






import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

__A					=	16
__A					=	32







def       lowercase__	(					A_:						Accelerator  ,      A_:						int = 16		)	->				Tuple:







       """simple docstring"""






       __UpperCAmelCase     				=AutoTokenizer.from_pretrained("""bert-base-cased"""		)
       __UpperCAmelCase     				=load_dataset("""glue"""  ,      """mrpc"""		)

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

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

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

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

       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

   __A					=	mocked_dataloaders  # noqa: F811







def       lowercase__	(					A_:						List[str]  ,      A_:						List[str]		)	->				Dict:







       """simple docstring"""






       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,      A_		) == "1":
              __UpperCAmelCase     				=2
       # Initialize Accelerator

       # New Code #
       # We pass in "all" to `log_with` to grab all available trackers in the environment
       # Note: If using a custom `Tracker` class, should be passed in here such as:
       # >>> log_with = ["all", MyCustomTrackerClassInstance()]
       if args.with_tracking:
              __UpperCAmelCase     				=Accelerator(
                  cpu=args.cpu  ,      mixed_precision=args.mixed_precision  ,      log_with="""all"""  ,      project_dir=args.project_dir		)
       else:
              __UpperCAmelCase     				=Accelerator(cpu=args.cpu  ,      mixed_precision=args.mixed_precision		)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __UpperCAmelCase     				=config["""lr"""]
       __UpperCAmelCase     				=int(config["""num_epochs"""]		)
       __UpperCAmelCase     				=int(config["""seed"""]		)
       __UpperCAmelCase     				=int(config["""batch_size"""]		)
       set_seed(A_		)

       __UpperCAmelCase				, __UpperCAmelCase     				=get_dataloaders(A_  ,      A_		)
       __UpperCAmelCase     				=evaluate.load("""glue"""  ,      """mrpc"""		)

       # If the batch size is too big we use gradient accumulation
       __UpperCAmelCase     				=1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              __UpperCAmelCase     				=batch_size // MAX_GPU_BATCH_SIZE
              __UpperCAmelCase     				=MAX_GPU_BATCH_SIZE

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

       # 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).
       __UpperCAmelCase     				=model.to(accelerator.device		)

       # Instantiate optimizer
       __UpperCAmelCase     				=AdamW(params=model.parameters()  ,      lr=A_		)

       # Instantiate scheduler
       __UpperCAmelCase     				=get_linear_schedule_with_warmup(
           optimizer=A_  ,      num_warmup_steps=100  ,      num_training_steps=(len(A_		) * num_epochs) // gradient_accumulation_steps  ,      )

       # 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.
       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=accelerator.prepare(
           A_  ,      A_  ,      A_  ,      A_  ,      A_		)

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              __UpperCAmelCase     				=os.path.split(A_		)[-1].split("""."""		)[0]
              accelerator.init_trackers(A_  ,      A_		)

       # Now we train the model
       for epoch in range(A_		):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     __UpperCAmelCase     				=0
              for step, batch in enumerate(A_		):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device		)
                     __UpperCAmelCase     				=model(**A_		)
                     __UpperCAmelCase     				=outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     __UpperCAmelCase     				=loss / gradient_accumulation_steps
                     accelerator.backward(A_		)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(A_		):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device		)
                     with torch.no_grad():
                            __UpperCAmelCase     				=model(**A_		)
                     __UpperCAmelCase     				=outputs.logits.argmax(dim=-1		)
                     __UpperCAmelCase				, __UpperCAmelCase     				=accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                     metric.add_batch(
                         predictions=A_  ,      references=A_  ,      )

              __UpperCAmelCase     				=metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''  ,      A_		)

              # New Code #
              # To actually log, we call `Accelerator.log`
              # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
              if args.with_tracking:
                     accelerator.log(
                         {
                             """accuracy""": eval_metric["""accuracy"""],
                             """f1""": eval_metric["""f1"""],
                             """train_loss""": total_loss.item() / len(A_		),
                             """epoch""": epoch,
                         }  ,      step=A_  ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
       if args.with_tracking:
              accelerator.end_training()







def       lowercase__	(					)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=argparse.ArgumentParser(description="""Simple example of training script."""		)
       parser.add_argument(
           """--mixed_precision"""  ,      type=A_  ,      default=A_  ,      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(
           """--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(
           """--project_dir"""  ,      type=A_  ,      default="""logs"""  ,      help="""Location on where to store experiment tracking logs` and relevent project information"""  ,      )
       __UpperCAmelCase     				=parser.parse_args()
       __UpperCAmelCase     				={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(A_  ,      A_		)


if __name__ == "__main__":
   main()





'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects



def snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]	,   args[1:])])
         raise TypeError(
             F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
             F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
      while True:
         if batch_size == 0:
            raise RuntimeError('''No executable batch size found, reached zero.''')
         try:
            return function(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator





