



"""simple docstring"""







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class        __A       (	A_     ,A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Union[str, Any]   	=				StableDiffusionControlNetImgaImgPipeline
		lowerCAmelCase			:					Any   	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		lowerCAmelCase			:					List[str]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Tuple   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}    )
		lowerCAmelCase			:					int   	=				IMAGE_TO_IMAGE_IMAGE_PARAMS







		def 					UpperCAmelCase    (	self						:  int						)					->		Union[str, Any]:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    Optional[int]				=			UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,cross_attention_dim=32			,)
				torch.manual_seed(0						)
				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			DDIMScheduler(
				    beta_start=0.0_0085			,beta_end=0.012			,beta_schedule='''scaled_linear'''			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,latent_channels=4			,)
				torch.manual_seed(0						)
				lowercase__   :    Optional[Any]				=			CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1_000			,)
				lowercase__   :    List[Any]				=			CLIPTextModel(_snake_case						)
				lowercase__   :    Union[str, Any]				=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

				lowercase__   :    Dict				=			{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components







		def 					UpperCAmelCase    (	self						:  Any			,_snake_case						:  List[Any]			,_snake_case						:  Any=0						)					->		Any:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    Optional[Any]				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    str				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    List[Any]				=			2
				lowercase__   :    Optional[int]				=			randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,)
				lowercase__   :    str				=			floats_tensor(control_image.shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Optional[Any]				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[Any]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    Union[str, Any]				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()			,reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,)
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)





class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Dict   	=				StableDiffusionControlNetImgaImgPipeline
		lowerCAmelCase			:					List[Any]   	=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		lowerCAmelCase			:					Union[str, Any]   	=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowerCAmelCase			:					Dict   	=				frozenset([]    )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				torch.manual_seed(0						)
				lowercase__   :    List[Any]				=			UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,cross_attention_dim=32			,)
				torch.manual_seed(0						)

				def init_weights(_snake_case						:  Optional[int]						):
						if isinstance(_snake_case			,torch.nn.Convad						):
								torch.nn.init.normal(m.weight						)
								m.bias.data.fill_(1.0						)

				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

				torch.manual_seed(0						)
				lowercase__   :    Any				=			ControlNetModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,in_channels=4			,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,cross_attention_dim=32			,conditioning_embedding_out_channels=(16, 32)			,)
				controlneta.controlnet_down_blocks.apply(_snake_case						)

				torch.manual_seed(0						)
				lowercase__   :    Dict				=			DDIMScheduler(
				    beta_start=0.0_0085			,beta_end=0.012			,beta_schedule='''scaled_linear'''			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0						)
				lowercase__   :    List[str]				=			AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,latent_channels=4			,)
				torch.manual_seed(0						)
				lowercase__   :    List[Any]				=			CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1e-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=1_000			,)
				lowercase__   :    int				=			CLIPTextModel(_snake_case						)
				lowercase__   :    Union[str, Any]				=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

				lowercase__   :    int				=			MultiControlNetModel([controlneta, controlneta]						)

				lowercase__   :    Optional[Any]				=			{
				    '''unet''': unet,
				    '''controlnet''': controlnet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  Dict			,_snake_case						:  Union[str, Any]=0						)					->		List[Any]:






				"""simple docstring"""



				if str(_snake_case						).startswith('''mps'''						):
						lowercase__   :    int				=			torch.manual_seed(_snake_case						)
				else:
						lowercase__   :    Dict				=			torch.Generator(device=_snake_case						).manual_seed(_snake_case						)

				lowercase__   :    int				=			2

				lowercase__   :    Optional[Any]				=			[
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,generator=_snake_case			,device=torch.device(_snake_case						)			,),
				]

				lowercase__   :    Dict				=			floats_tensor(control_image[0].shape			,rng=random.Random(_snake_case						)						).to(_snake_case						)
				lowercase__   :    Dict				=			image.cpu().permute(0			,2			,3			,1						)[0]
				lowercase__   :    Optional[int]				=			Image.fromarray(np.uinta(_snake_case						)						).convert('''RGB'''						).resize((64, 64)						)
				lowercase__   :    Any				=			{
				    '''prompt''': '''A painting of a squirrel eating a burger''',
				    '''generator''': generator,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				    '''image''': image,
				    '''control_image''': control_image,
				}

				return inputs







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    Dict				=			self.get_dummy_components()
				lowercase__   :    Dict				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)

				lowercase__   :    Optional[Any]				=			10.0
				lowercase__   :    Tuple				=			4

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[Any]				=			steps
				lowercase__   :    Any				=			scale
				lowercase__   :    Optional[Any]				=			pipe(**_snake_case						)[0]

				lowercase__   :    List[str]				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    int				=			scale
				lowercase__   :    List[str]				=			pipe(**_snake_case			,control_guidance_start=0.1			,control_guidance_end=0.2						)[0]

				lowercase__   :    int				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    Optional[int]				=			steps
				lowercase__   :    Dict				=			scale
				lowercase__   :    Dict				=			pipe(**_snake_case			,control_guidance_start=[0.1, 0.3]			,control_guidance_end=[0.2, 0.7]						)[0]

				lowercase__   :    Dict				=			self.get_dummy_inputs(_snake_case						)
				lowercase__   :    List[Any]				=			steps
				lowercase__   :    Optional[int]				=			scale
				lowercase__   :    List[Any]				=			pipe(**_snake_case			,control_guidance_start=0.4			,control_guidance_end=[0.5, 0.8]						)[0]

