






"""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()











"""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_   )