




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							SCREAMING_SNAKE_CASE_   	=    []
							for i in range(config.num_hidden_layers     ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"blocks.{i}.norm1.weight", F"deit.encoder.layer.{i}.layernorm_before.weight")     )
														rename_keys.append((F"blocks.{i}.norm1.bias", F"deit.encoder.layer.{i}.layernorm_before.bias")     )
														rename_keys.append((F"blocks.{i}.attn.proj.weight", F"deit.encoder.layer.{i}.attention.output.dense.weight")     )
														rename_keys.append((F"blocks.{i}.attn.proj.bias", F"deit.encoder.layer.{i}.attention.output.dense.bias")     )
														rename_keys.append((F"blocks.{i}.norm2.weight", F"deit.encoder.layer.{i}.layernorm_after.weight")     )
														rename_keys.append((F"blocks.{i}.norm2.bias", F"deit.encoder.layer.{i}.layernorm_after.bias")     )
														rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"deit.encoder.layer.{i}.intermediate.dense.weight")     )
														rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"deit.encoder.layer.{i}.intermediate.dense.bias")     )
														rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"deit.encoder.layer.{i}.output.dense.weight")     )
														rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"deit.encoder.layer.{i}.output.dense.bias")     )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('''cls_token''', '''deit.embeddings.cls_token'''),
							        ('''dist_token''', '''deit.embeddings.distillation_token'''),
							        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
							        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
							        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
							    ]     )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]     )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														SCREAMING_SNAKE_CASE_   	=    [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''     ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''deit.layernorm.weight'''),
														        ('''norm.bias''', '''deit.layernorm.bias'''),
														        ('''head.weight''', '''cls_classifier.weight'''),
														        ('''head.bias''', '''cls_classifier.bias'''),
														        ('''head_dist.weight''', '''distillation_classifier.weight'''),
														        ('''head_dist.bias''', '''distillation_classifier.bias'''),
														    ]     )

							return rename_keys

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=False     ):
							for i in range(config.num_hidden_layers     ):
														if base_model:
																					SCREAMING_SNAKE_CASE_   	=    ''''''
														else:
																					SCREAMING_SNAKE_CASE_   	=    '''deit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"blocks.{i}.attn.qkv.weight"     )
														SCREAMING_SNAKE_CASE_   	=    state_dict.pop(F"blocks.{i}.attn.qkv.bias"     )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE_   	=    in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE_   	=    in_proj_bias[-config.hidden_size :]

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    dct.pop(UpperCAmelCase__     )
							SCREAMING_SNAKE_CASE_   	=    val

def 			a   ():
							SCREAMING_SNAKE_CASE_   	=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE_   	=    Image.open(requests.get(UpperCAmelCase__  ,    stream=UpperCAmelCase__     ).raw     )
							return im

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    DeiTConfig()
							# all deit models have fine-tuned heads
							SCREAMING_SNAKE_CASE_   	=    False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							SCREAMING_SNAKE_CASE_   	=    1_0_0_0
							SCREAMING_SNAKE_CASE_   	=    '''huggingface/label-files'''
							SCREAMING_SNAKE_CASE_   	=    '''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE_   	=    json.load(open(hf_hub_download(UpperCAmelCase__  ,    UpperCAmelCase__  ,    repo_type='''dataset'''     )  ,    '''r'''     )     )
							SCREAMING_SNAKE_CASE_   	=    {int(UpperCAmelCase__     ): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_   	=    idalabel
							SCREAMING_SNAKE_CASE_   	=    {v: k for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_   	=    int(deit_name[-6:-4]     )
							SCREAMING_SNAKE_CASE_   	=    int(deit_name[-3:]     )
							# size of the architecture
							if deit_name[9:].startswith('''tiny'''     ):
														SCREAMING_SNAKE_CASE_   	=    1_9_2
														SCREAMING_SNAKE_CASE_   	=    7_6_8
														SCREAMING_SNAKE_CASE_   	=    1_2
														SCREAMING_SNAKE_CASE_   	=    3
							elif deit_name[9:].startswith('''small'''     ):
														SCREAMING_SNAKE_CASE_   	=    3_8_4
														SCREAMING_SNAKE_CASE_   	=    1_5_3_6
														SCREAMING_SNAKE_CASE_   	=    1_2
														SCREAMING_SNAKE_CASE_   	=    6
							if deit_name[9:].startswith('''base'''     ):
														pass
							elif deit_name[4:].startswith('''large'''     ):
														SCREAMING_SNAKE_CASE_   	=    1_0_2_4
														SCREAMING_SNAKE_CASE_   	=    4_0_9_6
														SCREAMING_SNAKE_CASE_   	=    2_4
														SCREAMING_SNAKE_CASE_   	=    1_6

							# load original model from timm
							SCREAMING_SNAKE_CASE_   	=    timm.create_model(UpperCAmelCase__  ,    pretrained=UpperCAmelCase__     )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE_   	=    timm_model.state_dict()
							SCREAMING_SNAKE_CASE_   	=    create_rename_keys(UpperCAmelCase__  ,    UpperCAmelCase__     )
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__  ,    UpperCAmelCase__  ,    UpperCAmelCase__     )
							read_in_q_k_v(UpperCAmelCase__  ,    UpperCAmelCase__  ,    UpperCAmelCase__     )

							# load HuggingFace model
							SCREAMING_SNAKE_CASE_   	=    DeiTForImageClassificationWithTeacher(UpperCAmelCase__     ).eval()
							model.load_state_dict(UpperCAmelCase__     )

							# Check outputs on an image, prepared by DeiTImageProcessor
							SCREAMING_SNAKE_CASE_   	=    int(
							    (2_5_6 / 2_2_4) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							SCREAMING_SNAKE_CASE_   	=    DeiTImageProcessor(size=UpperCAmelCase__  ,    crop_size=config.image_size     )
							SCREAMING_SNAKE_CASE_   	=    image_processor(images=prepare_img()  ,    return_tensors='''pt'''     )
							SCREAMING_SNAKE_CASE_   	=    encoding['''pixel_values''']
							SCREAMING_SNAKE_CASE_   	=    model(UpperCAmelCase__     )

							SCREAMING_SNAKE_CASE_   	=    timm_model(UpperCAmelCase__     )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(UpperCAmelCase__  ,    outputs.logits  ,    atol=1e-3     )

							Path(UpperCAmelCase__     ).mkdir(exist_ok=UpperCAmelCase__     )
							print(F"Saving model {deit_name} to {pytorch_dump_folder_path}"     )
							model.save_pretrained(UpperCAmelCase__     )
							print(F"Saving image processor to {pytorch_dump_folder_path}"     )
							image_processor.save_pretrained(UpperCAmelCase__     )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE 						=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--deit_name""",
			    default="""vit_deit_base_distilled_patch16_224""",
			    type=str,
			    help="""Name of the DeiT timm model you\'d like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
			)

			__SCREAMING_SNAKE_CASE 						=parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





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()


