






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



class 				_UpperCamelCase      (a_      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None						)->						List[str]:
           __lowerCAmelCase             =       file_names
           __lowerCAmelCase             =       image_transform
           __lowerCAmelCase             =       label_to_id


      def __len__(						self						)->						Any:
           return len(self.file_names						)


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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     # Initialize accelerator
     if args.with_tracking:
          __lowerCAmelCase             =       Accelerator(
              cpu=args.cpu       ,			mixed_precision=args.mixed_precision       ,			log_with="all"       ,			project_dir=args.project_dir							)
     else:
          __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)

     # Parse out whether we are saving every epoch or after a certain number of batches
     if hasattr(args.checkpointing_steps       ,			"isdigit"							):
          if args.checkpointing_steps == "epoch":
               __lowerCAmelCase             =       args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __lowerCAmelCase             =       int(args.checkpointing_steps							)
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
     else:
          __lowerCAmelCase             =       None

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          __lowerCAmelCase             =       os.path.split(__snake_case							)[-1].split("."							)[0]
          accelerator.init_trackers(__snake_case       ,			__snake_case							)

     # 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 / 25							)

     # 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							)
     # We need to keep track of how many total steps we have iterated over
     __lowerCAmelCase             =       0
     # We also need to keep track of the starting epoch so files are named properly
     __lowerCAmelCase             =       0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
               accelerator.load_state(args.resume_from_checkpoint							)
               __lowerCAmelCase             =       os.path.basename(args.resume_from_checkpoint							)
          else:
               # Get the most recent checkpoint
               __lowerCAmelCase             =       [f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
               dirs.sort(key=os.path.getctime							)
               __lowerCAmelCase             =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __lowerCAmelCase             =       os.path.splitext(__snake_case							)[0]

          if "epoch" in training_difference:
               __lowerCAmelCase             =       int(training_difference.replace("epoch_"       ,			""							)							) + 1
               __lowerCAmelCase             =       None
          else:
               __lowerCAmelCase             =       int(training_difference.replace("step_"       ,			""							)							)
               __lowerCAmelCase             =       resume_step // len(__snake_case							)
               resume_step -= starting_epoch * len(__snake_case							)

    # Now we train the model
     for epoch in range(__snake_case       ,			__snake_case							):
          model.train()
          if args.with_tracking:
               __lowerCAmelCase             =       0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               __lowerCAmelCase             =       accelerator.skip_first_batches(__snake_case       ,			__snake_case							)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               __lowerCAmelCase             =       train_dataloader
          for batch in active_dataloader:
               # 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"]							)
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__snake_case							)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(__snake_case       ,			__snake_case							):
                    __lowerCAmelCase             =       F"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              __lowerCAmelCase             =       os.path.join(args.output_dir       ,			__snake_case							)
                         accelerator.save_state(__snake_case							)
          model.eval()
          __lowerCAmelCase             =       0
          __lowerCAmelCase             =       0
          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
               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}: {100 * eval_metric:.2f}"""							)
          if args.with_tracking:
               accelerator.log(
                   {
                       "accuracy": 100 * eval_metric,
                       "train_loss": total_loss.item() / len(__snake_case							),
                       "epoch": epoch,
                   }       ,			step=__snake_case       ,			)
          if checkpointing_steps == "epoch":
               __lowerCAmelCase             =       F"""epoch_{epoch}"""
               if args.output_dir is not None:
                    __lowerCAmelCase             =       os.path.join(args.output_dir       ,			__snake_case							)
               accelerator.save_state(__snake_case							)

     if args.with_tracking:
          accelerator.end_training()





def    __lowerCAmelCase   (    ):
     __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("--fp16"       ,			action="store_true"       ,			help="If passed, will use FP16 training."							)
     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("--cpu"       ,			action="store_true"       ,			help="If passed, will train on the CPU."							)
     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(
         "--output_dir"       ,			type=__snake_case       ,			default="."       ,			help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,			)
     parser.add_argument(
         "--resume_from_checkpoint"       ,			type=__snake_case       ,			default=__snake_case       ,			help="If the training should continue from a checkpoint folder."       ,			)
     parser.add_argument(
         "--with_tracking"       ,			action="store_true"       ,			help="Whether to load in all available experiment trackers from the environment and use them for logging."       ,			)
     parser.add_argument(
         "--project_dir"       ,			type=__snake_case       ,			default="logs"       ,			help="Location on where to store experiment tracking logs` and relevent project information"       ,			)
     __lowerCAmelCase             =       parser.parse_args()
     __lowerCAmelCase             =       {"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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __lowerCAmelCase             =       [[1, 2, 4], [1, 2, 3, 4]]
           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)
           self.assertTrue(isinstance(dc.token_ids       ,    __UpperCamelCase						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __lowerCAmelCase             =       [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(__UpperCamelCase						)  # fails here


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(3						)
           __lowerCAmelCase             =       stepped is True and completed is True and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(4						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2, 4]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

           dc.reset()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 3						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 2						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.remaining() == 0						)
           self.assertTrue(dc.current_seq == [1, 2, 5]						)






