
"""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      lowerCamelCase_     (						lowerCamelCase_			)  ->      int:


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




class  _snake_case	(    _lowercase							):






							def __init__(       self    :      int    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      List[str]=None    ,			snake_case__    :      Optional[int]=None     ):






												"""simple docstring"""



												__a:    Tuple		     =       file_names
												__a:    Optional[Any]		     =       image_transform
												__a:    str		     =       label_to_id






							def __len__(       self    :      List[Any]     ):






												"""simple docstring"""



												return len(self.file_names     )






							def __getitem__(       self    :      List[str]    ,			snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Dict		     =       self.file_names[idx]
												__a:    Any		     =       PIL.Image.open(snake_case__     )
												__a:    Union[str, Any]		     =       raw_image.convert("""RGB"""     )
												if self.image_transform is not None:
																	__a:    Any		     =       self.image_transform(snake_case__     )
												__a:    int		     =       extract_label(snake_case__     )
												if self.label_to_id is not None:
																	__a:    List[Any]		     =       self.label_to_id[label]
												return {"image": image, "label": label}






def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# Initialize accelerator
					__a:    Tuple		     =       Accelerator(cpu=args.cpu						,					mixed_precision=args.mixed_precision			)

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

					# Grab all the image filenames
					__a:    Optional[Any]		     =       [os.path.join(args.data_dir						,					lowerCamelCase_			) for fname in os.listdir(args.data_dir			) if fname.endswith(""".jpg"""			)]

					# Build the label correspondences
					__a:    Optional[int]		     =       [extract_label(lowerCamelCase_			) for fname in file_names]
					__a:    Any		     =       list(set(lowerCamelCase_			)			)
					id_to_label.sort()
					__a:    Optional[int]		     =       {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
					__a:    Tuple		     =       np.random.permutation(len(lowerCamelCase_			)			)
					__a:    Optional[Any]		     =       int(0.8 * len(lowerCamelCase_			)			)
					__a:    Optional[Any]		     =       random_perm[:cut]
					__a:    Optional[int]		     =       random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					__a:    Tuple		     =       Compose([RandomResizedCrop(lowerCamelCase_						,					scale=(0.5, 1.0)			), ToTensor()]			)
					__a:    Optional[Any]		     =       PetsDataset(
					    [file_names[i] for i in train_split]						,					image_transform=lowerCamelCase_						,					label_to_id=lowerCamelCase_			)

					# For evaluation, we use a deterministic Resize
					__a:    Optional[int]		     =       Compose([Resize(lowerCamelCase_			), ToTensor()]			)
					__a:    Any		     =       PetsDataset([file_names[i] for i in eval_split]						,					image_transform=lowerCamelCase_						,					label_to_id=lowerCamelCase_			)

					# Instantiate dataloaders.
					__a:    Union[str, Any]		     =       DataLoader(lowerCamelCase_						,					shuffle=lowerCamelCase_						,					batch_size=lowerCamelCase_						,					num_workers=4			)
					__a:    List[str]		     =       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)
					__a:    Tuple		     =       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).
					__a:    Any		     =       model.to(accelerator.device			)

					# Freezing the base model
					for param in model.parameters():
										__a:    List[Any]		     =       False
					for param in model.get_classifier().parameters():
										__a:    Dict		     =       True

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

					# Instantiate optimizer
					__a:    List[Any]		     =       torch.optim.Adam(params=model.parameters()						,					lr=lr / 25			)

					# Instantiate learning rate scheduler
					__a:    Tuple		     =       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.
					__a     ,					__a     ,					__a     ,					__a     ,					__a:    Optional[int]		     =       accelerator.prepare(
					    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)

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

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

										__a:    Optional[int]		     =       accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f"""epoch {epoch}: {1_00 * eval_metric:.2f}"""			)


def      lowerCamelCase_     (						)  ->      Tuple:


					'''simple docstring'''
					__a:    Dict		     =       argparse.ArgumentParser(description="""Simple example of training script."""			)
					parser.add_argument("""--data_dir"""						,					required=lowerCamelCase_						,					help="""The data folder on disk."""			)
					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(
					    """--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("""--cpu"""						,					action="""store_true"""						,					help="""If passed, will train on the CPU."""			)
					__a:    str		     =       parser.parse_args()
					__a:    Dict		     =       {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
					training_function(lowerCamelCase_						,					lowerCamelCase_			)


if __name__ == "__main__":
			main()


"""simple docstring"""
from typing import TYPE_CHECKING

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


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

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