




import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__SCREAMING_SNAKE_CASE 						=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE 						=50  # max width of layer names
__SCREAMING_SNAKE_CASE 						=70  # max width of quantizer names

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    parser.add_argument_group('''quant_trainer arguments'''     )
							group.add_argument('''--wprec'''  ,    type=lowerCAmelCase__  ,    default=8  ,    help='''weight precision'''     )
							group.add_argument('''--aprec'''  ,    type=lowerCAmelCase__  ,    default=8  ,    help='''activation precision'''     )
							group.add_argument('''--quant-per-tensor'''  ,    action='''store_true'''  ,    help='''per tensor weight scaling'''     )
							group.add_argument('''--quant-disable'''  ,    action='''store_true'''  ,    help='''disable all quantizers'''     )
							group.add_argument('''--quant-disable-embeddings'''  ,    action='''store_true'''  ,    help='''disable all embeddings quantizers'''     )
							group.add_argument('''--quant-disable-keyword'''  ,    type=lowerCAmelCase__  ,    nargs='''+'''  ,    help='''disable quantizers by keyword'''     )
							group.add_argument('''--quant-disable-layer-module'''  ,    type=lowerCAmelCase__  ,    help='''disable quantizers by keyword under layer.'''     )
							group.add_argument('''--quant-enable-layer-module'''  ,    type=lowerCAmelCase__  ,    help='''enable quantizers by keyword under layer'''     )
							group.add_argument('''--calibrator'''  ,    default='''max'''  ,    help='''which quantization range calibrator to use'''     )
							group.add_argument('''--percentile'''  ,    default=lowerCAmelCase__  ,    type=lowerCAmelCase__  ,    help='''percentile for PercentileCalibrator'''     )
							group.add_argument('''--fuse-qkv'''  ,    action='''store_true'''  ,    help='''use the same scale factor for qkv'''     )
							group.add_argument('''--clip-gelu'''  ,    metavar='''N'''  ,    type=lowerCAmelCase__  ,    help='''clip gelu output maximum value to N'''     )
							group.add_argument(
							    '''--recalibrate-weights'''  ,    action='''store_true'''  ,    help=(
							        '''recalibrate weight amaxes by taking the max of the weights.'''
							        ''' amaxes will be computed with the current quantization granularity (axis).'''
							    )  ,    )

def 			a   (_lowerCAmelCase     ):

							if args.calibrator == "max":
														SCREAMING_SNAKE_CASE_   	=    '''max'''
							elif args.calibrator == "percentile":
														if args.percentile is None:
																					raise ValueError('''Specify --percentile when using percentile calibrator'''     )
														SCREAMING_SNAKE_CASE_   	=    '''histogram'''
							elif args.calibrator == "mse":
														SCREAMING_SNAKE_CASE_   	=    '''histogram'''
							else:
														raise ValueError(F"Invalid calibrator {args.calibrator}"     )

							SCREAMING_SNAKE_CASE_   	=    QuantDescriptor(num_bits=args.aprec  ,    calib_method=lowerCAmelCase__     )
							SCREAMING_SNAKE_CASE_   	=    QuantDescriptor(num_bits=args.wprec  ,    axis=(None if args.quant_per_tensor else (0,))     )
							quant_nn.QuantLinear.set_default_quant_desc_input(lowerCAmelCase__     )
							quant_nn.QuantLinear.set_default_quant_desc_weight(lowerCAmelCase__     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase=False  ,    _lowerCAmelCase=False     ):
							logger.info('''Configuring Model for Quantization'''     )
							logger.info(F"using quantization package {pytorch_quantization.__file__}"     )

							if not calib:
														if args.quant_disable_embeddings:
																					set_quantizer_by_name(lowerCAmelCase__  ,    ['''embeddings''']  ,    which='''weight'''  ,    _disabled=lowerCAmelCase__     )

														if args.quant_disable:
																					set_quantizer_by_name(lowerCAmelCase__  ,    ['''''']  ,    _disabled=lowerCAmelCase__     )

														if args.quant_disable_keyword:
																					set_quantizer_by_name(lowerCAmelCase__  ,    args.quant_disable_keyword  ,    _disabled=lowerCAmelCase__     )

														if args.quant_disable_layer_module:
																					set_quantizer_by_name(lowerCAmelCase__  ,    [R'''layer.\d+.''' + args.quant_disable_layer_module]  ,    _disabled=lowerCAmelCase__     )

														if args.quant_enable_layer_module:
																					set_quantizer_by_name(lowerCAmelCase__  ,    [R'''layer.\d+.''' + args.quant_enable_layer_module]  ,    _disabled=lowerCAmelCase__     )

														if args.recalibrate_weights:
																					recalibrate_weights(lowerCAmelCase__     )

														if args.fuse_qkv:
																					fuse_qkv(lowerCAmelCase__  ,    lowerCAmelCase__     )

							if args.clip_gelu:
														clip_gelu(lowerCAmelCase__  ,    args.clip_gelu     )

							# if args.local_rank in [-1, 0] and not calib:
							print_quant_summary(lowerCAmelCase__     )

def 			a   (_lowerCAmelCase     ):
							logger.info('''Enabling Calibration'''     )
							for name, module in model.named_modules():
														if name.endswith('''_quantizer'''     ):
																					if module._calibrator is not None:
																												module.disable_quant()
																												module.enable_calib()
																					else:
																												module.disable()
																					logger.info(F"{name:80}: {module}"     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							logger.info('''Loading calibrated amax'''     )
							for name, module in model.named_modules():
														if name.endswith('''_quantizer'''     ):
																					if module._calibrator is not None:
																												if isinstance(module._calibrator  ,    calib.MaxCalibrator     ):
																																			module.load_calib_amax()
																												else:
																																			module.load_calib_amax('''percentile'''  ,    percentile=args.percentile     )
																												module.enable_quant()
																												module.disable_calib()
																					else:
																												module.enable()
							model.cuda()
							print_quant_summary(lowerCAmelCase__     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):

							def fusea(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
														for mod in [qq, qk, qv]:
																					if not hasattr(lowerCAmelCase__  ,    '''_amax'''     ):
																												print('''          WARNING: NO AMAX BUFFER'''     )
																												return
														SCREAMING_SNAKE_CASE_   	=    qq._amax.detach().item()
														SCREAMING_SNAKE_CASE_   	=    qk._amax.detach().item()
														SCREAMING_SNAKE_CASE_   	=    qv._amax.detach().item()

														SCREAMING_SNAKE_CASE_   	=    max(lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
														qq._amax.fill_(lowerCAmelCase__     )
														qk._amax.fill_(lowerCAmelCase__     )
														qv._amax.fill_(lowerCAmelCase__     )
														logger.info(F"          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"     )

							for name, mod in model.named_modules():
														if name.endswith('''.attention.self'''     ):
																					logger.info(F"FUSE_QKV: {name:{name_width}}"     )
																					fusea(mod.matmul_q_input_quantizer  ,    mod.matmul_k_input_quantizer  ,    mod.matmul_v_input_quantizer     )
																					if args.quant_per_tensor:
																												fusea(mod.query._weight_quantizer  ,    mod.key._weight_quantizer  ,    mod.value._weight_quantizer     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):

							for name, mod in model.named_modules():
														if name.endswith('''.output.dense'''     ) and not name.endswith('''attention.output.dense'''     ):
																					SCREAMING_SNAKE_CASE_   	=    mod._input_quantizer._amax.data.detach().item()
																					mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase__     )
																					SCREAMING_SNAKE_CASE_   	=    mod._input_quantizer._amax.data.detach().item()
																					logger.info(F"CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"     )

def 			a   (_lowerCAmelCase     ):

							for name, mod in model.named_modules():
														if hasattr(lowerCAmelCase__  ,    '''_weight_quantizer'''     ) and mod._weight_quantizer.axis is not None:
																					SCREAMING_SNAKE_CASE_   	=    mod.weight.shape[0]
																					SCREAMING_SNAKE_CASE_   	=    mod._weight_quantizer._amax.detach()
																					SCREAMING_SNAKE_CASE_   	=    torch.ones(lowerCAmelCase__  ,    dtype=amax.dtype  ,    device=amax.device     ) * amax
																					print(F"expanding {name} {amax} -> {mod._weight_quantizer._amax}"     )

def 			a   (_lowerCAmelCase     ):

							for name, mod in model.named_modules():
														if hasattr(lowerCAmelCase__  ,    '''_weight_quantizer'''     ):
																					if not hasattr(mod.weight_quantizer  ,    '''_amax'''     ):
																												print('''RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'''     )
																												continue

																					# determine which axes to reduce across
																					# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
																					SCREAMING_SNAKE_CASE_   	=    set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis     )
																					SCREAMING_SNAKE_CASE_   	=    set(range(len(mod.weight.size()     )     )     ) - axis_set
																					SCREAMING_SNAKE_CASE_   	=    pytorch_quantization.utils.reduce_amax(mod.weight  ,    axis=lowerCAmelCase__  ,    keepdims=lowerCAmelCase__     ).detach()
																					logger.info(F"RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"     )
																					SCREAMING_SNAKE_CASE_   	=    amax

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=2_5  ,    _lowerCAmelCase=1_8_0  ,    _lowerCAmelCase=None     ):

							if ignore is None:
														SCREAMING_SNAKE_CASE_   	=    []
							elif not isinstance(lowerCAmelCase__  ,    lowerCAmelCase__     ):
														SCREAMING_SNAKE_CASE_   	=    [ignore]

							SCREAMING_SNAKE_CASE_   	=    0
							for name, mod in model.named_modules():
														if not hasattr(lowerCAmelCase__  ,    '''weight'''     ):
																					continue
														SCREAMING_SNAKE_CASE_   	=    max(lowerCAmelCase__  ,    len(lowerCAmelCase__     )     )

							for name, mod in model.named_modules():
														SCREAMING_SNAKE_CASE_   	=    getattr(lowerCAmelCase__  ,    '''_input_quantizer'''  ,    lowerCAmelCase__     )
														SCREAMING_SNAKE_CASE_   	=    getattr(lowerCAmelCase__  ,    '''_weight_quantizer'''  ,    lowerCAmelCase__     )
														if not hasattr(lowerCAmelCase__  ,    '''weight'''     ):
																					continue
														if type(lowerCAmelCase__     ) in ignore:
																					continue
														if [True for s in ignore if type(lowerCAmelCase__     ) is str and s in name]:
																					continue
														SCREAMING_SNAKE_CASE_   	=    F"Act:{input_q.extra_repr()}"
														SCREAMING_SNAKE_CASE_   	=    F"Wgt:{weight_q.extra_repr()}"
														SCREAMING_SNAKE_CASE_   	=    F"{name:{name_width}} {act_str} {wgt_str}"
														if len(lowerCAmelCase__     ) <= line_width:
																					logger.info(lowerCAmelCase__     )
														else:
																					logger.info(F"{name:{name_width}} {act_str}"     )
																					logger.info(F"{'  ':{name_width}} {wgt_str}"     )

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    0
							for name, mod in model.named_modules():
														if isinstance(lowerCAmelCase__  ,    pytorch_quantization.nn.TensorQuantizer     ):
																					print(F"{name:80} {mod}"     )
																					count += 1
							print(F"{count} TensorQuantizers found in model"     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    getattr(lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
							if quantizer_mod is not None:
														assert hasattr(lowerCAmelCase__  ,    lowerCAmelCase__     )
														setattr(lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
							else:
														logger.warning(F"{name} has no {quantizer}"     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase="both"  ,    **_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    F"Warning: changing {which} quantizers of {name:{qname_width}}"
							for k, v in kwargs.items():
														s += F" {k}={v}"
														if which in ["input", "both"]:
																					set_quantizer(lowerCAmelCase__  ,    lowerCAmelCase__  ,    '''_input_quantizer'''  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
														if which in ["weight", "both"]:
																					set_quantizer(lowerCAmelCase__  ,    lowerCAmelCase__  ,    '''_weight_quantizer'''  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
							logger.info(lowerCAmelCase__     )






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    **_lowerCAmelCase     ):

							for name, mod in model.named_modules():
														if hasattr(lowerCAmelCase__  ,    '''_input_quantizer'''     ) or hasattr(lowerCAmelCase__  ,    '''_weight_quantizer'''     ):
																					for n in names:
																												if re.search(lowerCAmelCase__  ,    lowerCAmelCase__     ):
																																			set_quantizers(lowerCAmelCase__  ,    lowerCAmelCase__  ,    **lowerCAmelCase__     )
														elif name.endswith('''_quantizer'''     ):
																					for n in names:
																												if re.search(lowerCAmelCase__  ,    lowerCAmelCase__     ):
																																			SCREAMING_SNAKE_CASE_   	=    F"Warning: changing {name:{name_width}}"
																																			for k, v in kwargs.items():
																																										s += F" {k}={v}"
																																										setattr(lowerCAmelCase__  ,    lowerCAmelCase__  ,    lowerCAmelCase__     )
																																			logger.info(lowerCAmelCase__     )





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()


