


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_    ):



     """simple docstring"""






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

class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case=None      , __snake_case=None   ):
         snake_case					=    file_names
         snake_case					=    image_transform
         snake_case					=    label_to_id
    def __len__(	self   ):
         return len(self.file_names   )




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






def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






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

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

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

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

     # Build the label correspondences
     snake_case					=    [extract_label(UpperCamelCase_    ) for fname in file_names]
     snake_case					=    list(set(UpperCamelCase_    )    )
     id_to_label.sort()
     snake_case					=    {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
     snake_case					=    np.random.permutation(len(UpperCamelCase_    )    )
     snake_case					=    int(0.8 * len(UpperCamelCase_    )    )
     snake_case					=    random_perm[:cut]
     snake_case					=    random_perm[cut:]

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

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

     # Instantiate dataloaders.
     snake_case					=    DataLoader(UpperCamelCase_							,shuffle=UpperCamelCase_							,batch_size=UpperCamelCase_							,num_workers=4    )
     snake_case					=    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)
     snake_case					=    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).
     snake_case					=    model.to(accelerator.device    )

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

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

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

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

          if "epoch" in training_difference:
               snake_case					=    int(training_difference.replace('''epoch_'''							,''''''    )    ) + 1
               snake_case					=    None
          else:
               snake_case					=    int(training_difference.replace('''step_'''							,''''''    )    )
               snake_case					=    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:
               snake_case					=    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
               snake_case					=    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
               snake_case					=    train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               snake_case					=    {k: v.to(accelerator.device    ) for k, v in batch.items()}
               snake_case					=    (batch['''image'''] - mean) / std
               snake_case					=    model(UpperCamelCase_    )
               snake_case					=    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_    ):
                    snake_case					=    F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              snake_case					=    os.path.join(args.output_dir							,UpperCamelCase_    )
                         accelerator.save_state(UpperCamelCase_    )
          model.eval()
          snake_case					=    0
          snake_case					=    0
          for step, batch in enumerate(UpperCamelCase_    ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               snake_case					=    {k: v.to(accelerator.device    ) for k, v in batch.items()}
               snake_case					=    (batch['''image'''] - mean) / std
               with torch.no_grad():
                    snake_case					=    model(UpperCamelCase_    )
               snake_case					=    outputs.argmax(dim=-1    )
               snake_case			,  snake_case					=    accelerator.gather_for_metrics((predictions, batch['''label'''])    )
               snake_case					=    predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

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

     if args.with_tracking:
          accelerator.end_training()
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    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'''							,)
     snake_case					=    parser.parse_args()
     snake_case					=    {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
     training_function(UpperCamelCase_							,UpperCamelCase_    )


if __name__ == "__main__":
 main()








from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




    @property
    def 		a_      (	self   ):
         return 1E-5




