
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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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
#
########################################################################


_SCREAMING_SNAKE_CASE    :      Tuple          =			1_6
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			3_2

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Accelerator							,						__UpperCamelCase :						int = 16       ) ->       Optional[Any]:

				"""simple docstring"""







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

				def tokenize_function(__UpperCamelCase :						Optional[int]       ):
								# max_length=None => use the model max length (it's actually the default)
								A__  :	int         =							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():
								A__  :	str         =							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
				A__  :	Union[str, Any]         =							tokenized_datasets.rename_column('''label'''							,						'''labels'''       )

				def collate_fn(__UpperCamelCase :						Union[str, Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__  :	Union[str, Any]         =							1_28 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												A__  :	Union[str, Any]         =							16
								elif accelerator.mixed_precision != "no":
												A__  :	Dict         =							8
								else:
												A__  :	List[Any]         =							None

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

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

					_SCREAMING_SNAKE_CASE    :      Optional[int]          =			mocked_dataloaders  # noqa: F811

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple							,						__UpperCamelCase :						Optional[int]       ) ->       int:

				"""simple docstring"""







				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,						__UpperCamelCase       ) == "1":
								A__  :	Tuple         =							2
				# New Code #
				A__  :	Optional[int]         =							int(args.gradient_accumulation_steps       )
				A__  :	List[Any]         =							int(args.local_sgd_steps       )
				# Initialize accelerator
				A__  :	Tuple         =							Accelerator(
				    cpu=args.cpu							,						mixed_precision=args.mixed_precision							,						gradient_accumulation_steps=__UpperCamelCase       )
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__  :	Tuple         =							config['''lr''']
				A__  :	List[Any]         =							int(config['''num_epochs''']       )
				A__  :	int         =							int(config['''seed''']       )
				A__  :	List[Any]         =							int(config['''batch_size''']       )

				A__  :	List[Any]         =							evaluate.load('''glue'''							,						'''mrpc'''       )

				set_seed(__UpperCamelCase       )
				A__       ,							A__  :	List[Any]         =							get_dataloaders(__UpperCamelCase							,						__UpperCamelCase       )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				A__  :	Dict         =							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).
				A__  :	List[Any]         =							model.to(accelerator.device       )

				# Instantiate optimizer
				A__  :	Dict         =							AdamW(params=model.parameters()							,						lr=__UpperCamelCase       )

				# Instantiate scheduler
				A__  :	Optional[int]         =							get_linear_schedule_with_warmup(
				    optimizer=__UpperCamelCase							,						num_warmup_steps=1_00							,						num_training_steps=(len(__UpperCamelCase       ) * 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__  :	Dict         =							accelerator.prepare(
				    __UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# Now we train the model
				for epoch in range(__UpperCamelCase       ):
								model.train()
								with LocalSGD(
								    accelerator=__UpperCamelCase							,						model=__UpperCamelCase							,						local_sgd_steps=__UpperCamelCase							,						enabled=local_sgd_steps is not None       ) as local_sgd:
												for step, batch in enumerate(__UpperCamelCase       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(__UpperCamelCase       ):
																				A__  :	Tuple         =							model(**__UpperCamelCase       )
																				A__  :	str         =							output.loss
																				accelerator.backward(__UpperCamelCase       )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								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():
																A__  :	str         =							model(**__UpperCamelCase       )
												A__  :	Dict         =							outputs.logits.argmax(dim=-1       )
												A__       ,							A__  :	int         =							accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
												metric.add_batch(
												    predictions=__UpperCamelCase							,						references=__UpperCamelCase							,						)

								A__  :	List[str]         =							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      ) ->       int:

				"""simple docstring"""







				A__  :	List[Any]         =							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.'''							,						)
				# New Code #
				parser.add_argument(
				    '''--gradient_accumulation_steps'''							,						type=__UpperCamelCase							,						default=1							,						help='''The number of minibatches to be ran before gradients are accumulated.'''							,						)
				parser.add_argument(
				    '''--local_sgd_steps'''							,						type=__UpperCamelCase							,						default=8							,						help='''Number of local SGD steps or None to disable local SGD'''       )
				parser.add_argument('''--cpu'''							,						action='''store_true'''							,						help='''If passed, will train on the CPU.'''       )
				A__  :	Optional[int]         =							parser.parse_args()
				A__  :	Optional[Any]         =							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__UpperCamelCase							,						__UpperCamelCase       )


if __name__ == "__main__":
					main()

from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')