


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__							):
			SCREAMING_SNAKE_CASE_						:			int       						= fname.split(os.path.sep							)[-1]
			return re.search(r'^(.*)_\d+\.jpg$',	SCREAMING_SNAKE_CASE__							).groups()[0]







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







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





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= file_names
										SCREAMING_SNAKE_CASE_						:			Tuple       						= image_transform
										SCREAMING_SNAKE_CASE_						:			List[str]       						= label_to_id






							def __len__(	self    ):





										"""simple docstring"""




										return len(self.file_names    )






							def __getitem__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




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







def 		a__       (							A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			int       						= Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision							)

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

			# Grab all the image filenames
			SCREAMING_SNAKE_CASE_						:			Optional[int]       						= [os.path.join(args.data_dir,	SCREAMING_SNAKE_CASE__							) for fname in os.listdir(args.data_dir							) if fname.endswith('.jpg'							)]

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

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

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

			# For training we use a simple RandomResizedCrop
			SCREAMING_SNAKE_CASE_						:			Tuple       						= Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE__,	scale=(0.5, 1.0)							), ToTensor()]							)
			SCREAMING_SNAKE_CASE_						:			Dict       						= PetsDataset(
			    [file_names[i] for i in train_split],	image_transform=SCREAMING_SNAKE_CASE__,	label_to_id=SCREAMING_SNAKE_CASE__							)

			# For evaluation, we use a deterministic Resize
			SCREAMING_SNAKE_CASE_						:			Tuple       						= Compose([Resize(SCREAMING_SNAKE_CASE__							), ToTensor()]							)
			SCREAMING_SNAKE_CASE_						:			str       						= PetsDataset([file_names[i] for i in eval_split],	image_transform=SCREAMING_SNAKE_CASE__,	label_to_id=SCREAMING_SNAKE_CASE__							)

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE_						:			Tuple       						= DataLoader(SCREAMING_SNAKE_CASE__,	shuffle=SCREAMING_SNAKE_CASE__,	batch_size=SCREAMING_SNAKE_CASE__,	num_workers=4							)
			SCREAMING_SNAKE_CASE_						:			Tuple       						= DataLoader(SCREAMING_SNAKE_CASE__,	shuffle=SCREAMING_SNAKE_CASE__,	batch_size=SCREAMING_SNAKE_CASE__,	num_workers=4							)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= create_model('resnet50d',	pretrained=SCREAMING_SNAKE_CASE__,	num_classes=len(SCREAMING_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).
			SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.to(accelerator.device							)

			# Freezing the base model
			for param in model.parameters():
						SCREAMING_SNAKE_CASE_						:			Dict       						= False
			for param in model.get_classifier().parameters():
						SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= True

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

			# Instantiate optimizer
			SCREAMING_SNAKE_CASE_						:			List[str]       						= torch.optim.Adam(params=model.parameters(),	lr=lr / 2_5							)

			# Instantiate learning rate scheduler
			SCREAMING_SNAKE_CASE_						:			int       						= OneCycleLR(optimizer=SCREAMING_SNAKE_CASE__,	max_lr=SCREAMING_SNAKE_CASE__,	epochs=SCREAMING_SNAKE_CASE__,	steps_per_epoch=len(SCREAMING_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.
			SCREAMING_SNAKE_CASE_						:			Dict       						= accelerator.prepare(
			    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__							)

			# Now we train the model
			for epoch in range(SCREAMING_SNAKE_CASE__							):
						model.train()
						for step, batch in enumerate(SCREAMING_SNAKE_CASE__							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									SCREAMING_SNAKE_CASE_						:			Tuple       						= {k: v.to(accelerator.device							) for k, v in batch.items()}
									SCREAMING_SNAKE_CASE_						:			Any       						= (batch['''image'''] - mean) / std
									SCREAMING_SNAKE_CASE_						:			Tuple       						= model(SCREAMING_SNAKE_CASE__							)
									SCREAMING_SNAKE_CASE_						:			str       						= torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE__,	batch['label']							)
									accelerator.backward(SCREAMING_SNAKE_CASE__							)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

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

						SCREAMING_SNAKE_CASE_						:			Any       						= 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 		a__       (							):
			SCREAMING_SNAKE_CASE_						:			Tuple       						= argparse.ArgumentParser(description='Simple example of training script.'							)
			parser.add_argument('--data_dir',	required=SCREAMING_SNAKE_CASE__,	help='The data folder on disk.'							)
			parser.add_argument(
			    '--mixed_precision',	type=SCREAMING_SNAKE_CASE__,	default=SCREAMING_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=SCREAMING_SNAKE_CASE__,	default=SCREAMING_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.'							)
			SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= parser.parse_args()
			SCREAMING_SNAKE_CASE_						:			Tuple       						= {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
			training_function(SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
				main()







