



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 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			fname.split(os.path.sep						)[-1]
       return re.search(R'''^(.*)_\d+\.jpg$''',    _UpperCAmelCase						).groups()[0]






class 	__snake_case	(     __UpperCamelCase):




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Any						, __lowerCAmelCase					:						Optional[int]=None						, __lowerCAmelCase					:						Any=None		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			file_names
         _lowerCamelCase						:    Optional[int]         =			image_transform
         _lowerCamelCase						:    int         =			label_to_id




  def __len__(			self					:						Tuple		):






         """simple docstring"""






         return len(self.file_names		)




  def __getitem__(			self					:						Optional[Any]						, __lowerCAmelCase					:						List[Any]		):






         """simple docstring"""






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






def 	snake_case_  (				A_ :		Dict,    A_ :		List[Any]						):


       '''simple docstring'''





       if args.with_tracking:
              _lowerCamelCase						:    Tuple         =			Accelerator(
                  cpu=args.cpu,    mixed_precision=args.mixed_precision,    log_with='''all''',    project_dir=args.project_dir						)
       else:
              _lowerCamelCase						:    Tuple         =			Accelerator(cpu=args.cpu,    mixed_precision=args.mixed_precision						)

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase						:    Optional[int]         =			config['lr']
       _lowerCamelCase						:    Union[str, Any]         =			int(config['''num_epochs''']						)
       _lowerCamelCase						:    Any         =			int(config['''seed''']						)
       _lowerCamelCase						:    Union[str, Any]         =			int(config['''batch_size''']						)
       _lowerCamelCase						:    Any         =			config['image_size']
       if not isinstance(_UpperCAmelCase,    (list, tuple)						):
              _lowerCamelCase						:    Dict         =			(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						:    Dict         =			args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     _lowerCamelCase						:    Optional[int]         =			int(args.checkpointing_steps						)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
       else:
              _lowerCamelCase						:    Union[str, Any]         =			None

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

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

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

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

       # Split our filenames between train and validation
       _lowerCamelCase						:    Dict         =			np.random.permutation(len(_UpperCAmelCase						)						)
       _lowerCamelCase						:    Tuple         =			int(0.8 * len(_UpperCAmelCase						)						)
       _lowerCamelCase						:    Optional[int]         =			random_perm[:cut]
       _lowerCamelCase						:    Optional[Any]         =			random_perm[cut:]

       # For training we use a simple RandomResizedCrop
       _lowerCamelCase						:    List[str]         =			Compose([RandomResizedCrop(_UpperCAmelCase,    scale=(0.5, 1.0)						), ToTensor()]						)
       _lowerCamelCase						:    List[str]         =			PetsDataset(
           [file_names[i] for i in train_split],    image_transform=_UpperCAmelCase,    label_to_id=_UpperCAmelCase						)

       # For evaluation, we use a deterministic Resize
       _lowerCamelCase						:    Dict         =			Compose([Resize(_UpperCAmelCase						), ToTensor()]						)
       _lowerCamelCase						:    Optional[int]         =			PetsDataset([file_names[i] for i in eval_split],    image_transform=_UpperCAmelCase,    label_to_id=_UpperCAmelCase						)

       # Instantiate dataloaders.
       _lowerCamelCase						:    List[Any]         =			DataLoader(_UpperCAmelCase,    shuffle=_UpperCAmelCase,    batch_size=_UpperCAmelCase,    num_workers=4						)
       _lowerCamelCase						:    int         =			DataLoader(_UpperCAmelCase,    shuffle=_UpperCAmelCase,    batch_size=_UpperCAmelCase,    num_workers=4						)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase						:    List[Any]         =			create_model('''resnet50d''',    pretrained=_UpperCAmelCase,    num_classes=len(_UpperCAmelCase						)						)

       # 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						:    str         =			model.to(accelerator.device						)

       # Freezing the base model
       for param in model.parameters():
              _lowerCamelCase						:    Union[str, Any]         =			False
       for param in model.get_classifier().parameters():
              _lowerCamelCase						:    Tuple         =			True

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

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

       # Instantiate learning rate scheduler
       _lowerCamelCase						:    Dict         =			OneCycleLR(optimizer=_UpperCAmelCase,    max_lr=_UpperCAmelCase,    epochs=_UpperCAmelCase,    steps_per_epoch=len(_UpperCAmelCase						)						)

