

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_       	=	16
lowerCAmelCase_       	=	32


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = 16       ):
						snake_case_   =	AutoTokenizer.from_pretrained('''bert-base-cased'''       )
						snake_case_   =	load_dataset('''glue'''  ,		'''mrpc'''       )

						def tokenize_function(SCREAMING_SNAKE_CASE__       ):
												# max_length=None => use the model max length (it's actually the default)
												snake_case_   =	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():
												snake_case_   =	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
						snake_case_   =	tokenized_datasets.rename_column('''label'''  ,		'''labels'''       )

						def collate_fn(SCREAMING_SNAKE_CASE__       ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case_   =	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":
																		snake_case_   =	16
												elif accelerator.mixed_precision != "no":
																		snake_case_   =	8
												else:
																		snake_case_   =	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.
						snake_case_   =	DataLoader(
						    tokenized_datasets['''train''']  ,		shuffle=SCREAMING_SNAKE_CASE__  ,		collate_fn=SCREAMING_SNAKE_CASE__  ,		batch_size=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,		SCREAMING_SNAKE_CASE__       ) == "1":
												snake_case_   =	2
						# Initialize accelerator
						snake_case_   =	Accelerator(cpu=args.cpu  ,		mixed_precision=args.mixed_precision       )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case_   =	config['''lr''']
						snake_case_   =	int(config['''num_epochs''']       )
						snake_case_   =	int(config['''seed''']       )
						snake_case_   =	int(config['''batch_size''']       )

						snake_case_   =	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(SCREAMING_SNAKE_CASE__       ):
												# 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)
												snake_case_   =	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).
												snake_case_   =	model.to(accelerator.device       )

												# Instantiate optimizer
												snake_case_   =	AdamW(params=model.parameters()  ,		lr=SCREAMING_SNAKE_CASE__       )
												snake_case_,    snake_case_   =	get_dataloaders(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

												# Instantiate scheduler
												snake_case_   =	get_linear_schedule_with_warmup(
												    optimizer=SCREAMING_SNAKE_CASE__  ,		num_warmup_steps=100  ,		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.
												snake_case_,    snake_case_,    snake_case_,    snake_case_,    snake_case_   =	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       )
																								snake_case_   =	model(**SCREAMING_SNAKE_CASE__       )
																								snake_case_   =	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():
																														snake_case_   =	model(**SCREAMING_SNAKE_CASE__       )
																								snake_case_   =	outputs.logits.argmax(dim=-1       )
																								snake_case_,    snake_case_   =	accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
																								metric.add_batch(
																								    predictions=SCREAMING_SNAKE_CASE__  ,		references=SCREAMING_SNAKE_CASE__  ,		)

																		snake_case_   =	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   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	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.'''       )
						snake_case_   =	parser.parse_args()
						snake_case_   =	{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					main()


from ..utils import DummyObject, requires_backends





class 	snake_case_   (		metaclass=__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				["note_seq"]







			def __init__(     self :				Optional[int]							,   *_UpperCamelCase :				str							,   **_UpperCamelCase :				Optional[int]							)      ->Any:
									requires_backends(self							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				int							,   *_UpperCamelCase :				Any							,   **_UpperCamelCase :				List[Any]							)      ->int:
									requires_backends(cls							,   ['''note_seq''']							)







			@classmethod
			def 				snake_case__(     cls :				Dict							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				Union[str, Any]							)      ->List[str]:
									requires_backends(cls							,   ['''note_seq''']							)