




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







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]						,				A :       List[str]						,				A :       Optional[int]=None						,				A :       Any=None    )			->			List[str]:






         """simple docstring"""
         a__      =       file_names
         a__      =       image_transform
         a__      =       label_to_id

     def __len__(      self :       List[str]    )			->			Optional[Any]:






         """simple docstring"""
         return len(self.file_names    )






     def __getitem__(      self :       Any						,				A :       Optional[int]    )			->			Union[str, Any]:






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






def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    # Initialize accelerator
    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(UpperCAmelCase_   ,    (list, tuple)		):
        a__      =       (image_size, image_size)

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

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

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

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

    # Instantiate dataloaders.
    a__      =       DataLoader(UpperCAmelCase_   ,    shuffle=UpperCAmelCase_   ,    batch_size=UpperCAmelCase_   ,    num_workers=4		)
    a__      =       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)
    a__      =       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).
    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=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.
    a__ ,		a__ ,		a__ ,		a__ ,		a__      =       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`.
            a__      =       {k: v.to(accelerator.device		) for k, v in batch.items()}
            a__      =       (batch["""image"""] - mean) / std
            a__      =       model(UpperCAmelCase_		)
            a__      =       torch.nn.functional.cross_entropy(UpperCAmelCase_   ,    batch["""label"""]		)
            accelerator.backward(UpperCAmelCase_		)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

        model.eval()
        a__      =       0
        a__      =       0
        for _, batch in enumerate(UpperCAmelCase_		):
            # 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(UpperCAmelCase_		)
            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}: {100 * eval_metric:.2f}"""		)
def        __lowerCAmelCase ():
    a__      =       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."""		)
    a__      =       parser.parse_args()
    a__      =       {"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
    training_function(UpperCAmelCase_   ,    UpperCAmelCase_		)


if __name__ == "__main__":
    main()












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


SCREAMING_SNAKE_CASE_						:					Tuple			   =       {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       F"""pretrained_models/{model_name}"""
    if not os.path.isfile(UpperCAmelCase_		):
        os.makedirs("""pretrained_models"""   ,    exist_ok=UpperCAmelCase_		)
        a__      =       F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(UpperCAmelCase_   ,    """pretrained_models"""		)
    a__      =       torch.load(UpperCAmelCase_   ,    map_location=lambda UpperCAmelCase_   ,    UpperCAmelCase_		: storage		)
    return model
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       download_model(pretrained_models[args.image_size]		)

    a__      =       state_dict["""x_embedder.proj.weight"""]
    a__      =       state_dict["""x_embedder.proj.bias"""]
    state_dict.pop("""x_embedder.proj.weight"""		)
    state_dict.pop("""x_embedder.proj.bias"""		)

    for depth in range(28		):
        a__      =       state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        a__      =       state_dict[
            """y_embedder.embedding_table.weight"""
        ]

        a__      =       state_dict[
            F"""blocks.{depth}.adaLN_modulation.1.weight"""
        ]
        a__      =       state_dict[
            F"""blocks.{depth}.adaLN_modulation.1.bias"""
        ]

        a__ ,		a__ ,		a__      =       torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.weight"""]   ,    3   ,    dim=0		)
        a__ ,		a__ ,		a__      =       torch.chunk(state_dict[F"""blocks.{depth}.attn.qkv.bias"""]   ,    3   ,    dim=0		)

        a__      =       q
        a__      =       q_bias
        a__      =       k
        a__      =       k_bias
        a__      =       v
        a__      =       v_bias

        a__      =       state_dict[
            F"""blocks.{depth}.attn.proj.weight"""
        ]
        a__      =       state_dict[F"""blocks.{depth}.attn.proj.bias"""]

        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc2.bias"""]

        state_dict.pop(F"""blocks.{depth}.attn.qkv.weight"""		)
        state_dict.pop(F"""blocks.{depth}.attn.qkv.bias"""		)
        state_dict.pop(F"""blocks.{depth}.attn.proj.weight"""		)
        state_dict.pop(F"""blocks.{depth}.attn.proj.bias"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc1.weight"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc1.bias"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc2.weight"""		)
        state_dict.pop(F"""blocks.{depth}.mlp.fc2.bias"""		)
        state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.weight"""		)
        state_dict.pop(F"""blocks.{depth}.adaLN_modulation.1.bias"""		)

    state_dict.pop("""t_embedder.mlp.0.weight"""		)
    state_dict.pop("""t_embedder.mlp.0.bias"""		)
    state_dict.pop("""t_embedder.mlp.2.weight"""		)
    state_dict.pop("""t_embedder.mlp.2.bias"""		)
    state_dict.pop("""y_embedder.embedding_table.weight"""		)

    a__      =       state_dict["""final_layer.adaLN_modulation.1.weight"""]
    a__      =       state_dict["""final_layer.adaLN_modulation.1.bias"""]
    a__      =       state_dict["""final_layer.linear.weight"""]
    a__      =       state_dict["""final_layer.linear.bias"""]

    state_dict.pop("""final_layer.linear.weight"""		)
    state_dict.pop("""final_layer.linear.bias"""		)
    state_dict.pop("""final_layer.adaLN_modulation.1.weight"""		)
    state_dict.pop("""final_layer.adaLN_modulation.1.bias"""		)

    # DiT XL/2
    a__      =       TransformeraDModel(
        sample_size=args.image_size // 8   ,    num_layers=28   ,    attention_head_dim=72   ,    in_channels=4   ,    out_channels=8   ,    patch_size=2   ,    attention_bias=UpperCAmelCase_   ,    num_attention_heads=16   ,    activation_fn="""gelu-approximate"""   ,    num_embeds_ada_norm=1_000   ,    norm_type="""ada_norm_zero"""   ,    norm_elementwise_affine=UpperCAmelCase_   ,    )
    transformer.load_state_dict(UpperCAmelCase_   ,    strict=UpperCAmelCase_		)

    a__      =       DDIMScheduler(
        num_train_timesteps=1_000   ,    beta_schedule="""linear"""   ,    prediction_type="""epsilon"""   ,    clip_sample=UpperCAmelCase_   ,    )

    a__      =       AutoencoderKL.from_pretrained(args.vae_model		)

    a__      =       DiTPipeline(transformer=UpperCAmelCase_   ,    vae=UpperCAmelCase_   ,    scheduler=UpperCAmelCase_		)

    if args.save:
        pipeline.save_pretrained(args.checkpoint_path		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       argparse.ArgumentParser()

 parser.add_argument(
     '''--image_size''',
     default=2_56,
     type=int,
     required=False,
     help='''Image size of pretrained model, either 256 or 512.''',
 )
 parser.add_argument(
     '''--vae_model''',
     default='''stabilityai/sd-vae-ft-ema''',
     type=str,
     required=False,
     help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
 )
 parser.add_argument(
     '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
 )

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       parser.parse_args()
 main(args)