       # 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						:    Any         =			accelerator.prepare(
           _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase						)
       # We need to keep track of how many total steps we have iterated over
       _lowerCamelCase						:    List[str]         =			0
       # We also need to keep track of the starting epoch so files are named properly
       _lowerCamelCase						:    Optional[int]         =			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						:    Optional[Any]         =			os.path.basename(args.resume_from_checkpoint						)
              else:
                     # Get the most recent checkpoint
                     _lowerCamelCase						:    Dict         =			[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
                     dirs.sort(key=os.path.getctime						)
                     _lowerCamelCase						:    Dict         =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              _lowerCamelCase						:    Optional[int]         =			os.path.splitext(_UpperCAmelCase						)[0]

              if "epoch" in training_difference:
                     _lowerCamelCase						:    Optional[int]         =			int(training_difference.replace('''epoch_''',    ''''''						)						) + 1
                     _lowerCamelCase						:    Any         =			None
              else:
                     _lowerCamelCase						:    Optional[int]         =			int(training_difference.replace('''step_''',    ''''''						)						)
                     _lowerCamelCase						:    int         =			resume_step // len(_UpperCAmelCase						)
                     resume_step -= starting_epoch * len(_UpperCAmelCase						)

    # Now we train the model
       for epoch in range(_UpperCAmelCase,    _UpperCAmelCase						):
              model.train()
              if args.with_tracking:
                     _lowerCamelCase						:    Optional[int]         =			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						:    Optional[int]         =			accelerator.skip_first_batches(_UpperCAmelCase,    _UpperCAmelCase						)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     _lowerCamelCase						:    str         =			train_dataloader
              for batch in active_dataloader:
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     _lowerCamelCase						:    Optional[int]         =			{k: v.to(accelerator.device						) for k, v in batch.items()}
                     _lowerCamelCase						:    Tuple         =			(batch['image'] - mean) / std
                     _lowerCamelCase						:    Union[str, Any]         =			model(_UpperCAmelCase						)
                     _lowerCamelCase						:    Any         =			torch.nn.functional.cross_entropy(_UpperCAmelCase,    batch['''label''']						)
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(_UpperCAmelCase						)
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()
                     overall_step += 1
                     if isinstance(_UpperCAmelCase,    _UpperCAmelCase						):
                            _lowerCamelCase						:    Dict         =			F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          _lowerCamelCase						:    List[Any]         =			os.path.join(args.output_dir,    _UpperCAmelCase						)
                                   accelerator.save_state(_UpperCAmelCase						)
              model.eval()
              _lowerCamelCase						:    Tuple         =			0
              _lowerCamelCase						:    List[str]         =			0
              for step, batch in enumerate(_UpperCAmelCase						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     _lowerCamelCase						:    List[str]         =			{k: v.to(accelerator.device						) for k, v in batch.items()}
                     _lowerCamelCase						:    List[str]         =			(batch['image'] - mean) / std
                     with torch.no_grad():
                            _lowerCamelCase						:    Any         =			model(_UpperCAmelCase						)
                     _lowerCamelCase						:    List[Any]         =			outputs.argmax(dim=-1						)
                     _lowerCamelCase						:    int         =			accelerator.gather_for_metrics((predictions, batch['''label'''])						)
                     _lowerCamelCase						:    Optional[int]         =			predictions == references
                     num_elems += accurate_preds.shape[0]
                     accurate += accurate_preds.long().sum()

              _lowerCamelCase						:    Any         =			accurate.item() / num_elems
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''						)
              if args.with_tracking:
                     accelerator.log(
                         {
                             '''accuracy''': 1_00 * eval_metric,
                             '''train_loss''': total_loss.item() / len(_UpperCAmelCase						),
                             '''epoch''': epoch,
                         },    step=_UpperCAmelCase,    )
              if checkpointing_steps == "epoch":
                     _lowerCamelCase						:    Tuple         =			F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            _lowerCamelCase						:    Optional[Any]         =			os.path.join(args.output_dir,    _UpperCAmelCase						)
                     accelerator.save_state(_UpperCAmelCase						)

       if args.with_tracking:
              accelerator.end_training()