'''simple docstring'''
import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
lowercase__		    =	False
lowercase__		    =	Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
lowercase__		    =	datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       List[str]=sys.argv[1:]	)		->       str:






				"""simple docstring"""







				snake_case   :    List[str]			       =		None
				snake_case   :    Tuple			       =		None
				snake_case   :    List[Any]			       =		None
				snake_case   :    Tuple			       =		1
				snake_case       ,snake_case   :    List[Any]			       =		getopt.getopt(UpperCamelCase__   ,     '''i:o:b:s'''   ,     ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']	)
				for opt, arg in opts:
						if opt in ("-i", "--inputdir"):
								snake_case   :    Any			       =		arg
						elif opt in ("-o", "--outfile"):
								snake_case   :    str			       =		arg
						elif opt in ("-b", "--batch_size"):
								snake_case   :    Optional[Any]			       =		int(UpperCamelCase__	)
						elif opt in ("-s", "--subset_list"):
								snake_case   :    List[str]			       =		arg

				assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
				assert outputfile is not None and not os.path.isfile(UpperCamelCase__	), f'{outputfile}'
				if subset_list is not None:
						with open(os.path.realpath(UpperCamelCase__	)	) as f:
								snake_case   :    Optional[Any]			       =		{self._vqa_file_split()[0] for x in tryload(UpperCamelCase__	)}
				else:
						snake_case   :    Union[str, Any]			       =		None

				snake_case   :    Any			       =		CONFIG
				if torch.cuda.is_available():
						snake_case   :    List[Any]			       =		'''cuda'''
				snake_case   :    Dict			       =		os.path.realpath(UpperCamelCase__	)
				snake_case   :    List[str]			       =		os.path.realpath(UpperCamelCase__	)
				snake_case   :    Optional[int]			       =		Preprocess(self.config	)
				snake_case   :    int			       =		GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''   ,     config=self.config	)
				snake_case   :    Optional[Any]			       =		batch_size if batch_size != 0 else 1
				snake_case   :    Tuple			       =		DEFAULT_SCHEMA



		def 	lowerCAmelCase							(	self							:       Optional[int]   ,     UpperCamelCase__							:       List[str]	)		->       List[Any]:






				"""simple docstring"""







				snake_case   :    str			       =		int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
				snake_case   :    Optional[Any]			       =		os.path.join(self.inputdir   ,     UpperCamelCase__	)
				return (img_id, filepath)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		[]
				for i, file in enumerate(os.listdir(self.inputdir	)	):
						if self.subset_list is not None and i not in self.subset_list:
								continue
						batch.append(self._vqa_file_split(UpperCamelCase__	)	)
						if len(UpperCamelCase__	) == self.batch:
								snake_case   :    Optional[int]			       =		batch
								snake_case   :    int			       =		[]
								yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)

				for i in range(1	):
						yield list(map(UpperCamelCase__   ,     zip(*UpperCamelCase__	)	)	)



		def __call__(	self							:       Dict	)		->       str:






				"""simple docstring"""







				if not TEST:
						snake_case   :    Any			       =		datasets.ArrowWriter(features=self.schema   ,     path=self.outputfile	)
				# do file generator
				for i, (img_ids, filepaths) in enumerate(self.file_generator	):
						snake_case       ,snake_case       ,snake_case   :    Dict			       =		self.preprocess(UpperCamelCase__	)
						snake_case   :    List[Any]			       =		self.model(
						    UpperCamelCase__   ,     UpperCamelCase__   ,     scales_yx=UpperCamelCase__   ,     padding='''max_detections'''   ,     max_detections=self.config.MAX_DETECTIONS   ,     pad_value=0   ,     return_tensors='''np'''   ,     location='''cpu'''   ,     )
						snake_case   :    Dict			       =		output_dict.pop('''normalized_boxes'''	)
						if not TEST:
								snake_case   :    str			       =		np.array(UpperCamelCase__	)
								snake_case   :    Tuple			       =		self.schema.encode_batch(UpperCamelCase__	)
								writer.write_batch(UpperCamelCase__	)
						if TEST:
								break
						# finalizer the writer
				if not TEST:
						snake_case       ,snake_case   :    int			       =		writer.finalize()
						print(f'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)







def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		try:
				snake_case   :    Optional[int]			       =		json.load(SCREAMING_SNAKE_CASE__       )
				try:
						snake_case   :    Union[str, Any]			       =		list(data.keys()       )
				except Exception:
						snake_case   :    Any			       =		[d['''img_id'''] for d in data]
		except Exception:
				try:
						snake_case   :    Tuple			       =		eval(stream.read()       )
				except Exception:
						snake_case   :    List[Any]			       =		stream.read().split('''\n'''       )
		return data


if __name__ == "__main__":
						lowercase__		    =	Extract(sys.argv[1:])
						extract()
						if not TEST:
												lowercase__		    =	datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)


