


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
#
########################################################################


UpperCamelCase__				       =			16
UpperCamelCase__				       =			32

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:




       '''simple docstring'''






       return int(x / 2**20			)

class 						UpperCAmelCase__   :



     '''simple docstring'''




     def __enter__( self       :       Union[str, Any]		):





            """simple docstring"""



            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            _lowercase					:				Dict           =			torch.cuda.memory_allocated()
            _lowercase					:				Union[str, Any]           =			psutil.Process()

            _lowercase					:				List[Any]           =			self.cpu_mem_used()
            _lowercase					:				int           =			True
            _lowercase					:				Optional[int]           =			threading.Thread(target=self.peak_monitor_func		)
            _lowercase					:				List[str]           =			True
            peak_monitor_thread.start()
            return self




     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            return self.process.memory_info().rss




     def   lowerCAmelCase_   ( self       :       Dict		):





            """simple docstring"""



            _lowercase					:				Union[str, Any]           =			-1

            while True:
                   _lowercase					:				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       :       Optional[Any]	,      *UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				List[str]           =			False

            gc.collect()
            torch.cuda.empty_cache()
            _lowercase					:				str           =			torch.cuda.memory_allocated()
            _lowercase					:				int           =			torch.cuda.max_memory_allocated()
            _lowercase					:				int           =			bamb(self.end - self.begin		)
            _lowercase					:				List[str]           =			bamb(self.peak - self.begin		)

            _lowercase					:				List[Any]           =			self.cpu_mem_used()
            _lowercase					:				List[str]           =			bamb(self.cpu_end - self.cpu_begin		)
            _lowercase					:				Any           =			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

   UpperCamelCase__				       =			mocked_dataloaders  # noqa: F811

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Any:




       '''simple docstring'''






       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,     UpperCAmelCase_			) == "1":
              _lowercase					:				Optional[int]           =			2

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

       # Initialize accelerator
       if args.with_tracking:
              _lowercase					:				Union[str, Any]           =			Accelerator(
                  cpu=args.cpu    ,     mixed_precision=args.mixed_precision    ,     log_with='''wandb'''    ,     project_dir=args.logging_dir    ,     fsdp_plugin=UpperCAmelCase_    ,     )
       else:
              _lowercase					:				Optional[Any]           =			Accelerator(fsdp_plugin=UpperCAmelCase_			)
       accelerator.print(accelerator.distributed_type			)

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

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

       _lowercase					:				int           =			AutoTokenizer.from_pretrained(args.model_name_or_path			)
       _lowercase					:				List[Any]           =			load_dataset('''glue'''    ,     '''mrpc'''			)
       _lowercase					:				List[Any]           =			evaluate.load('''glue'''    ,     '''mrpc'''			)

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

       # If the batch size is too big we use gradient accumulation
       _lowercase					:				List[str]           =			1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _lowercase					:				Dict           =			batch_size // MAX_GPU_BATCH_SIZE
              _lowercase					:				Optional[int]           =			MAX_GPU_BATCH_SIZE

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

              return tokenizer.pad(
                  UpperCAmelCase_    ,     padding='''longest'''    ,     max_length=UpperCAmelCase_    ,     pad_to_multiple_of=UpperCAmelCase_    ,     return_tensors='''pt'''    ,     )

       # Instantiate dataloaders.
       _lowercase					:				Optional[int]           =			DataLoader(
           tokenized_datasets['''train''']    ,     shuffle=UpperCAmelCase_    ,     collate_fn=UpperCAmelCase_    ,     batch_size=UpperCAmelCase_			)
       _lowercase					:				int           =			DataLoader(
           tokenized_datasets['''validation''']    ,     shuffle=UpperCAmelCase_    ,     collate_fn=UpperCAmelCase_    ,     batch_size=UpperCAmelCase_			)

       set_seed(UpperCAmelCase_			)

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

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

       # 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					:				str           =			torch.optim.AdamW(params=model.parameters()    ,     lr=UpperCAmelCase_    ,     weight_decay=2E-4			)

       # Instantiate scheduler
       _lowercase					:				List[Any]           =			get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase_    ,     num_warmup_steps=10    ,     num_training_steps=(len(UpperCAmelCase_			) * 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					:				List[Any]           =			accelerator.prepare(
           UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)

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

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

    # Now we train the model
       for epoch in range(UpperCAmelCase_			):
              # 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					:				Optional[int]           =			0
                     for step, batch in enumerate(UpperCAmelCase_			):
                            # 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					:				Optional[Any]           =			model(**UpperCAmelCase_			)
                            _lowercase					:				int           =			outputs.loss
                            _lowercase					:				int           =			loss / gradient_accumulation_steps
                            # We keep track of the loss at each epoch
                            if args.with_tracking:
                                   total_loss += loss.detach().float()
                            accelerator.backward(UpperCAmelCase_			)
                            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(UpperCAmelCase_    ,     UpperCAmelCase_			):
                                   _lowercase					:				str           =			F'step_{overall_step}'
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 _lowercase					:				Tuple           =			os.path.join(args.output_dir    ,     UpperCAmelCase_			)
                                          accelerator.save_state(UpperCAmelCase_			)
        # 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=UpperCAmelCase_    ,     )

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

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

                     if checkpointing_steps == "epoch":
                            _lowercase					:				List[str]           =			F'epoch_{epoch}'
                            if args.output_dir is not None:
                                   _lowercase					:				int           =			os.path.join(args.output_dir    ,     UpperCAmelCase_			)
                            accelerator.save_state(UpperCAmelCase_			)
        # 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=UpperCAmelCase_    ,     )

       if args.with_tracking:
              accelerator.end_training()

def   UpperCamelCase__   (    )						->							str:




       '''simple docstring'''






       _lowercase					:				str           =			argparse.ArgumentParser(description='''Simple example of training script.'''			)
       parser.add_argument(
           '''--mixed_precision'''    ,     type=UpperCAmelCase_    ,     default=UpperCAmelCase_    ,     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=UpperCAmelCase_    ,     default=UpperCAmelCase_    ,     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=UpperCAmelCase_    ,     default=UpperCAmelCase_    ,     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=UpperCAmelCase_    ,     default='''.'''    ,     help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,     )
       parser.add_argument(
           '''--logging_dir'''    ,     type=UpperCAmelCase_    ,     default='''logs'''    ,     help='''Location on where to store experiment tracking logs`'''    ,     )
       parser.add_argument(
           '''--model_name_or_path'''    ,     type=UpperCAmelCase_    ,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,     required=UpperCAmelCase_    ,     )
       _lowercase					:				Dict           =			parser.parse_args()
       _lowercase					:				List[str]           =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(UpperCAmelCase_    ,     UpperCAmelCase_			)


if __name__ == "__main__":
   main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__				       =			{
    'configuration_realm': ['REALM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RealmConfig'],
    'tokenization_realm': ['RealmTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			['RealmTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'REALM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RealmEmbedder',
       'RealmForOpenQA',
       'RealmKnowledgeAugEncoder',
       'RealmPreTrainedModel',
       'RealmReader',
       'RealmScorer',
       'load_tf_weights_in_realm',
   ]
   UpperCamelCase__				       =			['RealmRetriever']


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)