"""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     a_					(				_lowerCAmelCase  :			Union[str, Any]					):





       '''simple docstring'''

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


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a=None		,      a=None							)	-> Dict:
             lowercase__      :		List[str]          =     file_names
             lowercase__      :		List[Any]          =     image_transform
             lowercase__      :		Optional[Any]          =     label_to_id


      def __len__(       self							)	-> int:
             return len(self.file_names							)


      def __getitem__(       self		,      a							)	-> int:
             lowercase__      :		str          =     self.file_names[idx]
             lowercase__      :		Tuple          =     PIL.Image.open(a							)
             lowercase__      :		Union[str, Any]          =     raw_image.convert('RGB'							)
             if self.image_transform is not None:
                    lowercase__      :		int          =     self.image_transform(a							)
             lowercase__      :		Union[str, Any]          =     extract_label(a							)
             if self.label_to_id is not None:
                    lowercase__      :		Tuple          =     self.label_to_id[label]
             return {"image": image, "label": label}





def     a_					(				_lowerCAmelCase  :			Dict       ,			_lowerCAmelCase  :			Optional[Any]					):





       '''simple docstring'''

       lowercase__      :		str          =     Accelerator(cpu=args.cpu       ,			mixed_precision=args.mixed_precision					)

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

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

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

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

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

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

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

       # Instantiate dataloaders.
       lowercase__      :		Optional[Any]          =     DataLoader(_lowerCAmelCase       ,			shuffle=_lowerCAmelCase       ,			batch_size=_lowerCAmelCase       ,			num_workers=4					)
       lowercase__      :		str          =     DataLoader(_lowerCAmelCase       ,			shuffle=_lowerCAmelCase       ,			batch_size=_lowerCAmelCase       ,			num_workers=4					)

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

       # 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__      :		Tuple          =     model.to(accelerator.device					)

       # Freezing the base model
       for param in model.parameters():
              lowercase__      :		Any          =     False
       for param in model.get_classifier().parameters():
              lowercase__      :		List[Any]          =     True

       # We normalize the batches of images to be a bit faster.
       lowercase__      :		Dict          =     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__      :		Any          =     OneCycleLR(optimizer=_lowerCAmelCase       ,			max_lr=_lowerCAmelCase       ,			epochs=_lowerCAmelCase       ,			steps_per_epoch=len(_lowerCAmelCase					)					)

       # 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__      :		Union[str, Any]          =     accelerator.prepare(
           _lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)

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

              model.eval()
              lowercase__      :		List[str]          =     0
              lowercase__      :		Tuple          =     0
              for _, batch in enumerate(_lowerCAmelCase					):
                     # 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__      :		Optional[Any]          =     (batch['image'] - mean) / std
                     with torch.no_grad():
                            lowercase__      :		Dict          =     model(_lowerCAmelCase					)
                     lowercase__      :		int          =     outputs.argmax(dim=-1					)
                     lowercase__   ,				lowercase__      :		List[Any]          =     accelerator.gather_for_metrics((predictions, batch['label'])					)
                     lowercase__      :		List[str]          =     predictions == references
                     num_elems += accurate_preds.shape[0]
                     accurate += accurate_preds.long().sum()

              lowercase__      :		List[str]          =     accurate.item() / num_elems
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""					)





def     a_					(				):





       '''simple docstring'''

       lowercase__      :		Union[str, Any]          =     argparse.ArgumentParser(description='Simple example of training script.'					)
       parser.add_argument('--data_dir'       ,			required=_lowerCAmelCase       ,			help='The data folder on disk.'					)
       parser.add_argument(
           '--mixed_precision'       ,			type=_lowerCAmelCase       ,			default=_lowerCAmelCase       ,			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=_lowerCAmelCase       ,			default=_lowerCAmelCase       ,			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__      :		Optional[Any]          =     parser.parse_args()
       lowercase__      :		Union[str, Any]          =     {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
       training_function(_lowerCAmelCase       ,			_lowerCAmelCase					)


if __name__ == "__main__":
       main()


"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output