				# make sure that all outputs are different
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3
				assert np.sum(np.abs(output_a - output_a						)						) > 1e-3







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[int]:






				"""simple docstring"""



				return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3						)







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()			,reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				self._test_inference_batch_single_identical(expected_max_diff=2e-3						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			self.get_dummy_components()
				lowercase__   :    Optional[Any]				=			self.pipeline_class(**_snake_case						)
				pipe.to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)
				with tempfile.TemporaryDirectory() as tmpdir:
						try:
								# save_pretrained is not implemented for Multi-ControlNet
								pipe.save_pretrained(_snake_case						)
						except NotImplementedError:
								pass





@slow
@require_torch_gpu
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Any						)					->		int:






				"""simple docstring"""



				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    int				=			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''						)

				lowercase__   :    Any				=			StableDiffusionControlNetImgaImgPipeline.from_pretrained(
				    '''runwayml/stable-diffusion-v1-5'''			,safety_checker=_snake_case			,controlnet=_snake_case						)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=_snake_case						)

				lowercase__   :    Optional[Any]				=			torch.Generator(device='''cpu'''						).manual_seed(0						)
				lowercase__   :    List[str]				=			'''evil space-punk bird'''
				lowercase__   :    Optional[Any]				=			load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''						).resize((512, 512)						)
				lowercase__   :    Tuple				=			load_image(
				    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''						).resize((512, 512)						)

				lowercase__   :    List[Any]				=			pipe(
				    _snake_case			,_snake_case			,control_image=_snake_case			,generator=_snake_case			,output_type='''np'''			,num_inference_steps=50			,strength=0.6			,)

				lowercase__   :    List[Any]				=			output.images[0]

				assert image.shape == (512, 512, 3)

				lowercase__   :    Dict				=			load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''						)

				assert np.abs(expected_image - image						).max() < 9e-2





"""simple docstring"""







import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCAmelCase_   						=   16
lowerCAmelCase_   						=   32



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		lowercase__   :    Optional[Any]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    int				=			load_dataset('''glue'''						,     '''mrpc'''	)

		def tokenize_function(__lowerCamelCase	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__   :    str				=			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():
				lowercase__   :    str				=			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
		lowercase__   :    Union[str, Any]				=			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.
				lowercase__   :    List[str]				=			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":
						lowercase__   :    Optional[int]				=			16
				elif accelerator.mixed_precision != "no":
						lowercase__   :    List[Any]				=			8
				else:
						lowercase__   :    int				=			None

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

		# Instantiate dataloaders.
		lowercase__   :    List[Any]				=			DataLoader(
		    tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)
		lowercase__   :    str				=			DataLoader(
		    tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=__lowerCamelCase	)

		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

							lowerCAmelCase_   						=   mocked_dataloaders  # noqa: F811



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,     __lowerCamelCase	) == "1":
				lowercase__   :    List[Any]				=			2
		# Initialize accelerator
		lowercase__   :    Optional[int]				=			Accelerator(cpu=args.cpu						,     mixed_precision=args.mixed_precision	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__   :    str				=			config['''lr''']
		lowercase__   :    str				=			int(config['''num_epochs''']	)
		lowercase__   :    Optional[int]				=			int(config['''seed''']	)
		lowercase__   :    Tuple				=			int(config['''batch_size''']	)

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

		# New Code #
		# We now can define an inner training loop function. It should take a batch size as the only parameter,
		# and build the dataloaders in there.
		# It also gets our decorator
		@find_executable_batch_size(starting_batch_size=__lowerCamelCase	)
		def inner_training_loop(__lowerCamelCase	):
				# And now just move everything below under this function
				# We need to bring in the Accelerator object from earlier
				nonlocal accelerator
				# And reset all of its attributes that could hold onto any memory:
				accelerator.free_memory()

				# Then we can declare the model, optimizer, and everything else:
				set_seed(__lowerCamelCase	)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase__   :    List[str]				=			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).
				lowercase__   :    Tuple				=			model.to(accelerator.device	)

				# Instantiate optimizer
				lowercase__   :    List[str]				=			AdamW(params=model.parameters()						,     lr=__lowerCamelCase	)
				lowercase__		,			lowercase__   :    List[Any]				=			get_dataloaders(__lowerCamelCase						,     __lowerCamelCase	)

				# Instantiate scheduler
				lowercase__   :    Optional[int]				=			get_linear_schedule_with_warmup(
				    optimizer=__lowerCamelCase						,     num_warmup_steps=1_00						,     num_training_steps=(len(__lowerCamelCase	) * 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.
				lowercase__		,			lowercase__		,			lowercase__		,			lowercase__		,			lowercase__   :    Optional[int]				=			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	)
								lowercase__   :    Dict				=			model(**__lowerCamelCase	)
								lowercase__   :    List[Any]				=			outputs.loss
								accelerator.backward(__lowerCamelCase	)
								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():
										lowercase__   :    Tuple				=			model(**__lowerCamelCase	)
								lowercase__   :    Any				=			outputs.logits.argmax(dim=-1	)
								lowercase__		,			lowercase__   :    int				=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
								metric.add_batch(
								    predictions=__lowerCamelCase						,     references=__lowerCamelCase						,     )

						lowercase__   :    List[Any]				=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"""epoch {epoch}:"""						,     __lowerCamelCase	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
		inner_training_loop()



def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    Optional[int]				=			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.'''	)
		lowercase__   :    int				=			parser.parse_args()
		lowercase__   :    Union[str, Any]				=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__lowerCamelCase						,     __lowerCamelCase	)


if __name__ == "__main__":
							main()
