



import argparse

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


lowerCAmelCase__						:	List[str]					=16
lowerCAmelCase__						:	List[str]					=32






def       __lowercase  (					a__				,  a__ = 16  )							->					Optional[int]:
							__SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained('bert-base-cased'  )
							__SCREAMING_SNAKE_CASE										=   load_dataset('glue'				,  'mrpc'  )

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

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

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

							# Instantiate dataloaders.
							__SCREAMING_SNAKE_CASE										=   DataLoader(
							    tokenized_datasets['train']				,  shuffle=__lowercase				,  collate_fn=__lowercase				,  batch_size=__lowercase				,  drop_last=__lowercase  )
							__SCREAMING_SNAKE_CASE										=   DataLoader(
							    tokenized_datasets['validation']				,  shuffle=__lowercase				,  collate_fn=__lowercase				,  batch_size=__lowercase				,  drop_last=(accelerator.mixed_precision == 'fp8')				,  )

							return train_dataloader, eval_dataloader






def       __lowercase  (					a__				,  a__  )							->					Union[str, Any]:
							# Initialize accelerator
							__SCREAMING_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
							__SCREAMING_SNAKE_CASE										=   config['''lr''']
							__SCREAMING_SNAKE_CASE										=   int(config['num_epochs']  )
							__SCREAMING_SNAKE_CASE										=   int(config['seed']  )
							__SCREAMING_SNAKE_CASE										=   int(config['batch_size']  )

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

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

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

							# 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).
							__SCREAMING_SNAKE_CASE										=   model.to(accelerator.device  )
							# Instantiate optimizer
							__SCREAMING_SNAKE_CASE										=   AdamW(params=model.parameters()				,  lr=__lowercase  )

							# Instantiate scheduler
							__SCREAMING_SNAKE_CASE										=   get_linear_schedule_with_warmup(
							    optimizer=__lowercase				,  num_warmup_steps=1_00				,  num_training_steps=(len(__lowercase  ) * 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.

							__SCREAMING_SNAKE_CASE										=   accelerator.prepare(
							    __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase  )

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

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

														__SCREAMING_SNAKE_CASE										=   metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}:"""				,  __lowercase  )






def       __lowercase  (					)							->					Optional[int]:
							__SCREAMING_SNAKE_CASE										=   argparse.ArgumentParser(description='Simple example of training script.'  )
							parser.add_argument(
							    '--mixed_precision'				,  type=__lowercase				,  default=__lowercase				,  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.'  )
							__SCREAMING_SNAKE_CASE										=   parser.parse_args()
							__SCREAMING_SNAKE_CASE										=   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(__lowercase				,  __lowercase  )


if __name__ == "__main__":
			main()














from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

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


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



