

'''simple docstring'''





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


lowerCAmelCase__								=		1_6
lowerCAmelCase__								=		3_2






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     str = 1_6	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						AutoTokenizer.from_pretrained('bert-base-cased'	)
	_UpperCamelCase   :		Union[str, Any]			=						load_dataset('glue'							,       'mrpc'	)

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

	def collate_fn(UpperCAmelCase_							:     int	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_UpperCamelCase   :		Dict			=						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":
			_UpperCamelCase   :		Optional[Any]			=						1_6
		elif accelerator.mixed_precision != "no":
			_UpperCamelCase   :		Dict			=						8
		else:
			_UpperCamelCase   :		Any			=						None

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

	# Instantiate dataloaders.
	_UpperCamelCase   :		Union[str, Any]			=						DataLoader(
	    tokenized_datasets['train']							,       shuffle=_SCREAMING_SNAKE_CASE							,       collate_fn=_SCREAMING_SNAKE_CASE							,       batch_size=_SCREAMING_SNAKE_CASE	)
	_UpperCamelCase   :		int			=						DataLoader(
	    tokenized_datasets['validation']							,       shuffle=_SCREAMING_SNAKE_CASE							,       collate_fn=_SCREAMING_SNAKE_CASE							,       batch_size=_SCREAMING_SNAKE_CASE	)

	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

							lowerCAmelCase__								=		mocked_dataloaders  # noqa: F811






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     List[Any]	)						->				Dict:




	'''simple docstring'''




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

	_UpperCamelCase   :		int			=						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=_SCREAMING_SNAKE_CASE	)
	def inner_training_loop(UpperCAmelCase_							:     Any	):
		# 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(_SCREAMING_SNAKE_CASE	)

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

		# 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   :		str			=						model.to(accelerator.device	)

		# Instantiate optimizer
		_UpperCamelCase   :		Tuple			=						AdamW(params=model.parameters()							,       lr=_SCREAMING_SNAKE_CASE	)
		_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						get_dataloaders(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE	)

		# Instantiate scheduler
		_UpperCamelCase   :		str			=						get_linear_schedule_with_warmup(
		    optimizer=_SCREAMING_SNAKE_CASE							,       num_warmup_steps=1_0_0							,       num_training_steps=(len(_SCREAMING_SNAKE_CASE	) * 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.
		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		str			=						accelerator.prepare(
		    _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE	)

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

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

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

		# 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 					lowerCamelCase_     (							)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		List[str]			=						argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument(
	    '--mixed_precision'							,       type=_SCREAMING_SNAKE_CASE							,       default=_SCREAMING_SNAKE_CASE							,       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.'	)
	_UpperCamelCase   :		Dict			=						parser.parse_args()
	_UpperCamelCase   :		Dict			=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(_SCREAMING_SNAKE_CASE							,       _SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
							main()







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




