

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
_lowerCAmelCase      :    List[str]						       =    False
_lowerCAmelCase      :    str						       =    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCAmelCase      :    List[str]						       =    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_048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class 			__magic_name__					:





			def __init__(	self      ,						__snake_case=sys.argv[1:]      )     ->				Tuple:




							'''simple docstring'''
							__a						   =None
							__a						   =None
							__a						   =None
							__a						   =1
							__a   ,					__a						   =getopt.getopt(lowerCamelCase_      ,						'i:o:b:s'      ,						['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']      )
							for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
															__a						   =arg
											elif opt in ("-o", "--outfile"):
															__a						   =arg
											elif opt in ("-b", "--batch_size"):
															__a						   =int(lowerCamelCase_      )
											elif opt in ("-s", "--subset_list"):
															__a						   =arg

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

							__a						   =CONFIG
							if torch.cuda.is_available():
											__a						   ='cuda'
							__a						   =os.path.realpath(lowerCamelCase_      )
							__a						   =os.path.realpath(lowerCamelCase_      )
							__a						   =Preprocess(self.config      )
							__a						   =GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'      ,						config=self.config      )
							__a						   =batch_size if batch_size != 0 else 1
							__a						   =DEFAULT_SCHEMA





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[int]:




							'''simple docstring'''
							__a						   =int(file.split('.'      )[0].split('_'      )[-1]      )
							__a						   =os.path.join(self.inputdir      ,						lowerCamelCase_      )
							return (img_id, filepath)





			@property
			def 	__magic_name__					(	self      )     ->				Any:




							'''simple docstring'''
							__a						   =[]
							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(lowerCamelCase_      )      )
											if len(lowerCamelCase_      ) == self.batch:
															__a						   =batch
															__a						   =[]
															yield list(map(lowerCamelCase_      ,						zip(*lowerCamelCase_      )      )      )

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





			def __call__(	self      )     ->				Dict:




							'''simple docstring'''

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






def  UpperCamelCase_(							_snake_case     :						Optional[Any]			):




				"""simple docstring"""






				try:
								__a						   =json.load(a_			)
								try:
												__a						   =list(data.keys()			)
								except Exception:
												__a						   =[d['img_id'] for d in data]
				except Exception:
								try:
												__a						   =eval(stream.read()			)
								except Exception:
												__a						   =stream.read().split('\n'			)
				return data


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









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  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






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






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=None      ,						__snake_case=None      )     ->				Tuple:




						'''simple docstring'''
						__a						   =file_names
						__a						   =image_transform
						__a						   =label_to_id





			def __len__(	self      )     ->				int:




						'''simple docstring'''
						return len(self.file_names      )





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




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






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Optional[int]			):




			"""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(_snake_case ,  (list, tuple)			):
						__a						   =(image_size, image_size)

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

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

			# For training we use a simple RandomResizedCrop
			__a						   =Compose([RandomResizedCrop(_snake_case ,  scale=(0.5, 1.0)			), ToTensor()]			)
			__a						   =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
			__a						   =Compose([Resize(_snake_case			), ToTensor()]			)
			__a						   =PetsDataset([file_names[i] for i in eval_split] ,  image_transform=_snake_case ,  label_to_id=_snake_case			)

			# Instantiate dataloaders.
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)
			__a						   =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)
			__a						   =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).
			__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=_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.
			__a   ,					__a   ,					__a   ,					__a   ,					__a						   =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`.
									__a						   ={k: v.to(accelerator.device			) for k, v in batch.items()}
									__a						   =(batch['image'] - mean) / std
									__a						   =model(_snake_case			)
									__a						   =torch.nn.functional.cross_entropy(_snake_case ,  batch['label']			)
									accelerator.backward(_snake_case			)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

						model.eval()
						__a						   =0
						__a						   =0
						for _, batch in enumerate(_snake_case			):
									# 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(_snake_case			)
									__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  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =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.'			)
			__a						   =parser.parse_args()
			__a						   ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_snake_case ,  _snake_case			)


if __name__ == "__main__":
						main()






