





# This model implementation is heavily based on:

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,
    StableDiffusionControlNetInpaintPipeline,
    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 (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class 						_A( snake_case__    ,			snake_case__    ,			snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  List[Any]    =     StableDiffusionControlNetInpaintPipeline
				UpperCamelCase   :  Tuple    =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase   :  Any    =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCamelCase   :  str    =     frozenset({'''control_image'''}       )  # skip `image` and `mask` for now, only test for control_image
				UpperCamelCase   :  List[Any]    =     TEXT_TO_IMAGE_IMAGE_PARAMS



				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						List[str]          =    UNetaDConditionModel(
									    block_out_channels=(32, 64)					, layers_per_block=2					, sample_size=32					, in_channels=9					, out_channels=4					, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					, cross_attention_dim=32					, )
									torch.manual_seed(0					)
									__A     :						Union[str, 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					)
									__A     :						str          =    DDIMScheduler(
									    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
									torch.manual_seed(0					)
									__A     :						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					)
									__A     :						int          =    CLIPTextConfig(
									    bos_token_id=0					, eos_token_id=2					, hidden_size=32					, intermediate_size=37					, layer_norm_eps=1e-0_5					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, )
									__A     :						List[Any]          =    CLIPTextModel(_A					)
									__A     :						Dict          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

									__A     :						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					, _A					, _A=0					):
									if str(_A					).startswith('mps'					):
														__A     :						Dict          =    torch.manual_seed(_A					)
									else:
														__A     :						Union[str, Any]          =    torch.Generator(device=_A					).manual_seed(_A					)

									__A     :						Optional[Any]          =    2
									__A     :						int          =    randn_tensor(
									    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					, generator=_A					, device=torch.device(_A					)					, )
									__A     :						Optional[int]          =    floats_tensor((1, 3, 32, 32)					, rng=random.Random(_A					)					).to(_A					)
									__A     :						Optional[Any]          =    init_image.cpu().permute(0					, 2					, 3					, 1					)[0]

									__A     :						Dict          =    Image.fromarray(np.uinta(_A					)					).convert('RGB'					).resize((64, 64)					)
									__A     :						int          =    Image.fromarray(np.uinta(init_image + 4					)					).convert('RGB'					).resize((64, 64)					)

									__A     :						str          =    {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									    'image': image,
									    'mask_image': mask_image,
									    'control_image': control_image,
									}

									return inputs



				def  UpperCAmelCase_		(      self					):
									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					):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)





				def  UpperCAmelCase_		(      self					):
									self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  str    =     StableDiffusionControlNetInpaintPipeline
				UpperCamelCase   :  Dict    =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase   :  Optional[int]    =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				UpperCamelCase   :  str    =     frozenset([]       )



				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						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					)
									__A     :						Dict          =    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					)
									__A     :						Optional[Any]          =    DDIMScheduler(
									    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
									torch.manual_seed(0					)
									__A     :						List[Any]          =    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					)
									__A     :						Tuple          =    CLIPTextConfig(
									    bos_token_id=0					, eos_token_id=2					, hidden_size=32					, intermediate_size=37					, layer_norm_eps=1e-0_5					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, )
									__A     :						List[str]          =    CLIPTextModel(_A					)
									__A     :						List[Any]          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

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





class 						_A( snake_case__    ,			snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[int]    =     StableDiffusionControlNetInpaintPipeline
				UpperCamelCase   :  Any    =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				UpperCamelCase   :  str    =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



				def  UpperCAmelCase_		(      self					):
									torch.manual_seed(0					)
									__A     :						List[str]          =    UNetaDConditionModel(
									    block_out_channels=(32, 64)					, layers_per_block=2					, sample_size=32					, in_channels=9					, out_channels=4					, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					, up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					, cross_attention_dim=32					, )
									torch.manual_seed(0					)

									def init_weights(_A					):
														if isinstance(_A					, torch.nn.Convad					):
																			torch.nn.init.normal(m.weight					)
																			m.bias.data.fill_(1.0					)

									__A     :						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(_A					)

									torch.manual_seed(0					)
									__A     :						int          =    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(_A					)

									torch.manual_seed(0					)
									__A     :						Any          =    DDIMScheduler(
									    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
									torch.manual_seed(0					)
									__A     :						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					)
									__A     :						Dict          =    CLIPTextConfig(
									    bos_token_id=0					, eos_token_id=2					, hidden_size=32					, intermediate_size=37					, layer_norm_eps=1e-0_5					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, )
									__A     :						Dict          =    CLIPTextModel(_A					)
									__A     :						Optional[int]          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

									__A     :						Tuple          =    MultiControlNetModel([controlneta, controlneta]					)

									__A     :						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					, _A					, _A=0					):
									if str(_A					).startswith('mps'					):
														__A     :						Union[str, Any]          =    torch.manual_seed(_A					)
									else:
														__A     :						Optional[int]          =    torch.Generator(device=_A					).manual_seed(_A					)

									__A     :						Dict          =    2

									__A     :						Dict          =    [
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					, generator=_A					, device=torch.device(_A					)					, ),
									    randn_tensor(
									        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					, generator=_A					, device=torch.device(_A					)					, ),
									]
									__A     :						Tuple          =    floats_tensor((1, 3, 32, 32)					, rng=random.Random(_A					)					).to(_A					)
									__A     :						Optional[int]          =    init_image.cpu().permute(0					, 2					, 3					, 1					)[0]

									__A     :						Dict          =    Image.fromarray(np.uinta(_A					)					).convert('RGB'					).resize((64, 64)					)
									__A     :						Any          =    Image.fromarray(np.uinta(init_image + 4					)					).convert('RGB'					).resize((64, 64)					)

									__A     :						Optional[int]          =    {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									    'image': image,
									    'mask_image': mask_image,
									    'control_image': control_image,
									}

									return inputs



				def  UpperCAmelCase_		(      self					):
									__A     :						Dict          =    self.get_dummy_components()
									__A     :						Optional[Any]          =    self.pipeline_class(**_A					)
									pipe.to(_A					)

									__A     :						Dict          =    1_0.0
									__A     :						Tuple          =    4

									__A     :						Any          =    self.get_dummy_inputs(_A					)
									__A     :						Tuple          =    steps
									__A     :						Optional[int]          =    scale
									__A     :						Tuple          =    pipe(**_A					)[0]

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

									__A     :						Optional[int]          =    self.get_dummy_inputs(_A					)
									__A     :						List[Any]          =    steps
									__A     :						List[Any]          =    scale
									__A     :						str          =    pipe(**_A					, control_guidance_start=[0.1, 0.3]					, control_guidance_end=[0.2, 0.7]					)[0]

									__A     :						int          =    self.get_dummy_inputs(_A					)
									__A     :						str          =    steps
									__A     :						Tuple          =    scale
									__A     :						Union[str, Any]          =    pipe(**_A					, 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					):
									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					):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



				def  UpperCAmelCase_		(      self					):
									self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





				def  UpperCAmelCase_		(      self					):
									__A     :						List[str]          =    self.get_dummy_components()
									__A     :						Any          =    self.pipeline_class(**_A					)
									pipe.to(_A					)
									pipe.set_progress_bar_config(disable=_A					)
									with tempfile.TemporaryDirectory() as tmpdir:
														try:
																			# save_pretrained is not implemented for Multi-ControlNet
																			pipe.save_pretrained(_A					)
														except NotImplementedError:
																			pass





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


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



				def  UpperCAmelCase_		(      self					):
									__A     :						Any          =    ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'					)

									__A     :						List[Any]          =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-inpainting'					, safety_checker=_A					, controlnet=_A					)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=_A					)

									__A     :						Union[str, Any]          =    torch.Generator(device='cpu'					).manual_seed(0					)
									__A     :						Dict          =    load_image(
									    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'					).resize((512, 512)					)

									__A     :						Dict          =    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
									    '/stable_diffusion_inpaint/input_bench_mask.png'					).resize((512, 512)					)

									__A     :						Union[str, Any]          =    'pitch black hole'

									__A     :						Tuple          =    load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'					).resize((512, 512)					)

									__A     :						Optional[int]          =    pipe(
									    _A					, image=_A					, mask_image=_A					, control_image=_A					, generator=_A					, output_type='np'					, num_inference_steps=3					, )

									__A     :						int          =    output.images[0]

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

									__A     :						List[str]          =    load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'					)

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





				def  UpperCAmelCase_		(      self					):
									__A     :						int          =    ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'					)

									__A     :						Any          =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5'					, safety_checker=_A					, controlnet=_A					)
									__A     :						List[str]          =    DDIMScheduler.from_config(pipe.scheduler.config					)
									pipe.enable_model_cpu_offload()
									pipe.set_progress_bar_config(disable=_A					)

									__A     :						Dict          =    torch.Generator(device='cpu'					).manual_seed(33					)

									__A     :						Union[str, Any]          =    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'					)
									__A     :						str          =    init_image.resize((512, 512)					)

									__A     :						str          =    load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'					)
									__A     :						List[Any]          =    mask_image.resize((512, 512)					)

									__A     :						Any          =    'a handsome man with ray-ban sunglasses'

									def make_inpaint_condition(_A					, _A					):
														__A     :						List[Any]          =    np.array(image.convert('RGB'					)					).astype(np.floataa					) / 2_5_5.0
														__A     :						int          =    np.array(image_mask.convert('L'					)					).astype(np.floataa					) / 2_5_5.0

														assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
														__A     :						Any          =    -1.0  # set as masked pixel
														__A     :						Any          =    np.expand_dims(_A					, 0					).transpose(0					, 3					, 1					, 2					)
														__A     :						Optional[int]          =    torch.from_numpy(_A					)
														return image

									__A     :						List[str]          =    make_inpaint_condition(_A					, _A					)

									__A     :						Union[str, Any]          =    pipe(
									    _A					, image=_A					, mask_image=_A					, control_image=_A					, guidance_scale=9.0					, eta=1.0					, generator=_A					, num_inference_steps=20					, output_type='np'					, )
									__A     :						Dict          =    output.images[0]

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

									__A     :						Union[str, Any]          =    load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'					)

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











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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

UpperCAmelCase						:       List[Any]        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 16  )				->				List[Any]:
					__A     :						List[str]          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						int          =    load_dataset('glue' ,						'mrpc'  )

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

					def collate_fn(a  ):
										# 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     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

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

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

					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

				UpperCAmelCase						:       Tuple        =					mocked_dataloaders  # noqa: F811







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

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

										# 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     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * 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     :						Tuple          =    accelerator.prepare(
										    a ,						a ,						a ,						a ,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.loss
																									accelerator.backward(a  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

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

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # 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 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						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     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