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			argparse.ArgumentParser(description='''Simple example of training script.'''						)
       parser.add_argument('''--data_dir''',    required=_UpperCAmelCase,    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=_UpperCAmelCase,    default=_UpperCAmelCase,    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=_UpperCAmelCase,    default=_UpperCAmelCase,    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=_UpperCAmelCase,    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=_UpperCAmelCase,    default=_UpperCAmelCase,    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=_UpperCAmelCase,    default='''logs''',    help='''Location on where to store experiment tracking logs` and relevent project information''',    )
       _lowerCamelCase						:    Any         =			parser.parse_args()
       _lowerCamelCase						:    Any         =			{'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
       training_function(_UpperCAmelCase,    _UpperCAmelCase						)


if __name__ == "__main__":
   main()







"""simple docstring"""






import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowerCAmelCase__      =    logging.getLogger(__name__)






class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						Optional[int]						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						int=None		):






         """simple docstring"""






         super().__init__(
             __lowerCAmelCase						, question_encoder_tokenizer=__lowerCAmelCase						, generator_tokenizer=__lowerCAmelCase						, index=__lowerCAmelCase						, init_retrieval=__lowerCAmelCase						, )
         _lowerCamelCase						:    Dict         =			None




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         logger.info('''initializing retrieval'''		)

         # initializing a separate process group for retrieval as the default
         # nccl backend doesn't support gather/scatter operations while gloo
         # is too slow to replace nccl for the core gpu communication
         if dist.is_initialized():
                logger.info('''dist initialized'''		)
                # needs to be set manually
                _lowerCamelCase						:    List[str]         =			self._infer_socket_ifname()
                # avoid clash with the NCCL port
                _lowerCamelCase						:    Dict         =			str(distributed_port + 1		)
                _lowerCamelCase						:    str         =			dist.new_group(ranks=__lowerCAmelCase						, backend='''gloo'''		)

         # initialize retriever only on the main worker
         if not dist.is_initialized() or self._is_main():
                logger.info('''dist not initialized / main'''		)
                self.index.init_index()

         # all processes wait untill the retriever is initialized by the main process
         if dist.is_initialized():
                torch.distributed.barrier(group=self.process_group		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         return dist.get_rank(group=self.process_group		) == 0




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Tuple=torch.floataa		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			torch.empty(__lowerCAmelCase						, dtype=__lowerCAmelCase		)
         dist.scatter(__lowerCAmelCase						, src=0						, scatter_list=__lowerCAmelCase						, group=self.process_group		)
         return target_tensor




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			psutil.net_if_addrs()
         # a hacky way to deal with varying network interface names
         _lowerCamelCase						:    str         =			next((addr for addr in addrs if addr.startswith('''e'''		))						, __lowerCAmelCase		)
         return ifname




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						np.ndarray						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         if not dist.is_initialized():
                _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			self._main_retrieve(__lowerCAmelCase						, __lowerCAmelCase		)
                return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase		)

         # distributed training
         _lowerCamelCase						:    Dict         =			dist.get_world_size(group=self.process_group		)

         # gather logic
         _lowerCamelCase						:    str         =			None
         if self._is_main():
                _lowerCamelCase						:    List[Any]         =			[torch.empty(question_hidden_states.shape						, dtype=torch.floataa		) for _ in range(__lowerCAmelCase		)]
         dist.gather(torch.tensor(__lowerCAmelCase		)						, dst=0						, gather_list=__lowerCAmelCase						, group=self.process_group		)

         # scatter logic
         _lowerCamelCase						:    int         =			question_hidden_states.shape[0]
         _lowerCamelCase						:    str         =			[]
         _lowerCamelCase						:    Optional[int]         =			[]
         if self._is_main():
                assert len(__lowerCAmelCase		) == world_size
                _lowerCamelCase      ,   _lowerCamelCase						:    Tuple         =			self._main_retrieve(torch.cat(__lowerCAmelCase		).numpy()						, __lowerCAmelCase		)
                _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			torch.tensor(__lowerCAmelCase		), torch.tensor(__lowerCAmelCase		)
                _lowerCamelCase						:    List[Any]         =			self._chunk_tensor(__lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    Optional[Any]         =			self._chunk_tensor(__lowerCAmelCase						, __lowerCAmelCase		)
         _lowerCamelCase						:    int         =			self._scattered(__lowerCAmelCase						, [n_queries, n_docs]						, target_type=torch.intaa		)
         _lowerCamelCase						:    str         =			self._scattered(__lowerCAmelCase						, [n_queries, n_docs, question_hidden_states.shape[1]]		)

         return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCAmelCase		)


