



'''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 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[int]			)		->						str:
				snake_case__      :   Optional[int]      =    fname.split(os.path.sep			)[-1]
				return re.search(R'''^(.*)_\d+\.jpg$'''   ,				UpperCamelCase__			).groups()[0]






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						int=None			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Dict      =    file_names
							snake_case__      :   Tuple      =    image_transform
							snake_case__      :   Optional[Any]      =    label_to_id



			def __len__(     self		:						str			)      ->      Dict:
							"""simple docstring"""





							return len(self.file_names			)







			def __getitem__(     self		:						Any    ,			__UpperCamelCase		:						str			)      ->      Union[str, Any]:
							"""simple docstring"""





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



def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Optional[Any]			)		->						int:
				# Initialize accelerator
				snake_case__      :   Union[str, Any]      =    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__      :   Any      =    config['''lr''']
				snake_case__      :   Dict      =    int(config['''num_epochs''']			)
				snake_case__      :   str      =    int(config['''seed''']			)
				snake_case__      :   Union[str, Any]      =    int(config['''batch_size''']			)
				snake_case__      :   int      =    config['''image_size''']
				if not isinstance(UpperCamelCase__   ,				(list, tuple)			):
								snake_case__      :   List[str]      =    (image_size, image_size)

				# Grab all the image filenames
				snake_case__      :   str      =    [os.path.join(args.data_dir   ,				UpperCamelCase__			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

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

				# For training we use a simple RandomResizedCrop
				snake_case__      :   Dict      =    Compose([RandomResizedCrop(UpperCamelCase__   ,				scale=(0.5, 1.0)			), ToTensor()]			)
				snake_case__      :   Union[str, Any]      =    PetsDataset(
				    [file_names[i] for i in train_split]   ,				image_transform=UpperCamelCase__   ,				label_to_id=UpperCamelCase__			)

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

				# Instantiate dataloaders.
				snake_case__      :   int      =    DataLoader(UpperCamelCase__   ,				shuffle=UpperCamelCase__   ,				batch_size=UpperCamelCase__   ,				num_workers=4			)
				snake_case__      :   Dict      =    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)
				snake_case__      :   Dict      =    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).
				snake_case__      :   Optional[Any]      =    model.to(accelerator.device			)

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

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

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

				# Instantiate learning rate scheduler
				snake_case__      :   List[str]      =    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.
				snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    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`.
												snake_case__      :   Optional[int]      =    {k: v.to(accelerator.device			) for k, v in batch.items()}
												snake_case__      :   Optional[Any]      =    (batch['''image'''] - mean) / std
												snake_case__      :   Optional[Any]      =    model(UpperCamelCase__			)
												snake_case__      :   str      =    torch.nn.functional.cross_entropy(UpperCamelCase__   ,				batch['''label''']			)
												accelerator.backward(UpperCamelCase__			)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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


def 							__UpperCAmelCase				(				)		->						Optional[int]:
				snake_case__      :   List[str]      =    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.'''			)
				snake_case__      :   Union[str, Any]      =    parser.parse_args()
				snake_case__      :   str      =    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(UpperCamelCase__   ,				UpperCamelCase__			)


if __name__ == "__main__":
				main()





'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Dict   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				Union[str, Any]   							=       """VideoMAEImageProcessor"""
			__magic_name__ :				List[str]   							=       ("""CLIPTokenizer""", """CLIPTokenizerFast""")



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       int				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       Optional[int]				=    kwargs.pop("feature_extractor"					)

									snake_case :       Union[str, Any]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."					)
									if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       List[Any]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and videos is None:
															raise ValueError("You have to specify either text or videos. Both cannot be none."					)

									if text is not None:
															snake_case :       List[Any]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if videos is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and videos is not None:
															snake_case :       Tuple				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





