




import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	_lowerCamelCase					:     Union[str, Any]					  =  True
	_lowerCamelCase					:     Optional[Any]					  =  ""
except Exception as e:
	_lowerCamelCase					:     int					  =  False
	_lowerCamelCase					:     List[str]					  =  (
	    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
	)


from PIL import Image  # noqa: E402
class 				__snake_case     (_a  ,		_a						):
				lowerCAmelCase__			=   "mel_config.json"



				@register_to_config
				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 2_2050						,		_UpperCAmelCase					:						int = 2048						,		_UpperCAmelCase					:						int = 512						,		_UpperCAmelCase					:						int = 80						,		_UpperCAmelCase					:						int = 32						,		)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					hop_length
								_lowerCAmelCase			:			Optional[Any]        =					sample_rate
								_lowerCAmelCase			:			Optional[Any]        =					n_fft
								_lowerCAmelCase			:			Dict        =					top_db
								_lowerCAmelCase			:			List[Any]        =					n_iter
								self.set_resolution(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					None

								if not _librosa_can_be_imported:
												raise ValueError(_import_error					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					x_res
								_lowerCAmelCase			:			Union[str, Any]        =					y_res
								_lowerCAmelCase			:			Any        =					self.y_res
								_lowerCAmelCase			:			Optional[Any]        =					self.x_res * self.hop_length - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str = None						,		_UpperCAmelCase					:						np.ndarray = None					)     ->			List[str]:





								'''simple docstring'''




								if audio_file is not None:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					librosa.load(_UpperCAmelCase						,		mono=_UpperCAmelCase						,		sr=self.sr					)
								else:
												_lowerCAmelCase			:			Any        =					raw_audio

								# Pad with silence if necessary.
								if len(self.audio					) < self.x_res * self.hop_length:
												_lowerCAmelCase			:			Optional[Any]        =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								return len(self.audio					) // self.slice_size



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int = 0					)     ->			np.ndarray:





								'''simple docstring'''




								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								return self.sr



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int					)     ->			Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					librosa.feature.melspectrogram(
								    y=self.get_audio_slice(_UpperCAmelCase					)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels					)
								_lowerCAmelCase			:			List[Any]        =					librosa.power_to_db(_UpperCAmelCase						,		ref=np.max						,		top_db=self.top_db					)
								_lowerCAmelCase			:			List[Any]        =					(((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255					) + 0.5).astype(np.uinta					)
								_lowerCAmelCase			:			Optional[Any]        =					Image.fromarray(_UpperCAmelCase					)
								return image



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Image.Image					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					np.frombuffer(image.tobytes()						,		dtype="""uint8"""					).reshape((image.height, image.width)					)
								_lowerCAmelCase			:			Optional[int]        =					bytedata.astype("""float"""					) * self.top_db / 255 - self.top_db
								_lowerCAmelCase			:			List[Any]        =					librosa.db_to_power(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					librosa.feature.inverse.mel_to_audio(
								    _UpperCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter					)
								return audio







'''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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=None,  __UpperCamelCase=None ):
							_snake_case	       =       file_names
							_snake_case	       =       image_transform
							_snake_case	       =       label_to_id






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






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






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_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(lowercase_						,							(list, tuple)  ):
				_snake_case	       =       (image_size, image_size)

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

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

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

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

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

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

		# Instantiate dataloaders.
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							num_workers=4  )
		_snake_case	       =       DataLoader(lowercase_						,							shuffle=lowercase_						,							batch_size=lowercase_						,							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=lowercase_						,							num_classes=len(lowercase_  )  )

		# 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=lowercase_						,							max_lr=lowercase_						,							epochs=lowercase_						,							steps_per_epoch=len(lowercase_  )  )

		# 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(
		    lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  )

		# Now we train the model
		for epoch in range(lowercase_  ):
				model.train()
				for step, batch in enumerate(lowercase_  ):
						# 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(lowercase_  )
						_snake_case	       =       torch.nn.functional.cross_entropy(lowercase_						,							batch["label"]  )
						accelerator.backward(lowercase_  )
						optimizer.step()
						lr_scheduler.step()
						optimizer.zero_grad()

				model.eval()
				_snake_case	       =       0
				_snake_case	       =       0
				for _, batch in enumerate(lowercase_  ):
						# 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(lowercase_  )
						_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 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       argparse.ArgumentParser(description="Simple example of training script."  )
		parser.add_argument("--data_dir"						,							required=lowercase_						,							help="The data folder on disk."  )
		parser.add_argument(
		    "--mixed_precision"						,							type=lowercase_						,							default=lowercase_						,							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=lowercase_						,							default=lowercase_						,							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(lowercase_						,							lowercase_  )


if __name__ == "__main__":
						main()





