

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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__ = 1_6			)  ->     Optional[int]:
	__lowerCamelCase				:				Optional[Any]					       = AutoTokenizer.from_pretrained('bert-base-cased'			)
	__lowerCamelCase				:				int					       = load_dataset('glue'	, 'mrpc'			)

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

	def collate_fn(lowerCamelCase__			):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__lowerCamelCase				:				List[str]					       = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			__lowerCamelCase				:				Optional[int]					       = 1_6
		elif accelerator.mixed_precision != "no":
			__lowerCamelCase				:				List[Any]					       = 8
		else:
			__lowerCamelCase				:				Any					       = None

		return tokenizer.pad(
		    lowerCamelCase__	, padding='longest'	, max_length=lowerCamelCase__	, pad_to_multiple_of=lowerCamelCase__	, return_tensors='pt'	, )

	# Instantiate dataloaders.
	__lowerCamelCase				:				Any					       = DataLoader(
	    tokenized_datasets['train']	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__			)
	__lowerCamelCase				:				str					       = DataLoader(
	    tokenized_datasets['validation']	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__			)

	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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'	, lowerCamelCase__			) == "1":
		__lowerCamelCase				:				Union[str, Any]					       = 2
	# Initialize accelerator
	__lowerCamelCase				:				List[Any]					       = Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision			)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__lowerCamelCase				:				Tuple					       = config['lr']
	__lowerCamelCase				:				List[str]					       = int(config['num_epochs']			)
	__lowerCamelCase				:				List[Any]					       = int(config['seed']			)
	__lowerCamelCase				:				int					       = int(config['batch_size']			)

	__lowerCamelCase				:				List[str]					       = evaluate.load('glue'	, 'mrpc'			)

	# New Code #
	# We now can define an inner training loop function. It should take a batch size as the only parameter,
	# and build the dataloaders in there.
	# It also gets our decorator
	@find_executable_batch_size(starting_batch_size=lowerCamelCase__			)
	def inner_training_loop(lowerCamelCase__			):
		# And now just move everything below under this function
		# We need to bring in the Accelerator object from earlier
		nonlocal accelerator
		# And reset all of its attributes that could hold onto any memory:
		accelerator.free_memory()

		# Then we can declare the model, optimizer, and everything else:
		set_seed(lowerCamelCase__			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCamelCase				:				Optional[int]					       = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	, return_dict=lowerCamelCase__			)

		# 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).
		__lowerCamelCase				:				Union[str, Any]					       = model.to(accelerator.device			)

		# Instantiate optimizer
		__lowerCamelCase				:				Any					       = AdamW(params=model.parameters()	, lr=lowerCamelCase__			)
		__lowerCamelCase       , __lowerCamelCase				:				str					       = get_dataloaders(lowerCamelCase__	, lowerCamelCase__			)

		# Instantiate scheduler
		__lowerCamelCase				:				int					       = get_linear_schedule_with_warmup(
		    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__			) * 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.
		__lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase				:				Any					       = accelerator.prepare(
		    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)

		# Now we train the model
		for epoch in range(lowerCamelCase__			):
			model.train()
			for step, batch in enumerate(lowerCamelCase__			):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device			)
				__lowerCamelCase				:				Optional[Any]					       = model(**lowerCamelCase__			)
				__lowerCamelCase				:				Tuple					       = outputs.loss
				accelerator.backward(lowerCamelCase__			)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

			__lowerCamelCase				:				Optional[Any]					       = metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F"epoch {epoch}:"	, lowerCamelCase__			)

    # 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    SCREAMING_SNAKE_CASE__       (  )  ->     Union[str, Any]:
	__lowerCamelCase				:				Union[str, Any]					       = argparse.ArgumentParser(description='Simple example of training script.'			)
	parser.add_argument(
	    '--mixed_precision'	, type=lowerCamelCase__	, default=lowerCamelCase__	, 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.'			)
	__lowerCamelCase				:				Optional[int]					       = parser.parse_args()
	__lowerCamelCase				:				Optional[int]					       = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(lowerCamelCase__	, lowerCamelCase__			)


if __name__ == "__main__":
					main()











from ..utils import DummyObject, requires_backends




class     SCREAMING_SNAKE_CASE__ (						metaclass=UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''keras_nlp''']



 def __init__(							self,*__lowerCamelCase,**__lowerCamelCase ):
        requires_backends(self,['''keras_nlp'''] )





