
'''simple docstring'''
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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case     =						fname.split(os.path.sep   )[-1]
							return re.search(R"""^(.*)_\d+\.jpg$"""							,      _SCREAMING_SNAKE_CASE   ).groups()[0]


class     _lowerCAmelCase					(					A__   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase=None						,						UpperCAmelCase=None						)  ->     str:
														_snake_case     =						file_names
														_snake_case     =						image_transform
														_snake_case     =						label_to_id

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





							def __getitem__(self						,						UpperCAmelCase						)  ->     Dict:
														_snake_case     =						self.file_names[idx]
														_snake_case     =						PIL.Image.open(_lowerCAmelCase						)
														_snake_case     =						raw_image.convert("""RGB"""						)
														if self.image_transform is not None:
																					_snake_case     =						self.image_transform(_lowerCAmelCase						)
														_snake_case     =						extract_label(_lowerCAmelCase						)
														if self.label_to_id is not None:
																					_snake_case     =						self.label_to_id[label]
														return {"image": image, "label": label}






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_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
							_snake_case     =						config["""lr"""]
							_snake_case     =						int(config["""num_epochs"""]   )
							_snake_case     =						int(config["""seed"""]   )
							_snake_case     =						int(config["""batch_size"""]   )
							_snake_case     =						config["""image_size"""]
							if not isinstance(_SCREAMING_SNAKE_CASE							,      (list, tuple)   ):
														_snake_case     =						(image_size, image_size)

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

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

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

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

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

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

							# Instantiate dataloaders.
							_snake_case     =						DataLoader(_SCREAMING_SNAKE_CASE							,      shuffle=_SCREAMING_SNAKE_CASE							,      batch_size=_SCREAMING_SNAKE_CASE							,      num_workers=4   )
							_snake_case     =						DataLoader(_SCREAMING_SNAKE_CASE							,      shuffle=_SCREAMING_SNAKE_CASE							,      batch_size=_SCREAMING_SNAKE_CASE							,      num_workers=4   )

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_snake_case     =						create_model("""resnet50d"""							,      pretrained=_SCREAMING_SNAKE_CASE							,      num_classes=len(_SCREAMING_SNAKE_CASE   )   )

							# 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).
							_snake_case     =						model.to(accelerator.device   )

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

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

							# Instantiate optimizer
							_snake_case     =						torch.optim.Adam(params=model.parameters()							,      lr=lr / 25   )

							# Instantiate learning rate scheduler
							_snake_case     =						OneCycleLR(optimizer=_SCREAMING_SNAKE_CASE							,      max_lr=_SCREAMING_SNAKE_CASE							,      epochs=_SCREAMING_SNAKE_CASE							,      steps_per_epoch=len(_SCREAMING_SNAKE_CASE   )   )

							# 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.
							_snake_case,							_snake_case,							_snake_case,							_snake_case,							_snake_case     =						accelerator.prepare(
							    _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )

							# Now we train the model
							for epoch in range(_SCREAMING_SNAKE_CASE   ):
														model.train()
														for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					_snake_case     =						{k: v.to(accelerator.device   ) for k, v in batch.items()}
																					_snake_case     =						(batch["""image"""] - mean) / std
																					_snake_case     =						model(_SCREAMING_SNAKE_CASE   )
																					_snake_case     =						torch.nn.functional.cross_entropy(_SCREAMING_SNAKE_CASE							,      batch["""label"""]   )
																					accelerator.backward(_SCREAMING_SNAKE_CASE   )
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

														model.eval()
														_snake_case     =						0
														_snake_case     =						0
														for _, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					_snake_case     =						{k: v.to(accelerator.device   ) for k, v in batch.items()}
																					_snake_case     =						(batch["""image"""] - mean) / std
																					with torch.no_grad():
																												_snake_case     =						model(_SCREAMING_SNAKE_CASE   )
																					_snake_case     =						outputs.argmax(dim=-1   )
																					_snake_case,							_snake_case     =						accelerator.gather_for_metrics((predictions, batch["""label"""])   )
																					_snake_case     =						predictions == references
																					num_elems += accurate_preds.shape[0]
																					accurate += accurate_preds.long().sum()

														_snake_case     =						accurate.item() / num_elems
														# Use accelerator.print to print only on the main process.
														accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""   )






def       __SCREAMING_SNAKE_CASE   (						):
							_snake_case     =						argparse.ArgumentParser(description="""Simple example of training script."""   )
							parser.add_argument("""--data_dir"""							,      required=_SCREAMING_SNAKE_CASE							,      help="""The data folder on disk."""   )
							parser.add_argument(
							    """--mixed_precision"""							,      type=_SCREAMING_SNAKE_CASE							,      default=_SCREAMING_SNAKE_CASE							,      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(
							    """--checkpointing_steps"""							,      type=_SCREAMING_SNAKE_CASE							,      default=_SCREAMING_SNAKE_CASE							,      help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,      )
							parser.add_argument("""--cpu"""							,      action="""store_true"""							,      help="""If passed, will train on the CPU."""   )
							_snake_case     =						parser.parse_args()
							_snake_case     =						{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
				main()

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase  							= {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= ['DetaImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= [
				    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetaForObjectDetection',
				    'DetaModel',
				    'DetaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase  							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)