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


__snake_case         =     16
__snake_case         =     32





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      return int(x / 2**20    )







class       __snake_case :
 def __enter__(	self    )						->				str:
       '''simple docstring'''





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

       UpperCAmelCase     :			List[str]     	=self.cpu_mem_used()
       UpperCAmelCase     :			List[str]     	=True
       UpperCAmelCase     :			List[Any]     	=threading.Thread(target=self.peak_monitor_func    )
       UpperCAmelCase     :			Union[str, Any]     	=True
       peak_monitor_thread.start()
       return self
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       return self.process.memory_info().rss
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=-1

       while True:
             UpperCAmelCase     :			Tuple     	=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		, *snake_case__    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=False

       gc.collect()
       torch.cuda.empty_cache()
       UpperCAmelCase     :			Dict     	=torch.cuda.memory_allocated()
       UpperCAmelCase     :			int     	=torch.cuda.max_memory_allocated()
       UpperCAmelCase     :			int     	=bamb(self.end - self.begin    )
       UpperCAmelCase     :			int     	=bamb(self.peak - self.begin    )

       UpperCAmelCase     :			Tuple     	=self.cpu_mem_used()
       UpperCAmelCase     :			Optional[int]     	=bamb(self.cpu_end - self.cpu_begin    )
       UpperCAmelCase     :			Union[str, 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

     __snake_case         =     mocked_dataloaders  # noqa: F811





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Any:






      '''simple docstring'''





      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,       __lowerCAmelCase    ) == "1":
            UpperCAmelCase     :			List[Any]     	=2

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

      # Initialize accelerator
      if args.with_tracking:
            UpperCAmelCase     :			int     	=Accelerator(
                cpu=args.cpu		,       mixed_precision=args.mixed_precision		,       log_with='''wandb'''		,       project_dir=args.logging_dir		,       fsdp_plugin=__lowerCAmelCase		,       )
      else:
            UpperCAmelCase     :			Dict     	=Accelerator(fsdp_plugin=__lowerCAmelCase    )
      accelerator.print(accelerator.distributed_type    )

      if hasattr(args.checkpointing_steps		,       '''isdigit'''    ):
            if args.checkpointing_steps == "epoch":
                  UpperCAmelCase     :			Dict     	=args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  UpperCAmelCase     :			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:
            UpperCAmelCase     :			Optional[Any]     	=None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase     :			Tuple     	=config['''lr''']
      UpperCAmelCase     :			Union[str, Any]     	=int(config['''num_epochs''']    )
      UpperCAmelCase     :			int     	=int(config['''seed''']    )
      UpperCAmelCase     :			Optional[int]     	=int(config['''batch_size''']    )

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

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

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

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

      def collate_fn(__lowerCAmelCase    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase     :			Tuple     	=1_28 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     :			List[Any]     	=16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase     :			str     	=8
            else:
                  UpperCAmelCase     :			Optional[int]     	=None

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

      # Instantiate dataloaders.
      UpperCAmelCase     :			List[str]     	=DataLoader(
          tokenized_datasets['''train''']		,       shuffle=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase		,       batch_size=__lowerCAmelCase    )
      UpperCAmelCase     :			List[str]     	=DataLoader(
          tokenized_datasets['''validation''']		,       shuffle=__lowerCAmelCase		,       collate_fn=__lowerCAmelCase		,       batch_size=__lowerCAmelCase    )

      set_seed(__lowerCAmelCase    )

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

      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      UpperCAmelCase     :			List[str]     	=accelerator.prepare(__lowerCAmelCase    )
      accelerator.print(__lowerCAmelCase    )

      # 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     :			Dict     	=torch.optim.AdamW(params=model.parameters()		,       lr=__lowerCAmelCase		,       weight_decay=2e-4    )

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

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

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

    # Now we train the model
      for epoch in range(__lowerCAmelCase    ):
            # 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     :			Dict     	=0
                  for step, batch in enumerate(__lowerCAmelCase    ):
                        # 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     :			Tuple     	=model(**__lowerCAmelCase    )
                        UpperCAmelCase     :			str     	=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(__lowerCAmelCase    )
                        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(__lowerCAmelCase		,       __lowerCAmelCase    ):
                              UpperCAmelCase     :			int     	=f'''step_{overall_step}'''
                              if overall_step % checkpointing_steps == 0:
                                    if args.output_dir is not None:
                                          UpperCAmelCase     :			Dict     	=os.path.join(args.output_dir		,       __lowerCAmelCase    )
                                    accelerator.save_state(__lowerCAmelCase    )
        # 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=__lowerCAmelCase		,       )

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

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

                  if checkpointing_steps == "epoch":
                        UpperCAmelCase     :			Dict     	=f'''epoch_{epoch}'''
                        if args.output_dir is not None:
                              UpperCAmelCase     :			Optional[Any]     	=os.path.join(args.output_dir		,       __lowerCAmelCase    )
                        accelerator.save_state(__lowerCAmelCase    )
        # 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=__lowerCAmelCase		,       )

      if args.with_tracking:
            accelerator.end_training()





def 	lowerCAmelCase_			(					)->	Any:






      '''simple docstring'''





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


if __name__ == "__main__":
     main()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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