.print(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
			__UpperCamelCase				:      Optional[Any]									=       torch.optim.AdamW(params=model.parameters() ,				lr=snake_case__ ,				weight_decay=2E-4						)

			# Instantiate scheduler
			__UpperCamelCase				:      int									=       get_linear_schedule_with_warmup(
			    optimizer=snake_case__ ,				num_warmup_steps=10 ,				num_training_steps=(len(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.
			__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase				:      Union[str, Any]									=       accelerator.prepare(
			    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__						)

			__UpperCamelCase				:      Dict									=       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						)
									__UpperCamelCase				:      List[Any]									=       os.path.basename(args.resume_from_checkpoint						)
						else:
									# Get the most recent checkpoint
									__UpperCamelCase				:      Union[str, Any]									=       [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
									dirs.sort(key=os.path.getctime						)
									__UpperCamelCase				:      Optional[int]									=       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						__UpperCamelCase				:      Optional[int]									=       os.path.splitext(snake_case__						)[0]

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

    # Now we train the model
			for epoch in range(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:
												__UpperCamelCase				:      int									=       0
									for step, batch in enumerate(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						)
												__UpperCamelCase				:      int									=       model(**snake_case__						)
												__UpperCamelCase				:      Tuple									=       outputs.loss
												__UpperCamelCase				:      List[str]									=       loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
															total_loss += loss.detach().float()
												accelerator.backward(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(snake_case__ ,				snake_case__						):
															__UpperCamelCase				:      Union[str, Any]									=       F"step_{overall_step}"
															if overall_step % checkpointing_steps == 0:
																		if args.output_dir is not None:
																					__UpperCamelCase				:      List[str]									=       os.path.join(args.output_dir ,				snake_case__						)
																		accelerator.save_state(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=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(snake_case__						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												with torch.no_grad():
															__UpperCamelCase				:      Any									=       model(**snake_case__						)
												__UpperCamelCase				:      Tuple									=       outputs.logits.argmax(dim=-1						)
												__UpperCamelCase		, __UpperCamelCase				:      Union[str, Any]									=       accelerator.gather_for_metrics((predictions, batch["labels"])						)
												metric.add_batch(
												    predictions=snake_case__ ,				references=snake_case__ ,				)

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

									if checkpointing_steps == "epoch":
												__UpperCamelCase				:      str									=       F"epoch_{epoch}"
												if args.output_dir is not None:
															__UpperCamelCase				:      Tuple									=       os.path.join(args.output_dir ,				snake_case__						)
												accelerator.save_state(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=snake_case__ ,				)

			if args.with_tracking:
						accelerator.end_training()

def       __lowerCAmelCase (   ):
			__UpperCamelCase				:      List[Any]									=       argparse.ArgumentParser(description="Simple example of training script."						)
			parser.add_argument(
			    "--mixed_precision" ,				type=snake_case__ ,				default=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=snake_case__ ,				default=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=snake_case__ ,				default=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=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=snake_case__ ,				default="logs" ,				help="Location on where to store experiment tracking logs`" ,				)
			parser.add_argument(
			    "--model_name_or_path" ,				type=snake_case__ ,				help="Path to pretrained model or model identifier from huggingface.co/models." ,				required=snake_case__ ,				)
			__UpperCamelCase				:      Optional[Any]									=       parser.parse_args()
			__UpperCamelCase				:      Optional[int]									=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(snake_case__ ,				snake_case__						)


if __name__ == "__main__":
				main()



