


'''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      a (     __a  )    ->      Optional[int]:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					fname.split(os.path.sep  )[-1]
					return re.search(R'''^(.*)_\d+\.jpg$'''					,   __a  ).groups()[0]
class   lowercase      (				A__   ):

		"""simple docstring"""

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

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					file_names
							UpperCamelCase__  :Optional[int]				   =					image_transform
							UpperCamelCase__  :Union[str, Any]				   =					label_to_id




		def __len__(						self				):

							'''simple docstring'''


							return len(self.file_names				)




		def __getitem__(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


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



def      a (     __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






					if args.with_tracking:
										UpperCamelCase__  :Optional[int]				   =					Accelerator(
										    cpu=args.cpu					,   mixed_precision=args.mixed_precision					,   log_with='''all'''					,   project_dir=args.project_dir  )
					else:
										UpperCamelCase__  :Optional[Any]				   =					Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision  )

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

					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps					,   '''isdigit'''  ):
										if args.checkpointing_steps == "epoch":
															UpperCamelCase__  :Any				   =					args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															UpperCamelCase__  :List[Any]				   =					int(args.checkpointing_steps  )
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''  )
					else:
										UpperCamelCase__  :Dict				   =					None

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										UpperCamelCase__  :Dict				   =					os.path.split(__a  )[-1].split('''.'''  )[0]
										accelerator.init_trackers(__a					,   __a  )

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

					# Build the label correspondences
					UpperCamelCase__  :Optional[int]				   =					[extract_label(__a  ) for fname in file_names]
					UpperCamelCase__  :Optional[Any]				   =					list(set(__a  )  )
					id_to_label.sort()
					UpperCamelCase__  :Any				   =					{lbl: i for i, lbl in enumerate(__a  )}

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

					# Split our filenames between train and validation
					UpperCamelCase__  :List[Any]				   =					np.random.permutation(len(__a  )  )
					UpperCamelCase__  :int				   =					int(0.8 * len(__a  )  )
					UpperCamelCase__  :Dict				   =					random_perm[:cut]
					UpperCamelCase__  :Dict				   =					random_perm[cut:]

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

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

					# Instantiate dataloaders.
					UpperCamelCase__  :str				   =					DataLoader(__a					,   shuffle=__a					,   batch_size=__a					,   num_workers=4  )
					UpperCamelCase__  :Any				   =					DataLoader(__a					,   shuffle=__a					,   batch_size=__a					,   num_workers=4  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :Dict				   =					create_model('''resnet50d'''					,   pretrained=__a					,   num_classes=len(__a  )  )

					# 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).
					UpperCamelCase__  :str				   =					model.to(accelerator.device  )

					# Freezing the base model
					for param in model.parameters():
										UpperCamelCase__  :Tuple				   =					False
					for param in model.get_classifier().parameters():
										UpperCamelCase__  :Union[str, Any]				   =					True

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

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

					# Instantiate learning rate scheduler
					UpperCamelCase__  :Union[str, Any]				   =					OneCycleLR(optimizer=__a					,   max_lr=__a					,   epochs=__a					,   steps_per_epoch=len(__a  )  )

					# 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.
					UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					accelerator.prepare(
					    __a					,   __a					,   __a					,   __a					,   __a  )
					# We need to keep track of how many total steps we have iterated over
					UpperCamelCase__  :Optional[int]				   =					0
					# We also need to keep track of the starting epoch so files are named properly
					UpperCamelCase__  :Optional[Any]				   =					0

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''  )
															accelerator.load_state(args.resume_from_checkpoint  )
															UpperCamelCase__  :Tuple				   =					os.path.basename(args.resume_from_checkpoint  )
										else:
															# Get the most recent checkpoint
															UpperCamelCase__  :Any				   =					[f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
															dirs.sort(key=os.path.getctime  )
															UpperCamelCase__  :Tuple				   =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										UpperCamelCase__  :Union[str, Any]				   =					os.path.splitext(__a  )[0]

										if "epoch" in training_difference:
															UpperCamelCase__  :List[Any]				   =					int(training_difference.replace('''epoch_'''					,   ''''''  )  ) + 1
															UpperCamelCase__  :List[Any]				   =					None
										else:
															UpperCamelCase__  :int				   =					int(training_difference.replace('''step_'''					,   ''''''  )  )
															UpperCamelCase__  :Any				   =					resume_step // len(__a  )
															resume_step -= starting_epoch * len(__a  )

