



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

_lowercase				:					Tuple     					=32




def    UpperCAmelCase					(  lowercase__						:					Accelerator				,					lowercase__						:					int = 16	):



			'''simple docstring'''





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

			def tokenize_function(lowercase__						:					List[Any]	):
						# max_length=None => use the model max length (it's actually the default)
						a__					=  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():
						a__					=  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
			a__					=  tokenized_datasets.rename_column("""label"""				,					"""labels"""	)

			def collate_fn(lowercase__						:					Union[str, Any]	):
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									a__					=  16
						elif accelerator.mixed_precision != "no":
									a__					=  8
						else:
									a__					=  None

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

			# Instantiate dataloaders.
			a__					=  DataLoader(
			    tokenized_datasets["""train"""]				,					shuffle=lowercase__				,					collate_fn=lowercase__				,					batch_size=lowercase__	)
			a__					=  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    UpperCAmelCase					(  lowercase__						:					List[str]				,					lowercase__						:					Dict	):



			'''simple docstring'''





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

			a__					=  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(lowercase__	)	)
			def inner_training_loop(lowercase__						:					List[str]	):
						# 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
						a__					=  observed_batch_size // batch_size

						# And then set it in the Accelerator directly:
						a__					=  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(lowercase__	)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						a__					=  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).
						a__					=  model.to(accelerator.device	)

						# Instantiate optimizer
						a__					=  AdamW(params=model.parameters()				,					lr=lowercase__	)
						a__				,       a__					=  get_dataloaders(lowercase__				,					lowercase__	)

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

						# Now we train the model
						for epoch in range(lowercase__	):
									model.train()
									for step, batch in enumerate(lowercase__	):
												# And perform gradient accumulation
												with accelerator.accumulate(lowercase__	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device	)
															a__					=  model(**lowercase__	)
															a__					=  outputs.loss
															accelerator.backward(lowercase__	)
															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`.
												batch.to(accelerator.device	)
												with torch.no_grad():
															a__					=  model(**lowercase__	)
												a__					=  outputs.logits.argmax(dim=-1	)
												a__				,       a__					=  accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
												metric.add_batch(
												    predictions=lowercase__				,					references=lowercase__				,					)

									a__					=  metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f'epoch {epoch}:'				,					lowercase__	)

    # 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    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  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."""	)
			a__					=  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
			a__					=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 256}
			training_function(lowercase__				,					lowercase__	)


if __name__ == "__main__":
	main()






'''simple docstring'''



lowerCAmelCase						:		Any						      = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase						:		Any						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase						:		str						      = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}