


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 		snake_case_   (				lowercase__     :    List[Any]     ):






				'''simple docstring'''






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


class        __lowerCamelCase      (				lowerCamelCase_ ):


				"""simple docstring"""







				def __init__(				self     :    int	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Union[str, Any]=None	,							lowerCamelCase_     :    int=None					):
								_lowerCAmelCase  						=file_names
								_lowerCAmelCase  						=image_transform
								_lowerCAmelCase  						=label_to_id






				def __len__(				self     :    Optional[int]					):
								return len(self.file_names					)






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

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    Union[str, Any]     ):






				'''simple docstring'''






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

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

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

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

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

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

				# Instantiate dataloaders.
				_lowerCAmelCase  						=DataLoader(lowercase__       ,     shuffle=lowercase__       ,     batch_size=lowercase__       ,     num_workers=4     )
				_lowerCAmelCase  						=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)
				_lowerCAmelCase  						=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).
				_lowerCAmelCase  						=model.to(accelerator.device     )

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

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

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

				# Instantiate learning rate scheduler
				_lowerCAmelCase  						=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.
				_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase  						=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`.
												_lowerCAmelCase  						={k: v.to(accelerator.device     ) for k, v in batch.items()}
												_lowerCAmelCase  						=(batch["""image"""] - mean) / std
												_lowerCAmelCase  						=model(lowercase__     )
												_lowerCAmelCase  						=torch.nn.functional.cross_entropy(lowercase__       ,     batch["""label"""]     )
												accelerator.backward(lowercase__     )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

								_lowerCAmelCase  						=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 		snake_case_   (				):






				'''simple docstring'''






				_lowerCAmelCase  						=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."""     )
				_lowerCAmelCase  						=parser.parse_args()
				_lowerCAmelCase  						={"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 2_24}
				training_function(lowercase__       ,     lowercase__     )


if __name__ == "__main__":
		main()





from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