    # Now we train the model
					for epoch in range(__a					,   __a  ):
										model.train()
										if args.with_tracking:
															UpperCamelCase__  :List[Any]				   =					0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															UpperCamelCase__  :Optional[Any]				   =					accelerator.skip_first_batches(__a					,   __a  )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															UpperCamelCase__  :Dict				   =					train_dataloader
										for batch in active_dataloader:
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															UpperCamelCase__  :Tuple				   =					{k: v.to(accelerator.device  ) for k, v in batch.items()}
															UpperCamelCase__  :Optional[int]				   =					(batch['''image'''] - mean) / std
															UpperCamelCase__  :str				   =					model(__a  )
															UpperCamelCase__  :List[str]				   =					torch.nn.functional.cross_entropy(__a					,   batch['''label''']  )
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(__a  )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															overall_step += 1
															if isinstance(__a					,   __a  ):
																				UpperCamelCase__  :Any				   =					f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														UpperCamelCase__  :List[str]				   =					os.path.join(args.output_dir					,   __a  )
																									accelerator.save_state(__a  )
										model.eval()
										UpperCamelCase__  :Any				   =					0
										UpperCamelCase__  :Tuple				   =					0
										for step, batch in enumerate(__a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															UpperCamelCase__  :Tuple				   =					{k: v.to(accelerator.device  ) for k, v in batch.items()}
															UpperCamelCase__  :Tuple				   =					(batch['''image'''] - mean) / std
															with torch.no_grad():
																				UpperCamelCase__  :Tuple				   =					model(__a  )
															UpperCamelCase__  :Optional[Any]				   =					outputs.argmax(dim=-1  )
															UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					accelerator.gather_for_metrics((predictions, batch['''label'''])  )
															UpperCamelCase__  :str				   =					predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										UpperCamelCase__  :List[str]				   =					accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''  )
										if args.with_tracking:
															accelerator.log(
															    {
															        '''accuracy''': 100 * eval_metric,
															        '''train_loss''': total_loss.item() / len(__a  ),
															        '''epoch''': epoch,
															    }					,   step=__a					,   )
										if checkpointing_steps == "epoch":
															UpperCamelCase__  :str				   =					f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				UpperCamelCase__  :List[Any]				   =					os.path.join(args.output_dir					,   __a  )
															accelerator.save_state(__a  )

					if args.with_tracking:
										accelerator.end_training()


def      a (     )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					argparse.ArgumentParser(description='''Simple example of training script.'''  )
					parser.add_argument('''--data_dir'''					,   required=__a					,   help='''The data folder on disk.'''  )
					parser.add_argument('''--fp16'''					,   action='''store_true'''					,   help='''If passed, will use FP16 training.'''  )
					parser.add_argument(
					    '''--mixed_precision'''					,   type=__a					,   default=__a					,   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('''--cpu'''					,   action='''store_true'''					,   help='''If passed, will train on the CPU.'''  )
					parser.add_argument(
					    '''--checkpointing_steps'''					,   type=__a					,   default=__a					,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''					,   )
					parser.add_argument(
					    '''--output_dir'''					,   type=__a					,   default='''.'''					,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''					,   )
					parser.add_argument(
					    '''--resume_from_checkpoint'''					,   type=__a					,   default=__a					,   help='''If the training should continue from a checkpoint folder.'''					,   )
					parser.add_argument(
					    '''--with_tracking'''					,   action='''store_true'''					,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''					,   )
					parser.add_argument(
					    '''--project_dir'''					,   type=__a					,   default='''logs'''					,   help='''Location on where to store experiment tracking logs` and relevent project information'''					,   )
					UpperCamelCase__  :int				   =					parser.parse_args()
					UpperCamelCase__  :Tuple				   =					{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()



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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case				 =	{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''CLIPTokenizerFast''']

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''CLIPFeatureExtractor''']
		__snake_case				 =	['''CLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CLIPModel''',
		    '''CLIPPreTrainedModel''',
		    '''CLIPTextModel''',
		    '''CLIPTextModelWithProjection''',
		    '''CLIPVisionModel''',
		    '''CLIPVisionModelWithProjection''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCLIPModel''',
		    '''TFCLIPPreTrainedModel''',
		    '''TFCLIPTextModel''',
		    '''TFCLIPVisionModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxCLIPModel''',
		    '''FlaxCLIPPreTrainedModel''',
		    '''FlaxCLIPTextModel''',
		    '''FlaxCLIPTextPreTrainedModel''',
		    '''FlaxCLIPVisionModel''',
		    '''FlaxCLIPVisionPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

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