


"""simple docstring"""







import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCamelCase_   :   Any									=  0b101_100_111_110_110_010_010_000_011_110_111_011_000_110_011_110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCamelCase_   :   List[Any]									=  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]






class       __A				:






 """simple docstring"""




 def __init__(		self						)					->     int:
     a 		=WATERMARK_BITS
     a 		=WatermarkEncoder()

     self.encoder.set_watermark('''bits''' ,    self.watermark						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     str:
     # can't encode images that are smaller than 256
     if images.shape[-1] < 256:
         return images

     a 		=(255 * (images / 2 + 0.5)).cpu().permute(0 ,    2 ,    3 ,    1						).float().numpy()

     a 		=[self.encoder.encode(__lowerCamelCase ,    '''dwtDct'''						) for image in images]

     a 		=torch.from_numpy(np.array(__lowerCamelCase						)						).permute(0 ,    3 ,    1 ,    2						)

     a 		=torch.clamp(2 * (images / 255 - 0.5) ,    min=-1.0 ,    max=1.0						)
     return images







import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


UpperCAmelCase_ :						Union[str, Any]      =      16
UpperCAmelCase_ :						int      =      32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Accelerator	,			__magic_name__					:  int = 16	,			__magic_name__					:  str = "bert-base-cased"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	AutoTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	load_dataset("""glue"""	,			"""mrpc"""					)

	def tokenize_function(__magic_name__					:  Tuple					):
		# max_length=None => use the model max length (it's actually the default)
		UpperCamelCase     :List[Any]          =	tokenizer(examples["""sentence1"""]	,			examples["""sentence2"""]	,			truncation=__magic_name__	,			max_length=__magic_name__					)
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	UpperCamelCase     :List[Any]          =	datasets.map(
	    __magic_name__	,			batched=__magic_name__	,			remove_columns=["""idx""", """sentence1""", """sentence2"""]	,			load_from_cache_file=__magic_name__					)

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCamelCase     :Optional[Any]          =	tokenized_datasets.rename_column("""label"""	,			"""labels"""					)

	def collate_fn(__magic_name__					:  Any					):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(__magic_name__	,			padding="""max_length"""	,			max_length=128	,			return_tensors="""pt"""					)
		return tokenizer.pad(__magic_name__	,			padding="""longest"""	,			return_tensors="""pt"""					)

	# Instantiate dataloaders.
	UpperCamelCase     :List[str]          =	DataLoader(
	    tokenized_datasets["""train"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)
	UpperCamelCase     :List[Any]          =	DataLoader(
	    tokenized_datasets["""validation"""]	,			shuffle=__magic_name__	,			collate_fn=__magic_name__	,			batch_size=__magic_name__					)

	return train_dataloader, eval_dataloader

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[Any]					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCamelCase     :Union[str, Any]          =	config["""lr"""]
	UpperCamelCase     :List[str]          =	int(config["""num_epochs"""]					)
	UpperCamelCase     :str          =	int(config["""seed"""]					)
	UpperCamelCase     :Dict          =	int(config["""batch_size"""]					)
	UpperCamelCase     :Union[str, Any]          =	args.model_name_or_path

	set_seed(__magic_name__					)
	UpperCamelCase     ,      UpperCamelCase     :Dict          =	get_dataloaders(__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCamelCase     :List[str]          =	AutoModelForSequenceClassification.from_pretrained(__magic_name__	,			return_dict=__magic_name__					)

	# Instantiate optimizer
	UpperCamelCase     :Union[str, Any]          =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCamelCase     :Optional[Any]          =	optimizer_cls(params=model.parameters()	,			lr=__magic_name__					)

	if accelerator.state.deepspeed_plugin is not None:
		UpperCamelCase     :Any          =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		UpperCamelCase     :Any          =	1
	UpperCamelCase     :Dict          =	(len(__magic_name__					) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		UpperCamelCase     :List[Any]          =	get_linear_schedule_with_warmup(
		    optimizer=__magic_name__	,			num_warmup_steps=0	,			num_training_steps=__magic_name__	,			)
	else:
		UpperCamelCase     :Any          =	DummyScheduler(__magic_name__	,			total_num_steps=__magic_name__	,			warmup_num_steps=0					)

	# 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(
	    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# We need to keep track of how many total steps we have iterated over
	UpperCamelCase     :int          =	0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCamelCase     :Tuple          =	0

	# Now we train the model
	UpperCamelCase     :Any          =	evaluate.load("""glue"""	,			"""mrpc"""					)
	UpperCamelCase     :Tuple          =	0
	UpperCamelCase     :List[Any]          =	{}
	for epoch in range(__magic_name__	,			__magic_name__					):
		model.train()
		for step, batch in enumerate(__magic_name__					):
			UpperCamelCase     :List[str]          =	model(**__magic_name__					)
			UpperCamelCase     :Dict          =	outputs.loss
			UpperCamelCase     :Optional[int]          =	loss / gradient_accumulation_steps
			accelerator.backward(__magic_name__					)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		UpperCamelCase     :str          =	0
		for step, batch in enumerate(__magic_name__					):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device					)
			with torch.no_grad():
				UpperCamelCase     :Optional[int]          =	model(**__magic_name__					)
			UpperCamelCase     :List[Any]          =	outputs.logits.argmax(dim=-1					)
			# It is slightly faster to call this once, than multiple times
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	accelerator.gather(
			    (predictions, batch["""labels"""])					)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(__magic_name__					) - 1:
					UpperCamelCase     :Dict          =	predictions[: len(eval_dataloader.dataset					) - samples_seen]
					UpperCamelCase     :List[str]          =	references[: len(eval_dataloader.dataset					) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=__magic_name__	,			references=__magic_name__	,			)

		UpperCamelCase     :List[str]          =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,			__magic_name__					)
		UpperCamelCase     :Dict          =	eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			UpperCamelCase     :str          =	eval_metric["""accuracy"""]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,			"""all_results.json"""					)	,			"""w"""					) as f:
			json.dump(__magic_name__	,			__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""					)
	parser.add_argument(
	    """--model_name_or_path"""	,			type=__magic_name__	,			default="""bert-base-cased"""	,			help="""Path to pretrained model or model identifier from huggingface.co/models."""	,			required=__magic_name__	,			)
	parser.add_argument(
	    """--output_dir"""	,			type=__magic_name__	,			default="""."""	,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,			)
	parser.add_argument(
	    """--performance_lower_bound"""	,			type=__magic_name__	,			default=__magic_name__	,			help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""	,			)
	parser.add_argument(
	    """--num_epochs"""	,			type=__magic_name__	,			default=3	,			help="""Number of train epochs."""	,			)
	UpperCamelCase     :str          =	parser.parse_args()
	UpperCamelCase     :Any          =	{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(__magic_name__	,			__magic_name__					)


if __name__ == "__main__":
	main()
