




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


__UpperCamelCase   :			str          = 16
__UpperCamelCase   :			Any          = 32





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 16       ) ->			str:
	a									=				AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	a									=				load_dataset("""glue"""				,     """mrpc"""       )

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

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

		return tokenizer.pad(
		    __lowerCamelCase				,     padding="""longest"""				,     max_length=__lowerCamelCase				,     pad_to_multiple_of=__lowerCamelCase				,     return_tensors="""pt"""				,     )

	# Instantiate dataloaders.
	a									=				DataLoader(
	    tokenized_datasets["""train"""]				,     shuffle=__lowerCamelCase				,     collate_fn=__lowerCamelCase				,     batch_size=__lowerCamelCase				,     drop_last=__lowerCamelCase       )
	a									=				DataLoader(
	    tokenized_datasets["""validation"""]				,     shuffle=__lowerCamelCase				,     collate_fn=__lowerCamelCase				,     batch_size=__lowerCamelCase				,     drop_last=(accelerator.mixed_precision == """fp8""")				,     )

	return train_dataloader, eval_dataloader





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	# Initialize accelerator
	a									=				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	a									=				config["""lr"""]
	a									=				int(config["""num_epochs"""]       )
	a									=				int(config["""seed"""]       )
	a									=				int(config["""batch_size"""]       )

	a									=				evaluate.load("""glue"""				,     """mrpc"""       )

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

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

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

	# Instantiate scheduler
	a									=				get_linear_schedule_with_warmup(
	    optimizer=__lowerCamelCase				,     num_warmup_steps=100				,     num_training_steps=(len(__lowerCamelCase       ) * 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.

	a							,					a							,					a							,					a							,					a									=				accelerator.prepare(
	    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )

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

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

		a									=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'				,     __lowerCamelCase       )





def 						__A						(      ) ->			Any:
	a									=				argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument(
	    """--mixed_precision"""				,     type=__lowerCamelCase				,     default=__lowerCamelCase				,     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."""       )
	a									=				parser.parse_args()
	a									=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		main()







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

