






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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def       _UpperCamelCase			(*_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Union[Dict, Any]] = None ,						_lowerCamelCase				:  List[Any]=True ,						_lowerCamelCase				:  str=2						)-> str:




     '''simple docstring'''





     from .. import __version__

     __snake_case            =	take_from
     __snake_case            =	()
     if not isinstance(args[0] ,						_lowerCamelCase						):
          __snake_case            =	(args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(_lowerCamelCase						).base_version						) >= version.parse(_lowerCamelCase						):
               raise ValueError(
                   f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                   f''' version {__version__} is >= {version_name}'''						)

          __snake_case            =	None
          if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
          elif hasattr(_lowerCamelCase ,						_lowerCamelCase						):
               values += (getattr(_lowerCamelCase ,						_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
          elif deprecated_kwargs is None:
               __snake_case            =	f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

          if warning is not None:
               __snake_case            =	warning + ''' ''' if standard_warn else ''''''
               warnings.warn(warning + message ,						_lowerCamelCase ,						stacklevel=_lowerCamelCase						)

     if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and len(_lowerCamelCase						) > 0:
          __snake_case            =	inspect.getouterframes(inspect.currentframe()						)[1]
          __snake_case            =	call_frame.filename
          __snake_case            =	call_frame.lineno
          __snake_case            =	call_frame.function
          __snake_case       ,     __snake_case            =	next(iter(deprecated_kwargs.items()						)						)
          raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''						)

     if len(_lowerCamelCase						) == 0:
          return
     elif len(_lowerCamelCase						) == 1:
          return values[0]
     return values






