



'''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 		_lowerCAmelCase				(_lowercase			):

       """simple docstring"""

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



class    lowerCamelCase__				(			__lowerCamelCase						):






      """simple docstring"""




      def __init__(       self							:     Tuple			,a__							:     List[str]			,a__							:     Optional[Any]=None			,a__							:     Optional[Any]=None   ):
             a__								= file_names
             a__								= image_transform
             a__								= label_to_id




      def __len__(       self							:     Optional[Any]   ):
             return len(self.file_names   )




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






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

       if args.with_tracking:
              a__								= Accelerator(
                  cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with="all"   ,					project_dir=args.project_dir			)
       else:
              a__								= Accelerator(cpu=args.cpu   ,					mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

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

       # Instantiate dataloaders.
       a__								= DataLoader(lowercase__   ,					shuffle=lowercase__   ,					batch_size=lowercase__   ,					num_workers=4			)
       a__								= DataLoader(lowercase__   ,					shuffle=lowercase__   ,					batch_size=lowercase__   ,					num_workers=4			)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__								= create_model("resnet50d"   ,					pretrained=lowercase__   ,					num_classes=len(lowercase__			)			)

       # 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).
       a__								= model.to(accelerator.device			)

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

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

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

       # Instantiate learning rate scheduler
       a__								= OneCycleLR(optimizer=lowercase__   ,					max_lr=lowercase__   ,					epochs=lowercase__   ,					steps_per_epoch=len(lowercase__			)			)

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

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

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

              a__								= 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(lowercase__			),
                             "epoch": epoch,
                         }   ,					step=lowercase__   ,					)
              if checkpointing_steps == "epoch":
                     a__								= F'epoch_{epoch}'
                     if args.output_dir is not None:
                            a__								= os.path.join(args.output_dir   ,					lowercase__			)
                     accelerator.save_state(lowercase__			)

       if args.with_tracking:
              accelerator.end_training()






def 		_lowerCAmelCase				():

       """simple docstring"""

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


if __name__ == "__main__":
    main()



'''simple docstring'''

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     return EnvironmentCommand()

def 					__UpperCamelCase				(						lowercase__    :	Any			):






     '''simple docstring'''





     return EnvironmentCommand(args.accelerate_config_file			)







class 	lowerCAmelCase  (   A ):







       @staticmethod
       def       snake_case (				__lowercase      :			ArgumentParser   ):







            """simple docstring"""







            __lowercase      		=parser.add_parser('env'   )
            download_parser.set_defaults(func=__lowercase   )
            download_parser.add_argument(
                '--accelerate-config_file'    ,   default=__lowercase    ,   help='The accelerate config file to use for the default values in the launching script.'    ,   )
            download_parser.set_defaults(func=__lowercase   )







       def __init__(				self      :			int    ,   __lowercase      :			Any    ,   *__lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=accelerate_config_file







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		='not installed'
            if is_safetensors_available():
                 import safetensors

                 __lowercase      		=safetensors.__version__
            elif importlib.util.find_spec('safetensors'   ) is not None:
                 import safetensors

                 __lowercase      		=f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

            __lowercase      		='not installed'
            __lowercase      		=__lowercase      		='not found'
            if is_accelerate_available():
                 import accelerate
                 from accelerate.commands.config import default_config_file, load_config_from_file

                 __lowercase      		=accelerate.__version__
                 # Get the default from the config file.
                 if self._accelerate_config_file is not None or os.path.isfile(__lowercase   ):
                      __lowercase      		=load_config_from_file(self._accelerate_config_file   ).to_dict()

                 __lowercase      		=(
                     '\n'.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]   )
                     if isinstance(__lowercase    ,   __lowercase   )
                     else f'''\t{accelerate_config}'''
                 )

            __lowercase      		='not installed'
            __lowercase      		='NA'
            if is_torch_available():
                 import torch

                 __lowercase      		=torch.__version__
                 __lowercase      		=torch.cuda.is_available()

            __lowercase      		='not installed'
            __lowercase      		='NA'
            if is_tf_available():
                 import tensorflow as tf

                 __lowercase      		=tf.__version__
                 try:
                      # deprecated in v2.1
                      __lowercase      		=tf.test.is_gpu_available()
                 except AttributeError:
                      # returns list of devices, convert to bool
                      __lowercase      		=bool(tf.config.list_physical_devices('GPU'   )   )

            __lowercase      		='not installed'
            __lowercase      		='not installed'
            __lowercase      		='not installed'
            __lowercase      		='NA'
            if is_flax_available():
                 import flax
                 import jax
                 import jaxlib

                 __lowercase      		=flax.__version__
                 __lowercase      		=jax.__version__
                 __lowercase      		=jaxlib.__version__
                 __lowercase      		=jax.lib.xla_bridge.get_backend().platform

            __lowercase      		={
                '`transformers` version': version,
                'Platform': platform.platform(),
                'Python version': platform.python_version(),
                'Huggingface_hub version': huggingface_hub.__version__,
                'Safetensors version': f'''{safetensors_version}''',
                'Accelerate version': f'''{accelerate_version}''',
                'Accelerate config': f'''{accelerate_config_str}''',
                'PyTorch version (GPU?)': f'''{pt_version} ({pt_cuda_available})''',
                'Tensorflow version (GPU?)': f'''{tf_version} ({tf_cuda_available})''',
                'Flax version (CPU?/GPU?/TPU?)': f'''{flax_version} ({jax_backend})''',
                'Jax version': f'''{jax_version}''',
                'JaxLib version': f'''{jaxlib_version}''',
                'Using GPU in script?': '<fill in>',
                'Using distributed or parallel set-up in script?': '<fill in>',
            }

            print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'   )
            print(self.format_dict(__lowercase   )   )

            return info







       @staticmethod
       def       snake_case (				__lowercase      :			Optional[Any]   ):







            """simple docstring"""







            return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]   ) + "\n"


