




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					StableDiffusionLDMaDPipeline
				SCREAMING_SNAKE_CASE__					:							str	      =					TEXT_TO_IMAGE_PARAMS
				SCREAMING_SNAKE_CASE__					:							str	      =					TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE__					:							str	      =					TEXT_TO_IMAGE_IMAGE_PARAMS


				def     _A							(							self:   Union[str, Any]		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    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		,					)
											SCREAMING_SNAKE_CASE_   	=    DDIMScheduler(
											    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''		,					clip_sample=UpperCamelCase_		,					set_alpha_to_one=UpperCamelCase_		,					)
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    AutoencoderKL(
											    block_out_channels=[32, 64]		,					in_channels=6		,					out_channels=6		,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,					latent_channels=4		,					)
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    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=10_00		,					)
											SCREAMING_SNAKE_CASE_   	=    CLIPTextModel(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

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


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Dict		,					_lowerCamelCase:   Optional[int]=0		):
											if str(UpperCamelCase_		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(UpperCamelCase_		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''guidance_scale''': 6.0,
											    '''output_type''': '''numpy''',
											}
											return inputs


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth

											SCREAMING_SNAKE_CASE_   	=    rgb[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_   	=    depth[0, -3:, -1]

											assert rgb.shape == (1, 64, 64, 3)
											assert depth.shape == (1, 64, 64)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]		)
											SCREAMING_SNAKE_CASE_   	=    np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]		)

											assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1E-2
											assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1E-2


				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    3 * [inputs['''prompt''']]

											# forward
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth
											SCREAMING_SNAKE_CASE_   	=    rgb_slice_a[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_   	=    depth_slice_a[0, -3:, -1]

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    3 * [inputs.pop('''prompt'''		)]

											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.tokenizer(
											    UpperCamelCase_		,					padding='''max_length'''		,					max_length=ldmad_pipe.tokenizer.model_max_length		,					truncation=UpperCamelCase_		,					return_tensors='''pt'''		,					)
											SCREAMING_SNAKE_CASE_   	=    text_inputs['''input_ids'''].to(UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.text_encoder(UpperCamelCase_		)[0]

											SCREAMING_SNAKE_CASE_   	=    prompt_embeds

											# forward
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth
											SCREAMING_SNAKE_CASE_   	=    rgb_slice_a[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_   	=    depth_slice_a[0, -3:, -1]

											assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1E-4
											assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1E-4





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    '''french fries'''
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		,					negative_prompt=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth
											SCREAMING_SNAKE_CASE_   	=    rgb[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_   	=    depth[0, -3:, -1]

											assert rgb.shape == (1, 64, 64, 3)
											assert depth.shape == (1, 64, 64)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]		)
											SCREAMING_SNAKE_CASE_   	=    np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]		)
											assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1E-2
											assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1E-2






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







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


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Union[str, Any]="cpu"		,					_lowerCamelCase:   int=torch.floataa		,					_lowerCamelCase:   Union[str, Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
											SCREAMING_SNAKE_CASE_   	=    torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		,					dtype=UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prompt''': '''a photograph of an astronaut riding a horse''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 3,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe.to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth
											SCREAMING_SNAKE_CASE_   	=    rgb[0, -3:, -3:, -1].flatten()
											SCREAMING_SNAKE_CASE_   	=    rgb[0, -3:, -1].flatten()

											assert rgb.shape == (1, 5_12, 5_12, 3)
											assert depth.shape == (1, 5_12, 5_12)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]		)
											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]		)
											assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3E-3
											assert np.abs(depth_slice - expected_slice_depth		).max() < 3E-3






@nightly
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







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


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   int="cpu"		,					_lowerCamelCase:   List[Any]=torch.floataa		,					_lowerCamelCase:   Optional[int]=0		):
											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    np.random.RandomState(UpperCamelCase_		).standard_normal((1, 4, 64, 64)		)
											SCREAMING_SNAKE_CASE_   	=    torch.from_numpy(UpperCamelCase_		).to(device=UpperCamelCase_		,					dtype=UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prompt''': '''a photograph of an astronaut riding a horse''',
											    '''latents''': latents,
											    '''generator''': generator,
											    '''num_inference_steps''': 50,
											    '''guidance_scale''': 7.5,
											    '''output_type''': '''numpy''',
											}
											return inputs


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''		).to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth

											SCREAMING_SNAKE_CASE_   	=    0.49_55_86
											SCREAMING_SNAKE_CASE_   	=    0.33_79_55_15
											SCREAMING_SNAKE_CASE_   	=    1_12.4_85_18
											SCREAMING_SNAKE_CASE_   	=    98.48_97_46
											assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
											assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
											assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
											assert np.abs(expected_depth_std - depth.std()		) < 1E-3





				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''		).to(UpperCamelCase_		)
											ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

											SCREAMING_SNAKE_CASE_   	=    self.get_inputs(UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_   	=    ldmad_pipe(**UpperCamelCase_		)
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    output.rgb, output.depth

											SCREAMING_SNAKE_CASE_   	=    0.4_19_41_27
											SCREAMING_SNAKE_CASE_   	=    0.35_37_55_86
											SCREAMING_SNAKE_CASE_   	=    0.5_63_85_02
											SCREAMING_SNAKE_CASE_   	=    0.34_68_61_03

											assert rgb.shape == (1, 5_12, 5_12, 3)
											assert depth.shape == (1, 5_12, 5_12, 1)
											assert np.abs(expected_rgb_mean - rgb.mean()		) < 1E-3
											assert np.abs(expected_rgb_std - rgb.std()		) < 1E-3
											assert np.abs(expected_depth_mean - depth.mean()		) < 1E-3
											assert np.abs(expected_depth_std - depth.std()		) < 1E-3





import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    fname.split(os.path.sep     )[-1]
							return re.search(R'''^(.*)_\d+\.jpg$'''  ,    _lowerCAmelCase     ).groups()[0]






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   Union[str, Any]		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=None		):
											SCREAMING_SNAKE_CASE_   	=    file_names
											SCREAMING_SNAKE_CASE_   	=    image_transform
											SCREAMING_SNAKE_CASE_   	=    label_to_id


				def __len__(							self:   Any		):
											return len(self.file_names		)





				def __getitem__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.file_names[idx]
											SCREAMING_SNAKE_CASE_   	=    PIL.Image.open(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    raw_image.convert('''RGB'''		)
											if self.image_transform is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.image_transform(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    extract_label(_lowerCamelCase		)
											if self.label_to_id is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.label_to_id[label]
											return {"image": image, "label": label}






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    Accelerator(
														    cpu=args.cpu  ,    mixed_precision=args.mixed_precision  ,    log_with='''all'''  ,    project_dir=args.project_dir     )
							else:
														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_   	=    config['''image_size''']
							if not isinstance(_lowerCAmelCase  ,    (list, tuple)     ):
														SCREAMING_SNAKE_CASE_   	=    (image_size, image_size)

							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps  ,    '''isdigit'''     ):
														if args.checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					SCREAMING_SNAKE_CASE_   	=    int(args.checkpointing_steps     )
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							# We need to initialize the trackers we use, and also store our configuration
							if args.with_tracking:
														SCREAMING_SNAKE_CASE_   	=    os.path.split(_lowerCAmelCase     )[-1].split('''.'''     )[0]
														accelerator.init_trackers(_lowerCAmelCase  ,    _lowerCAmelCase     )

							# Grab all the image filenames
							SCREAMING_SNAKE_CASE_   	=    [os.path.join(args.data_dir  ,    _lowerCAmelCase     ) for fname in os.listdir(args.data_dir     ) if fname.endswith('''.jpg'''     )]

							# Build the label correspondences
							SCREAMING_SNAKE_CASE_   	=    [extract_label(_lowerCAmelCase     ) for fname in file_names]
							SCREAMING_SNAKE_CASE_   	=    list(set(_lowerCAmelCase     )     )
							id_to_label.sort()
							SCREAMING_SNAKE_CASE_   	=    {lbl: i for i, lbl in enumerate(_lowerCAmelCase     )}

							# Set the seed before splitting the data.
							np.random.seed(_lowerCAmelCase     )
							torch.manual_seed(_lowerCAmelCase     )
							torch.cuda.manual_seed_all(_lowerCAmelCase     )

							# Split our filenames between train and validation
							SCREAMING_SNAKE_CASE_   	=    np.random.permutation(len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    int(0.8 * len(_lowerCAmelCase     )     )
							SCREAMING_SNAKE_CASE_   	=    random_perm[:cut]
							SCREAMING_SNAKE_CASE_   	=    random_perm[cut:]

							# For training we use a simple RandomResizedCrop
							SCREAMING_SNAKE_CASE_   	=    Compose([RandomResizedCrop(_lowerCAmelCase  ,    scale=(0.5, 1.0)     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset(
							    [file_names[i] for i in train_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# For evaluation, we use a deterministic Resize
							SCREAMING_SNAKE_CASE_   	=    Compose([Resize(_lowerCAmelCase     ), ToTensor()]     )
							SCREAMING_SNAKE_CASE_   	=    PetsDataset([file_names[i] for i in eval_split]  ,    image_transform=_lowerCAmelCase  ,    label_to_id=_lowerCAmelCase     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(_lowerCAmelCase  ,    shuffle=_lowerCAmelCase  ,    batch_size=_lowerCAmelCase  ,    num_workers=4     )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							SCREAMING_SNAKE_CASE_   	=    create_model('''resnet50d'''  ,    pretrained=_lowerCAmelCase  ,    num_classes=len(_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).
							SCREAMING_SNAKE_CASE_   	=    model.to(accelerator.device     )

							# Freezing the base model
							for param in model.parameters():
														SCREAMING_SNAKE_CASE_   	=    False
							for param in model.get_classifier().parameters():
														SCREAMING_SNAKE_CASE_   	=    True

							# We normalize the batches of images to be a bit faster.
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''mean''']     )[None, :, None, None].to(accelerator.device     )
							SCREAMING_SNAKE_CASE_   	=    torch.tensor(model.default_cfg['''std''']     )[None, :, None, None].to(accelerator.device     )

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE_   	=    torch.optim.Adam(params=model.parameters()  ,    lr=lr / 2_5     )

							# Instantiate learning rate scheduler
							SCREAMING_SNAKE_CASE_   	=    OneCycleLR(optimizer=_lowerCAmelCase  ,    max_lr=_lowerCAmelCase  ,    epochs=_lowerCAmelCase  ,    steps_per_epoch=len(_lowerCAmelCase     )     )

							# 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_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.prepare(
							    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
							# We need to keep track of how many total steps we have iterated over
							SCREAMING_SNAKE_CASE_   	=    0
							# We also need to keep track of the starting epoch so files are named properly
							SCREAMING_SNAKE_CASE_   	=    0

							# Potentially load in the weights and states from a previous save
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"     )
																					accelerator.load_state(args.resume_from_checkpoint     )
																					SCREAMING_SNAKE_CASE_   	=    os.path.basename(args.resume_from_checkpoint     )
														else:
																					# Get the most recent checkpoint
																					SCREAMING_SNAKE_CASE_   	=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																					dirs.sort(key=os.path.getctime     )
																					SCREAMING_SNAKE_CASE_   	=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														SCREAMING_SNAKE_CASE_   	=    os.path.splitext(_lowerCAmelCase     )[0]

														if "epoch" in training_difference:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''epoch_'''  ,    ''''''     )     ) + 1
																					SCREAMING_SNAKE_CASE_   	=    None
														else:
																					SCREAMING_SNAKE_CASE_   	=    int(training_difference.replace('''step_'''  ,    ''''''     )     )
																					SCREAMING_SNAKE_CASE_   	=    resume_step // len(_lowerCAmelCase     )
																					resume_step -= starting_epoch * len(_lowerCAmelCase     )

    # Now we train the model
							for epoch in range(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														model.train()
														if args.with_tracking:
																					SCREAMING_SNAKE_CASE_   	=    0
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					SCREAMING_SNAKE_CASE_   	=    accelerator.skip_first_batches(_lowerCAmelCase  ,    _lowerCAmelCase     )
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					SCREAMING_SNAKE_CASE_   	=    train_dataloader
														for batch in active_dataloader:
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.cross_entropy(_lowerCAmelCase  ,    batch['''label''']     )
																					# We keep track of the loss at each epoch
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					accelerator.backward(_lowerCAmelCase     )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()
																					overall_step += 1
																					if isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																																			accelerator.save_state(_lowerCAmelCase     )
														model.eval()
														SCREAMING_SNAKE_CASE_   	=    0
														SCREAMING_SNAKE_CASE_   	=    0
														for step, batch in enumerate(_lowerCAmelCase     ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					SCREAMING_SNAKE_CASE_   	=    {k: v.to(accelerator.device     ) for k, v in batch.items()}
																					SCREAMING_SNAKE_CASE_   	=    (batch['''image'''] - mean) / std
																					with torch.no_grad():
																												SCREAMING_SNAKE_CASE_   	=    model(_lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    outputs.argmax(dim=-1     )
																					SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    accelerator.gather_for_metrics((predictions, batch['''label'''])     )
																					SCREAMING_SNAKE_CASE_   	=    predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														SCREAMING_SNAKE_CASE_   	=    accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}: {1_0_0 * eval_metric:.2f}"     )
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        '''accuracy''': 1_0_0 * eval_metric,
																					        '''train_loss''': total_loss.item() / len(_lowerCAmelCase     ),
																					        '''epoch''': epoch,
																					    }  ,    step=_lowerCAmelCase  ,    )
														if checkpointing_steps == "epoch":
																					SCREAMING_SNAKE_CASE_   	=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												SCREAMING_SNAKE_CASE_   	=    os.path.join(args.output_dir  ,    _lowerCAmelCase     )
																					accelerator.save_state(_lowerCAmelCase     )

							if args.with_tracking:
														accelerator.end_training()

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    argparse.ArgumentParser(description='''Simple example of training script.'''     )
							parser.add_argument('''--data_dir'''  ,    required=_lowerCAmelCase  ,    help='''The data folder on disk.'''     )
							parser.add_argument('''--fp16'''  ,    action='''store_true'''  ,    help='''If passed, will use FP16 training.'''     )
							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.'''     )
							parser.add_argument(
							    '''--checkpointing_steps'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,    )
							parser.add_argument(
							    '''--output_dir'''  ,    type=_lowerCAmelCase  ,    default='''.'''  ,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,    )
							parser.add_argument(
							    '''--resume_from_checkpoint'''  ,    type=_lowerCAmelCase  ,    default=_lowerCAmelCase  ,    help='''If the training should continue from a checkpoint folder.'''  ,    )
							parser.add_argument(
							    '''--with_tracking'''  ,    action='''store_true'''  ,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,    )
							parser.add_argument(
							    '''--project_dir'''  ,    type=_lowerCAmelCase  ,    default='''logs'''  ,    help='''Location on where to store experiment tracking logs` and relevent project information'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(_lowerCAmelCase  ,    _lowerCAmelCase     )


if __name__ == "__main__":
			main()


