


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       (		__A			)						->					str:



       '''simple docstring'''





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


class     lowercase__  (					__lowerCamelCase		):






  def __init__(					self      :					int		,						_lowercase      :					str		,						_lowercase      :					List[Any]=None		,						_lowercase      :					List[Any]=None ):




         """simple docstring"""







         UpperCAmelCase__										=     file_names
         UpperCAmelCase__										=     image_transform
         UpperCAmelCase__										=     label_to_id






  def __len__(					self      :					Tuple ):




         """simple docstring"""







         return len(self.file_names )






  def __getitem__(					self      :					Tuple		,						_lowercase      :					Dict ):




         """simple docstring"""







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





def __UpperCAmelCase       (		__A ,						__A			)						->					Any:



       '''simple docstring'''





       if args.with_tracking:
              UpperCAmelCase__										=     Accelerator(
                  cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with="all" ,						project_dir=args.project_dir			)
       else:
              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(A__ ,						(list, tuple)			):
              UpperCAmelCase__										=     (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":
                     UpperCAmelCase__										=     args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCAmelCase__										=     int(args.checkpointing_steps			)
              else:
                     raise ValueError(
                         F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
       else:
              UpperCAmelCase__										=     None

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

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

       # Build the label correspondences
       UpperCAmelCase__										=     [extract_label(A__			) for fname in file_names]
       UpperCAmelCase__										=     list(set(A__			)			)
       id_to_label.sort()
       UpperCAmelCase__										=     {lbl: i for i, lbl in enumerate(A__			)}

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

       # Split our filenames between train and validation
       UpperCAmelCase__										=     np.random.permutation(len(A__			)			)
       UpperCAmelCase__										=     int(0.8 * len(A__			)			)
       UpperCAmelCase__										=     random_perm[:cut]
       UpperCAmelCase__										=     random_perm[cut:]

       # For training we use a simple RandomResizedCrop
       UpperCAmelCase__										=     Compose([RandomResizedCrop(A__ ,						scale=(0.5, 1.0)			), ToTensor()]			)
       UpperCAmelCase__										=     PetsDataset(
           [file_names[i] for i in train_split] ,						image_transform=A__ ,						label_to_id=A__			)

       # For evaluation, we use a deterministic Resize
       UpperCAmelCase__										=     Compose([Resize(A__			), ToTensor()]			)
       UpperCAmelCase__										=     PetsDataset([file_names[i] for i in eval_split] ,						image_transform=A__ ,						label_to_id=A__			)

       # Instantiate dataloaders.
       UpperCAmelCase__										=     DataLoader(A__ ,						shuffle=A__ ,						batch_size=A__ ,						num_workers=4			)
       UpperCAmelCase__										=     DataLoader(A__ ,						shuffle=A__ ,						batch_size=A__ ,						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=A__ ,						num_classes=len(A__			)			)

       # 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 / 2_5			)

       # Instantiate learning rate scheduler
       UpperCAmelCase__										=     OneCycleLR(optimizer=A__ ,						max_lr=A__ ,						epochs=A__ ,						steps_per_epoch=len(A__			)			)

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

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

    # Now we train the model
       for epoch in range(A__ ,						A__			):
              model.train()
              if args.with_tracking:
                     UpperCAmelCase__										=     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
                     UpperCAmelCase__										=     accelerator.skip_first_batches(A__ ,						A__			)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCAmelCase__										=     train_dataloader
              for batch in active_dataloader:
                     # 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(A__			)
                     UpperCAmelCase__										=     torch.nn.functional.cross_entropy(A__ ,						batch["label"]			)
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(A__			)
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()
                     overall_step += 1
                     if isinstance(A__ ,						A__			):
                            UpperCAmelCase__										=     F"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCAmelCase__										=     os.path.join(args.output_dir ,						A__			)
                                   accelerator.save_state(A__			)
              model.eval()
              UpperCAmelCase__										=     0
              UpperCAmelCase__										=     0
              for step, batch in enumerate(A__			):
                     # 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(A__			)
                     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}: {1_0_0 * eval_metric:.2f}"""			)
              if args.with_tracking:
                     accelerator.log(
                         {
                             "accuracy": 1_0_0 * eval_metric,
                             "train_loss": total_loss.item() / len(A__			),
                             "epoch": epoch,
                         } ,						step=A__ ,						)
              if checkpointing_steps == "epoch":
                     UpperCAmelCase__										=     F"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            UpperCAmelCase__										=     os.path.join(args.output_dir ,						A__			)
                     accelerator.save_state(A__			)

