





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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__A							:       Optional[int]         =  16
__A							:       str         =  32





def 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__     ,     UpperCamelCase__ = 16					)    ->							List[Any]:







			'''simple docstring'''

			UpperCAmelCase  						=							AutoTokenizer.from_pretrained('''bert-base-cased'''					)
			UpperCAmelCase  						=							load_dataset('''glue'''     ,     '''mrpc'''					)

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

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

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

			# Instantiate dataloaders.
			UpperCAmelCase  						=							DataLoader(
			    tokenized_datasets['''train''']     ,     shuffle=UpperCAmelCase_     ,     collate_fn=UpperCAmelCase_     ,     batch_size=UpperCAmelCase_					)
			UpperCAmelCase  						=							DataLoader(
			    tokenized_datasets['''validation''']     ,     shuffle=UpperCAmelCase_     ,     collate_fn=UpperCAmelCase_     ,     batch_size=UpperCAmelCase_					)

			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							:       Optional[Any]         =  mocked_dataloaders  # noqa: F811





def 		__SCREAMING_SNAKE_CASE    (							UpperCamelCase__     ,     UpperCamelCase__					)    ->							int:







			'''simple docstring'''

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

			UpperCAmelCase  						=							evaluate.load('''glue'''     ,     '''mrpc'''					)

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

			set_seed(UpperCAmelCase_					)
			UpperCAmelCase						,     UpperCAmelCase  						=							get_dataloaders(UpperCAmelCase_     ,     UpperCAmelCase_					)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase  						=							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,     return_dict=UpperCAmelCase_					)

			# 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  						=							model.to(accelerator.device					)

			# Instantiate optimizer
			UpperCAmelCase  						=							AdamW(params=model.parameters()     ,     lr=UpperCAmelCase_					)

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

			# Now we train the model
			for epoch in range(UpperCAmelCase_					):
						model.train()
						for step, batch in enumerate(UpperCAmelCase_					):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device					)
									UpperCAmelCase  						=							model(**UpperCAmelCase_					)
									UpperCAmelCase  						=							outputs.loss
									UpperCAmelCase  						=							loss / gradient_accumulation_steps
									accelerator.backward(UpperCAmelCase_					)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						UpperCAmelCase  						=							0
						for step, batch in enumerate(UpperCAmelCase_					):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device					)
									with torch.no_grad():
												UpperCAmelCase  						=							model(**UpperCAmelCase_					)
									UpperCAmelCase  						=							outputs.logits.argmax(dim=-1					)
									UpperCAmelCase						,     UpperCAmelCase  						=							accelerator.gather((predictions, batch['''labels'''])					)
									# New Code #
									# First we check if it's a distributed system
									if accelerator.use_distributed:
												# Then see if we're on the last batch of our eval dataloader
												if step == len(UpperCAmelCase_					) - 1:
															# Last batch needs to be truncated on distributed systems as it contains additional samples
															UpperCAmelCase  						=							predictions[: len(eval_dataloader.dataset					) - samples_seen]
															UpperCAmelCase  						=							references[: len(eval_dataloader.dataset					) - samples_seen]
												else:
															# Otherwise we add the number of samples seen
															samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
									metric.add_batch(
									    predictions=UpperCAmelCase_     ,     references=UpperCAmelCase_     ,     )

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





def 		__SCREAMING_SNAKE_CASE    (							)    ->							Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase  						=							argparse.ArgumentParser(description='''Simple example of training script.'''					)
			parser.add_argument(
			    '''--mixed_precision'''     ,     type=UpperCAmelCase_     ,     default=UpperCAmelCase_     ,     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  						=							parser.parse_args()
			UpperCAmelCase  						=							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(UpperCAmelCase_     ,     UpperCAmelCase_					)


if __name__ == "__main__":
				main()










'''simple docstring'''




def 						__lowerCamelCase			(	UpperCAmelCase_ = 10_00							)   ->int:
	return sum(e for e in range(3					,				UpperCAmelCase_							) if e % 3 == 0 or e % 5 == 0							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



