



'''simple docstring'''


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
#
########################################################################

lowercase_   :					List[Any]        =     16
lowercase_   :					Optional[int]        =     32







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Accelerator			,	lowercase_     :   int = 16    ):
						lowercase   =				AutoTokenizer.from_pretrained("""bert-base-cased"""    )
						lowercase   =				load_dataset("""glue"""			,	"""mrpc"""    )

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

						def collate_fn(lowercase_     :   Tuple    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												lowercase   =				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":
																		lowercase   =				16
												elif accelerator.mixed_precision != "no":
																		lowercase   =				8
												else:
																		lowercase   =				None

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

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

						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

						lowercase_   :					List[Any]        =     mocked_dataloaders  # noqa: F811







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   Optional[int]			,	lowercase_     :   Tuple    ):


						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,	lowercase_    ) == "1":
												lowercase   =				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:
												lowercase   =				Accelerator(
												    cpu=args.cpu			,	mixed_precision=args.mixed_precision			,	log_with="""all"""			,	project_dir=args.project_dir    )
						else:
												lowercase   =				Accelerator(cpu=args.cpu			,	mixed_precision=args.mixed_precision    )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase   =				config["""lr"""]
						lowercase   =				int(config["""num_epochs"""]    )
						lowercase   =				int(config["""seed"""]    )
						lowercase   =				int(config["""batch_size"""]    )
						set_seed(lowercase_    )

						lowercase  , lowercase   =				get_dataloaders(lowercase_			,	lowercase_    )
						lowercase   =				evaluate.load("""glue"""			,	"""mrpc"""    )

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

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

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

						# Instantiate optimizer
						lowercase   =				AdamW(params=model.parameters()			,	lr=lowercase_    )

						# Instantiate scheduler
						lowercase   =				get_linear_schedule_with_warmup(
						    optimizer=lowercase_			,	num_warmup_steps=100			,	num_training_steps=(len(lowercase_    ) * 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.
						lowercase  , lowercase  , lowercase  , lowercase  , lowercase   =				accelerator.prepare(
						    lowercase_			,	lowercase_			,	lowercase_			,	lowercase_			,	lowercase_    )

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

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

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

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

												# 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(lowercase_    ),
																		        """epoch""": epoch,
																		    }			,	step=lowercase_			,	)

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


if __name__ == "__main__":
						main()





'''simple docstring'''



from ..utils import DummyObject, requires_backends



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       int				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Dict				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)



class 				_snake_case					(   metaclass=a_			):
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     ['''torch''', '''transformers''', '''onnx''']

  def __init__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(self      , ['torch', 'transformers', 'onnx']	)

  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)


  @classmethod
  def   _SCREAMING_SNAKE_CASE				(				cls      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      requires_backends(cls      , ['torch', 'transformers', 'onnx']	)





