code: 
  
  
  
  
  
  
  """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
  #
  ########################################################################
  
  
  snake_case   :   Optional[int]    		=		1_6
  snake_case   :   Optional[Any]    		=		3_2
  
  
  def        A	(							__snake_case:   List[str]     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         return int(x / 2**2_0     )
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      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
             __magic_name__										=						torch.cuda.memory_allocated()
             __magic_name__										=						psutil.Process()
  
             __magic_name__										=						self.cpu_mem_used()
             __magic_name__										=						True
             __magic_name__										=						threading.Thread(target=self.peak_monitor_func   )
             __magic_name__										=						True
             peak_monitor_thread.start()
             return self
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return self.process.memory_info().rss
  
  
  
  
  
  
  
      def 							a__ (		self   :  int   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						-1
  
             while True:
                    __magic_name__										=						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   :  Tuple							,						*UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						False
  
             gc.collect()
             torch.cuda.empty_cache()
             __magic_name__										=						torch.cuda.memory_allocated()
             __magic_name__										=						torch.cuda.max_memory_allocated()
             __magic_name__										=						bamb(self.end - self.begin   )
             __magic_name__										=						bamb(self.peak - self.begin   )
  
             __magic_name__										=						self.cpu_mem_used()
             __magic_name__										=						bamb(self.cpu_end - self.cpu_begin   )
             __magic_name__										=						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   :   Optional[int]    		=		mocked_dataloaders  # noqa: F811
  
  
  def        A	(							__snake_case:   Optional[int]   ,    __snake_case:   List[str]     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,    SCREAMING_SNAKE_CASE__     ) == "1":
                __magic_name__										=						2
  
         # New Code #
         # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
         __magic_name__										=						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:
                __magic_name__										=						Accelerator(
                    cpu=args.cpu   ,    mixed_precision=args.mixed_precision   ,    log_with='wandb'   ,    project_dir=args.logging_dir   ,    fsdp_plugin=SCREAMING_SNAKE_CASE__   ,    )
         else:
                __magic_name__										=						Accelerator(fsdp_plugin=SCREAMING_SNAKE_CASE__     )
         accelerator.print(accelerator.distributed_type     )
  
         if hasattr(args.checkpointing_steps   ,    'isdigit'     ):
                if args.checkpointing_steps == "epoch":
                       __magic_name__										=						args.checkpointing_steps
                elif args.checkpointing_steps.isdigit():
                       __magic_name__										=						int(args.checkpointing_steps     )
                else:
                       raise ValueError(
                           F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
         else:
                __magic_name__										=						None
         # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
         __magic_name__										=						config['''lr''']
         __magic_name__										=						int(config['num_epochs']     )
         __magic_name__										=						int(config['seed']     )
         __magic_name__										=						int(config['batch_size']     )
  
         # We need to initialize the trackers we use, and also store our configuration
         if args.with_tracking:
                __magic_name__										=						vars(SCREAMING_SNAKE_CASE__     )
                accelerator.init_trackers('fsdp_glue_no_trainer'   ,    SCREAMING_SNAKE_CASE__     )
  
         __magic_name__										=						AutoTokenizer.from_pretrained(args.model_name_or_path     )
         __magic_name__										=						load_dataset('glue'   ,    'mrpc'     )
         __magic_name__										=						evaluate.load('glue'   ,    'mrpc'     )
  
         def tokenize_function(__snake_case:   List[Any]     ):
                # max_length=None => use the model max length (it's actually the default)
                __magic_name__										=						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():
                __magic_name__										=						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
         __magic_name__										=						tokenized_datasets.rename_column('label'   ,    'labels'     )
  
         # If the batch size is too big we use gradient accumulation
         __magic_name__										=						1
         if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
                __magic_name__										=						batch_size // MAX_GPU_BATCH_SIZE
                __magic_name__										=						MAX_GPU_BATCH_SIZE
  
         def collate_fn(__snake_case:   List[Any]     ):
                # On TPU it's best to pad everything to the same length or training will be very slow.
                __magic_name__										=						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
                # When using mixed precision we want round multiples of 8/16
                if accelerator.mixed_precision == "fp8":
                       __magic_name__										=						1_6
                elif accelerator.mixed_precision != "no":
                       __magic_name__										=						8
                else:
                       __magic_name__										=						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.
         __magic_name__										=						DataLoader(
             tokenized_datasets['train']   ,    shuffle=SCREAMING_SNAKE_CASE__   ,    collate_fn=SCREAMING_SNAKE_CASE__   ,    batch_size=SCREAMING_SNAKE_CASE__     )
         __magic_name__										=						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)
         __magic_name__										=						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
         __magic_name__										=						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
         __magic_name__										=						torch.optim.AdamW(params=model.parameters()   ,    lr=SCREAMING_SNAKE_CASE__   ,    weight_decay=2E-4     )
  
         # Instantiate scheduler
         __magic_name__										=						get_linear_schedule_with_warmup(
             optimizer=SCREAMING_SNAKE_CASE__   ,    num_warmup_steps=1_0   ,    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.
         __magic_name__										=						accelerator.prepare(
             SCREAMING_SNAKE_CASE__   ,    SCREAMING_SNAKE_CASE__   ,    SCREAMING_SNAKE_CASE__   ,    SCREAMING_SNAKE_CASE__     )
  
         __magic_name__										=						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     )
                       __magic_name__										=						os.path.basename(args.resume_from_checkpoint     )
                else:
                       # Get the most recent checkpoint
                       __magic_name__										=						[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                       dirs.sort(key=os.path.getctime     )
                       __magic_name__										=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
                # Extract `epoch_{i}` or `step_{i}`
                __magic_name__										=						os.path.splitext(SCREAMING_SNAKE_CASE__     )[0]
  
                if "epoch" in training_difference:
                       num_epochs -= int(training_difference.replace('epoch_'   ,    ''     )     )
                       __magic_name__										=						None
                else:
                       __magic_name__										=						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
                       __magic_name__										=						(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:
                              __magic_name__										=						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     )
                              __magic_name__										=						model(**SCREAMING_SNAKE_CASE__     )
                              __magic_name__										=						outputs.loss
                              __magic_name__										=						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__     ):
                                     __magic_name__										=						F"""step_{overall_step}"""
                                     if overall_step % checkpointing_steps == 0:
                                            if args.output_dir is not None:
                                                   __magic_name__										=						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():
                                     __magic_name__										=						model(**SCREAMING_SNAKE_CASE__     )
                              __magic_name__										=						outputs.logits.argmax(dim=-1     )
                              __magic_name__										=						accelerator.gather_for_metrics((predictions, batch['labels'])     )
                              metric.add_batch(
                                  predictions=SCREAMING_SNAKE_CASE__   ,    references=SCREAMING_SNAKE_CASE__   ,    )
  
                       __magic_name__										=						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":
                              __magic_name__										=						F"""epoch_{epoch}"""
                              if args.output_dir is not None:
                                     __magic_name__										=						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	(							)						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						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__   ,    )
         __magic_name__										=						parser.parse_args()
         __magic_name__										=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
         training_function(SCREAMING_SNAKE_CASE__   ,    SCREAMING_SNAKE_CASE__     )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )