





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   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		fname.split(os.path.sep      )[-1]
						return re.search(r"""^(.*)_\d+\.jpg$"""       ,    UpperCAmelCase__      ).groups()[0]





class        lowercase_				( A__):






							"""simple docstring"""







							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None		):







													"""simple docstring"""


													a_					=		file_names
													a_					=		image_transform
													a_					=		label_to_id


							def __len__(   self		):







													"""simple docstring"""


													return len(self.file_names		)






							def __getitem__(   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		self.file_names[idx]
													a_					=		PIL.Image.open(_lowerCAmelCase		)
													a_					=		raw_image.convert("""RGB"""		)
													if self.image_transform is not None:
																			a_					=		self.image_transform(_lowerCAmelCase		)
													a_					=		extract_label(_lowerCAmelCase		)
													if self.label_to_id is not None:
																			a_					=		self.label_to_id[label]
													return {"image": image, "label": label}




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		Accelerator(cpu=args.cpu       ,    mixed_precision=args.mixed_precision      )

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						a_					=		config["""lr"""]
						a_					=		int(config["""num_epochs"""]      )
						a_					=		int(config["""seed"""]      )
						a_					=		int(config["""batch_size"""]      )
						a_					=		config["""image_size"""]
						if not isinstance(UpperCAmelCase__       ,    (list, tuple)      ):
												a_					=		(image_size, image_size)

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

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

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

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

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

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

						# Instantiate dataloaders.
						a_					=		DataLoader(UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    num_workers=4      )
						a_					=		DataLoader(UpperCAmelCase__       ,    shuffle=UpperCAmelCase__       ,    batch_size=UpperCAmelCase__       ,    num_workers=4      )

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

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						a_					=		model.to(accelerator.device      )

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

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

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

						# Instantiate learning rate scheduler
						a_					=		OneCycleLR(optimizer=UpperCAmelCase__       ,    max_lr=UpperCAmelCase__       ,    epochs=UpperCAmelCase__       ,    steps_per_epoch=len(UpperCAmelCase__      )      )

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						a_			,   a_			,   a_			,   a_			,   a_					=		accelerator.prepare(
						    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )

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

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

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




def   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		argparse.ArgumentParser(description="""Simple example of training script."""      )
						parser.add_argument("""--data_dir"""       ,    required=UpperCAmelCase__       ,    help="""The data folder on disk."""      )
						parser.add_argument(
						    """--mixed_precision"""       ,    type=UpperCAmelCase__       ,    default=UpperCAmelCase__       ,    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=UpperCAmelCase__       ,    default=UpperCAmelCase__       ,    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."""      )
						a_					=		parser.parse_args()
						a_					=		{"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
						training_function(UpperCAmelCase__       ,    UpperCAmelCase__      )


if __name__ == "__main__":
	main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    :					Optional[Any]             ={
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_    :					Union[str, Any]             =[
	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """Wav2Vec2ConformerForAudioFrameClassification""",
	    """Wav2Vec2ConformerForCTC""",
	    """Wav2Vec2ConformerForPreTraining""",
	    """Wav2Vec2ConformerForSequenceClassification""",
	    """Wav2Vec2ConformerForXVector""",
	    """Wav2Vec2ConformerModel""",
	    """Wav2Vec2ConformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_wavaveca_conformer import (
	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    WavaVecaConformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca_conformer import (
		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaConformerForAudioFrameClassification,
		    WavaVecaConformerForCTC,
		    WavaVecaConformerForPreTraining,
		    WavaVecaConformerForSequenceClassification,
		    WavaVecaConformerForXVector,
		    WavaVecaConformerModel,
		    WavaVecaConformerPreTrainedModel,
		)

else:
	import sys

	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)