
'''simple docstring'''



import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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__					:						Tuple											=     32
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = 16  )   ->   Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = AutoTokenizer.from_pretrained("""bert-base-cased"""  )
   _SCREAMING_SNAKE_CASE          = load_dataset("""glue"""			,					"""mrpc"""  )

   def tokenize_function(SCREAMING_SNAKE_CASE_  ):
      # max_length=None => use the model max length (it's actually the default)
      _SCREAMING_SNAKE_CASE          = 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():
      _SCREAMING_SNAKE_CASE          = 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
   _SCREAMING_SNAKE_CASE          = tokenized_datasets.rename_column("""label"""			,					"""labels"""  )

   def collate_fn(SCREAMING_SNAKE_CASE_  ):
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _SCREAMING_SNAKE_CASE          = 16
      elif accelerator.mixed_precision != "no":
         _SCREAMING_SNAKE_CASE          = 8
      else:
         _SCREAMING_SNAKE_CASE          = None

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

   # Instantiate dataloaders.
   _SCREAMING_SNAKE_CASE          = DataLoader(
       tokenized_datasets["""train"""]			,					shuffle=SCREAMING_SNAKE_CASE_			,					collate_fn=SCREAMING_SNAKE_CASE_			,					batch_size=SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE          = DataLoader(
       tokenized_datasets["""validation"""]			,					shuffle=SCREAMING_SNAKE_CASE_			,					collate_fn=SCREAMING_SNAKE_CASE_			,					batch_size=SCREAMING_SNAKE_CASE_  )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     UpperCamelCase__					:						List[str]											=     mocked_dataloaders  # noqa: F811
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Optional[int]:




   """simple docstring"""
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,					SCREAMING_SNAKE_CASE_  ) == "1":
      _SCREAMING_SNAKE_CASE          = 2
   # Initialize accelerator
   _SCREAMING_SNAKE_CASE          = Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision  )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _SCREAMING_SNAKE_CASE          = config["""lr"""]
   _SCREAMING_SNAKE_CASE          = int(config["""num_epochs"""]  )
   _SCREAMING_SNAKE_CASE          = int(config["""seed"""]  )
   _SCREAMING_SNAKE_CASE          = int(config["""batch_size"""]  )

   _SCREAMING_SNAKE_CASE          = evaluate.load("""glue"""			,					"""mrpc"""  )

   # New Code #
   # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
   # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
   # half each time. From this, we can calculate the number of gradient accumulation steps needed
   # and modify the Accelerator object as a result
   @find_executable_batch_size(starting_batch_size=int(SCREAMING_SNAKE_CASE_  )  )
   def inner_training_loop(SCREAMING_SNAKE_CASE_  ):
      # Since we need to modify the outside accelerator object, we need to bring it
      # to the local scope
      nonlocal accelerator

      # We can calculate the number of gradient accumulation steps based on the current
      # batch size vs the starting batch size
      _SCREAMING_SNAKE_CASE          = observed_batch_size // batch_size

      # And then set it in the Accelerator directly:
      _SCREAMING_SNAKE_CASE          = num_gradient_accumulation_steps

      # Next we need to free all of the stored model references in the Accelerator each time
      accelerator.free_memory()

      # And set the seed so our results are reproducable each reset
      set_seed(SCREAMING_SNAKE_CASE_  )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _SCREAMING_SNAKE_CASE          = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""			,					return_dict=SCREAMING_SNAKE_CASE_  )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      _SCREAMING_SNAKE_CASE          = model.to(accelerator.device  )

      # Instantiate optimizer
      _SCREAMING_SNAKE_CASE          = AdamW(params=model.parameters()			,					lr=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = get_dataloaders(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

      # Instantiate scheduler
      _SCREAMING_SNAKE_CASE          = get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE_			,					num_warmup_steps=1_00			,					num_training_steps=(len(SCREAMING_SNAKE_CASE_  ) * num_epochs)			,					)

      # Prepare everything
      # 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.
      _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = accelerator.prepare(
          SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )

      # Now we train the model
      for epoch in range(SCREAMING_SNAKE_CASE_  ):
         model.train()
         for step, batch in enumerate(SCREAMING_SNAKE_CASE_  ):
            # And perform gradient accumulation
            with accelerator.accumulate(SCREAMING_SNAKE_CASE_  ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device  )
               _SCREAMING_SNAKE_CASE          = model(**SCREAMING_SNAKE_CASE_  )
               _SCREAMING_SNAKE_CASE          = outputs.loss
               accelerator.backward(SCREAMING_SNAKE_CASE_  )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

         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():
               _SCREAMING_SNAKE_CASE          = model(**SCREAMING_SNAKE_CASE_  )
            _SCREAMING_SNAKE_CASE          = outputs.logits.argmax(dim=-1  )
            _SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE          = accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE_			,					references=SCREAMING_SNAKE_CASE_			,					)

         _SCREAMING_SNAKE_CASE          = metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"epoch {epoch}:"			,					SCREAMING_SNAKE_CASE_  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
   inner_training_loop()
def       lowerCAmelCase_		( )   ->   List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          = 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."""  )
   _SCREAMING_SNAKE_CASE          = parser.parse_args()
   # New Code #
   # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
   _SCREAMING_SNAKE_CASE          = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 2_56}
   training_function(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     main()













"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (inputs > threshold).type(inputs.type()							)
										if mask.sum() < nb_min:
																# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					List[str]     =    mask.flatten()
										_UpperCamelCase			:					List[Any]     =    0
										_UpperCamelCase			:					str     =    1
										return mask



