'''simple docstring'''
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 (					__UpperCAmelCase  :					List[Any]			)							->	Union[str, Any]:
  lowercase				:							Union[str, Any]									=   fname.split(os.path.sep			)[-1]
  return re.search(R'''^(.*)_\d+\.jpg$''' ,   __UpperCAmelCase			).groups()[0]




class _lowerCAmelCase			(	snake_case					):







      def __init__(  self  ,    lowercase  ,    lowercase=None  ,    lowercase=None     ):
        lowercase				:							List[Any]									=   file_names
        lowercase				:							Optional[int]									=   image_transform
        lowercase				:							List[Any]									=   label_to_id







      def __len__(  self     ):
        return len(self.file_names     )







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







def 				__UpperCamelCase (					__UpperCAmelCase  :					Any ,   __UpperCAmelCase  :					Tuple			)							->	List[str]:
  # Initialize accelerator
  lowercase				:							Tuple									=   Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision			)

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase				:							List[Any]									=   config['''lr''']
  lowercase				:							str									=   int(config['''num_epochs''']			)
  lowercase				:							Optional[int]									=   int(config['''seed''']			)
  lowercase				:							int									=   int(config['''batch_size''']			)
  lowercase				:							Tuple									=   config['''image_size''']
  if not isinstance(__UpperCAmelCase ,   (list, tuple)			):
    lowercase				:							Any									=   (image_size, image_size)

  # Grab all the image filenames
  lowercase				:							List[str]									=   [os.path.join(args.data_dir ,   __UpperCAmelCase			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

  # Build the label correspondences
  lowercase				:							Dict									=   [extract_label(__UpperCAmelCase			) for fname in file_names]
  lowercase				:							Tuple									=   list(set(__UpperCAmelCase			)			)
  id_to_label.sort()
  lowercase				:							int									=   {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
  lowercase				:							List[Any]									=   np.random.permutation(len(__UpperCAmelCase			)			)
  lowercase				:							List[str]									=   int(0.8 * len(__UpperCAmelCase			)			)
  lowercase				:							Optional[int]									=   random_perm[:cut]
  lowercase				:							Optional[Any]									=   random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  lowercase				:							Union[str, Any]									=   Compose([RandomResizedCrop(__UpperCAmelCase ,   scale=(0.5, 1.0)			), ToTensor()]			)
  lowercase				:							Optional[int]									=   PetsDataset(
      [file_names[i] for i in train_split] ,   image_transform=__UpperCAmelCase ,   label_to_id=__UpperCAmelCase			)

  # For evaluation, we use a deterministic Resize
  lowercase				:							str									=   Compose([Resize(__UpperCAmelCase			), ToTensor()]			)
  lowercase				:							int									=   PetsDataset([file_names[i] for i in eval_split] ,   image_transform=__UpperCAmelCase ,   label_to_id=__UpperCAmelCase			)

  # Instantiate dataloaders.
  lowercase				:							int									=   DataLoader(__UpperCAmelCase ,   shuffle=__UpperCAmelCase ,   batch_size=__UpperCAmelCase ,   num_workers=4			)
  lowercase				:							Optional[Any]									=   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)
  lowercase				:							Optional[int]									=   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).
  lowercase				:							Union[str, Any]									=   model.to(accelerator.device			)

  # Freezing the base model
  for param in model.parameters():
    lowercase				:							Optional[int]									=   False
  for param in model.get_classifier().parameters():
    lowercase				:							Tuple									=   True

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

  # Instantiate optimizer
  lowercase				:							Optional[Any]									=   torch.optim.Adam(params=model.parameters() ,   lr=lr / 25			)

  # Instantiate learning rate scheduler
  lowercase				:							List[str]									=   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.
  lowercase				,      lowercase				,      lowercase				,      lowercase				,      lowercase				:							str									=   accelerator.prepare(
      __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Now we train the model
  for epoch in range(__UpperCAmelCase			):
    model.train()
    for step, batch in enumerate(__UpperCAmelCase			):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      lowercase				:							str									=   {k: v.to(accelerator.device			) for k, v in batch.items()}
      lowercase				:							Tuple									=   (batch['''image'''] - mean) / std
      lowercase				:							List[str]									=   model(__UpperCAmelCase			)
      lowercase				:							Any									=   torch.nn.functional.cross_entropy(__UpperCAmelCase ,   batch['''label''']			)
      accelerator.backward(__UpperCAmelCase			)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()

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

    lowercase				:							Tuple									=   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 (					)							->	Optional[int]:
  lowercase				:							int									=   argparse.ArgumentParser(description='''Simple example of training script.'''			)
  parser.add_argument('''--data_dir''' ,   required=__UpperCAmelCase ,   help='''The data folder on disk.'''			)
  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(
      '''--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('''--cpu''' ,   action='''store_true''' ,   help='''If passed, will train on the CPU.'''			)
  lowercase				:							str									=   parser.parse_args()
  lowercase				:							Tuple									=   {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
  training_function(__UpperCAmelCase ,   __UpperCAmelCase			)


if __name__ == "__main__":
 main()
'''simple docstring'''
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()






@require_cpu
class _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )