import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import CLIPImageProcessor




class      lowercase_						(						unittest.TestCase		):


	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=7	,    __SCREAMING_SNAKE_CASE=3	,    __SCREAMING_SNAKE_CASE=18	,    __SCREAMING_SNAKE_CASE=30	,    __SCREAMING_SNAKE_CASE=400	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,    __SCREAMING_SNAKE_CASE=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,    __SCREAMING_SNAKE_CASE=True	,    )		->str:
								lowerCAmelCase				=	size if size is not None else {'''shortest_edge''': 20}
								lowerCAmelCase				=	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCAmelCase				=	parent
								lowerCAmelCase				=	batch_size
								lowerCAmelCase				=	num_channels
								lowerCAmelCase				=	image_size
								lowerCAmelCase				=	min_resolution
								lowerCAmelCase				=	max_resolution
								lowerCAmelCase				=	do_resize
								lowerCAmelCase				=	size
								lowerCAmelCase				=	do_center_crop
								lowerCAmelCase				=	crop_size
								lowerCAmelCase				=	do_normalize
								lowerCAmelCase				=	image_mean
								lowerCAmelCase				=	image_std
								lowerCAmelCase				=	do_convert_rgb


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE=False	,    __SCREAMING_SNAKE_CASE=False	,    __SCREAMING_SNAKE_CASE=False       )		->Optional[Any]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
															lowerCAmelCase				=	[]
															for i in range(self.batch_size       ):
																						image_inputs.append(
																						    np.random.randint(
																						        255	,    size=(self.num_channels, self.max_resolution, self.max_resolution)	,    dtype=np.uinta       )       )
								else:
															lowerCAmelCase				=	[]
															for i in range(self.batch_size       ):
																						lowerCAmelCase	,   lowerCAmelCase				=	np.random.choice(np.arange(self.min_resolution	,    self.max_resolution       )	,    2       )
																						image_inputs.append(np.random.randint(255	,    size=(self.num_channels, width, height)	,    dtype=np.uinta       )       )

								if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowerCAmelCase				=	[Image.fromarray(np.moveaxis(_lowerCAmelCase	,    0	,    -1       )       ) for x in image_inputs]

								if torchify:
															lowerCAmelCase				=	[torch.from_numpy(_lowerCAmelCase       ) for x in image_inputs]

								return image_inputs




@require_torch
@require_vision
class      lowercase_						(						A__   ,  unittest.TestCase		):
	UpperCAmelCase_						:      Union[str, Any]     =       CLIPImageProcessor if is_vision_available() else None


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
								lowerCAmelCase				=	CLIPImageProcessingTester(self       )


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_resize'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''size'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_center_crop'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''center_crop'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_normalize'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''image_mean'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''image_std'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_convert_rgb'''       )       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
								lowerCAmelCase				=	self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size	,    {'''shortest_edge''': 20}       )
								self.assertEqual(image_processor.crop_size	,    {'''height''': 18, '''width''': 18}       )

								lowerCAmelCase				=	self.image_processing_class.from_dict(self.image_processor_dict	,    size=42	,    crop_size=84       )
								self.assertEqual(image_processor.size	,    {'''shortest_edge''': 42}       )
								self.assertEqual(image_processor.crop_size	,    {'''height''': 84, '''width''': 84}       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Any:
								pass


	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCAmelCase				=	self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,    Image.Image       )

								# Test not batched input
								lowerCAmelCase				=	image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(_lowerCAmelCase	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCAmelCase				=	self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase	,    numpify=_lowerCAmelCase       )
								for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,    np.ndarray       )

								# Test not batched input
								lowerCAmelCase				=	image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(_lowerCAmelCase	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )




	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCAmelCase				=	self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase	,    torchify=_lowerCAmelCase       )
								for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,    torch.Tensor       )

								# Test not batched input
								lowerCAmelCase				=	image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(_lowerCAmelCase	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )




@require_torch
@require_vision
class      lowercase_						(						A__   ,  unittest.TestCase		):
	UpperCAmelCase_						:      int     =       CLIPImageProcessor if is_vision_available() else None


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
								lowerCAmelCase				=	CLIPImageProcessingTester(self	,    num_channels=4       )
								lowerCAmelCase				=	3


	@property
	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
								return self.image_processor_tester.prepare_image_processor_dict()


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_resize'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''size'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_center_crop'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''center_crop'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_normalize'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''image_mean'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''image_std'''       )       )
								self.assertTrue(hasattr(_lowerCAmelCase	,    '''do_convert_rgb'''       )       )


	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
								pass




	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
								# Initialize image_processing
								lowerCAmelCase				=	self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCAmelCase				=	self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
															self.assertIsInstance(_lowerCAmelCase	,    Image.Image       )

								# Test not batched input
								lowerCAmelCase				=	image_processing(image_inputs[0]	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )

								# Test batched
								lowerCAmelCase				=	image_processing(_lowerCAmelCase	,    return_tensors='''pt'''       ).pixel_values
								self.assertEqual(
								    encoded_images.shape	,    (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )	,    )




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




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								lowerCAmelCase				=	file_names
								lowerCAmelCase				=	image_transform
								lowerCAmelCase				=	label_to_id


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




	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->int:
								lowerCAmelCase				=	self.file_names[idx]
								lowerCAmelCase				=	PIL.Image.open(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	raw_image.convert('''RGB'''       )
								if self.image_transform is not None:
															lowerCAmelCase				=	self.image_transform(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	extract_label(__SCREAMING_SNAKE_CASE       )
								if self.label_to_id is not None:
															lowerCAmelCase				=	self.label_to_id[label]
								return {"image": image, "label": label}

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# Initialize accelerator
							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(snake_case__     ,						(list, tuple)							):
														lowerCAmelCase				=	(image_size, image_size)

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

							# Build the label correspondences
							lowerCAmelCase				=	[extract_label(snake_case__							) for fname in file_names]
							lowerCAmelCase				=	list(set(snake_case__							)							)
							id_to_label.sort()
							lowerCAmelCase				=	{lbl: i for i, lbl in enumerate(snake_case__							)}

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

							# Split our filenames between train and validation
							lowerCAmelCase				=	np.random.permutation(len(snake_case__							)							)
							lowerCAmelCase				=	int(0.8 * len(snake_case__							)							)
							lowerCAmelCase				=	random_perm[:cut]
							lowerCAmelCase				=	random_perm[cut:]

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

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

							# Instantiate dataloaders.
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						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=snake_case__     ,						num_classes=len(snake_case__							)							)

							# 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 / 2_5							)

							# Instantiate learning rate scheduler
							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)

							# 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(
							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)

							# Now we train the model
							for epoch in range(snake_case__							):
														model.train()
														for step, batch in enumerate(snake_case__							):
																					# 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(snake_case__							)
																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
																					accelerator.backward(snake_case__							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

														model.eval()
														lowerCAmelCase				=	0
														lowerCAmelCase				=	0
														for _, batch in enumerate(snake_case__							):
																					# 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(snake_case__							)
																					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_0_0 * eval_metric:.2f}"							)

def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
							parser.add_argument(
							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						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=snake_case__     ,						default=snake_case__     ,						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''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(snake_case__     ,						snake_case__							)


if __name__ == "__main__":
	main()