       if args.with_tracking:
              accelerator.end_training()





def __UpperCAmelCase       (		)						->					Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument("--data_dir" ,						required=A__ ,						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=A__ ,						default=A__ ,						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=A__ ,						default=A__ ,						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=A__ ,						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=A__ ,						default=A__ ,						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=A__ ,						default="logs" ,						help="Location on where to store experiment tracking logs` and relevent project information" ,						)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
       training_function(A__ ,						A__			)


if __name__ == "__main__":
    main()









import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A      = 16
A      = 32





def __UpperCAmelCase       (		__A ,						__A = 1_6			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     AutoTokenizer.from_pretrained("bert-base-cased"			)
       UpperCAmelCase__										=     load_dataset("glue" ,						"mrpc"			)

       def tokenize_function(__A			):
              # max_length=None => use the model max length (it's actually the default)
              UpperCAmelCase__										=     tokenizer(examples["sentence1"] ,						examples["sentence2"] ,						truncation=__A ,						max_length=__A			)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCAmelCase__										=     datasets.map(
                  __A ,						batched=__A ,						remove_columns=["idx", "sentence1", "sentence2"] ,						)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCAmelCase__										=     tokenized_datasets.rename_column("label" ,						"labels"			)

       def collate_fn(__A			):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCAmelCase__										=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCAmelCase__										=     1_6
              elif accelerator.mixed_precision != "no":
                     UpperCAmelCase__										=     8
              else:
                     UpperCAmelCase__										=     None

              return tokenizer.pad(
                  __A ,						padding="longest" ,						max_length=__A ,						pad_to_multiple_of=__A ,						return_tensors="pt" ,						)

       # Instantiate dataloaders.
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["train"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)
       UpperCAmelCase__										=     DataLoader(
           tokenized_datasets["validation"] ,						shuffle=__A ,						collate_fn=__A ,						batch_size=__A			)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    A      = mocked_dataloaders  # noqa: F811





def __UpperCAmelCase       (		__A ,						__A			)						->					Dict:



       '''simple docstring'''





       if os.environ.get("TESTING_MOCKED_DATALOADERS" ,						__A			) == "1":
              UpperCAmelCase__										=     2
       # New Code #
       UpperCAmelCase__										=     int(args.gradient_accumulation_steps			)
       # Initialize accelerator
       UpperCAmelCase__										=     Accelerator(
           cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						gradient_accumulation_steps=__A			)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"			)
       # 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__										=     evaluate.load("glue" ,						"mrpc"			)

       set_seed(__A			)
       UpperCAmelCase__						,			UpperCAmelCase__										=     get_dataloaders(__A ,						__A			)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCAmelCase__										=     AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,						return_dict=__A			)

       # 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			)

       # Instantiate optimizer
       UpperCAmelCase__										=     AdamW(params=model.parameters() ,						lr=__A			)

       # Instantiate scheduler
       UpperCAmelCase__										=     get_linear_schedule_with_warmup(
           optimizer=__A ,						num_warmup_steps=1_0_0 ,						num_training_steps=(len(__A			) * num_epochs) ,						)

       # 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(
           __A ,						__A ,						__A ,						__A ,						__A			)

       # Now we train the model
       for epoch in range(__A			):
              model.train()
              for step, batch in enumerate(__A			):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device			)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(__A			):
                            UpperCAmelCase__										=     model(**__A			)
                            UpperCAmelCase__										=     output.loss
                            accelerator.backward(__A			)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(__A			):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device			)
                     with torch.no_grad():
                            UpperCAmelCase__										=     model(**__A			)
                     UpperCAmelCase__										=     outputs.logits.argmax(dim=-1			)
                     UpperCAmelCase__						,			UpperCAmelCase__										=     accelerator.gather_for_metrics((predictions, batch["labels"])			)
                     metric.add_batch(
                         predictions=__A ,						references=__A ,						)

              UpperCAmelCase__										=     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,						__A			)





def __UpperCAmelCase       (		)						->					Dict:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument(
           "--mixed_precision" ,						type=__A ,						default=__A ,						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." ,						)
       # New Code #
       parser.add_argument(
           "--gradient_accumulation_steps" ,						type=__A ,						default=1 ,						help="The number of minibatches to be ran before gradients are accumulated." ,						)
       parser.add_argument("--cpu" ,						action="store_true" ,						help="If passed, will train on the CPU."			)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()





