
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_			(				snake_case				: Union[str, Any]			)   ->     Optional[Any]:



						'''simple docstring'''

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      file_names
													__UpperCamelCase  	=      image_transform
													__UpperCamelCase  	=      label_to_id



							def __len__(	self      )-> Any:





													'''simple docstring'''






													return len(self.file_names      )



							def __getitem__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






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





def 						A_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     int:



						'''simple docstring'''

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

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

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

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

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

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

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

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

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

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

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

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




def 						A_			(				)   ->     Any:



						'''simple docstring'''

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


if __name__ == "__main__":
				main()





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
UpperCAmelCase      		=					False
UpperCAmelCase      		=					Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase      		=					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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase  :							Dict	    =	arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	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:
                      _UpperCamelCase  :							str	    =	{self._vqa_file_split()[0] for x in tryload(lowerCamelCase	)}
        else:
               _UpperCamelCase  :							Any	    =	None

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

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							str	    =	[]
        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:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

 def __call__(      self							:		Optional[Any]	)			->				Any:
        # make writer
        if not TEST:
               _UpperCamelCase  :							int	    =	datasets.ArrowWriter(features=self.schema							,      path=self.outputfile	)
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator	):
               _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase  :							Any	    =	self.preprocess(lowerCamelCase	)
               _UpperCamelCase  :							Any	    =	self.model(
                   lowerCamelCase							,      lowerCamelCase							,      scales_yx=lowerCamelCase							,      padding='max_detections'							,      max_detections=self.config.MAX_DETECTIONS							,      pad_value=0							,      return_tensors='np'							,      location='cpu'							,      )
               _UpperCamelCase  :							Tuple	    =	output_dict.pop('normalized_boxes'	)
               if not TEST:
                      _UpperCamelCase  :							Dict	    =	np.array(lowerCamelCase	)
                      _UpperCamelCase  :							Optional[int]	    =	self.schema.encode_batch(lowerCamelCase	)
                      writer.write_batch(lowerCamelCase	)
               if TEST:
                      break
               # finalizer the writer
        if not TEST:
               _UpperCamelCase    ,   _UpperCamelCase  :							Tuple	    =	writer.finalize()
               print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"	)







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


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



