
"""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 __snake_case (      UpperCamelCase    )       ->    Optional[Any]:


		"""simple docstring"""





		a__   		=     fname.split(os.path.sep    )[-1]
		return re.search(r'''^(.*)_\d+\.jpg$'''						,					UpperCamelCase    ).groups()[0]
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def __init__(  self :Any       ,    __magic_name__ :Any       ,    __magic_name__ :List[str]=None       ,    __magic_name__ :Optional[Any]=None			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     file_names
			a__   		=     image_transform
			a__   		=     label_to_id


	def __len__(  self :str			)			->   Dict:

			'''simple docstring'''






			return len(self.file_names			)






	def __getitem__(  self :List[str]       ,    __magic_name__ :List[Any]			)			->   Any:

			'''simple docstring'''






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

def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    List[str]:


		"""simple docstring"""





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

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

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

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

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

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

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

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

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

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

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

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

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

def __snake_case (      )       ->    str:


		"""simple docstring"""





		a__   		=     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.'''    )
		a__   		=     parser.parse_args()
		a__   		=     {'''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 argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Tuple			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




