



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

class      lowerCamelCase__     (     _a				):






	'''simple docstring'''

	def __init__(  self		:  Any   ,		__A		:  Optional[Any]   ,		__A		:  Tuple=None   ,		__A		:  Union[str, Any]=None					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   file_names
								lowerCAmelCase__  =   image_transform
								lowerCAmelCase__  =   label_to_id




	def __len__(  self		:  Union[str, Any]					)     ->			Tuple:




								'''simple docstring'''





								return len(self.file_names					)




	def __getitem__(  self		:  List[str]   ,		__A		:  int					)     ->			str:




								'''simple docstring'''





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





def 			_lowerCAmelCase(      UpperCAmelCase_      :	Optional[int]					, UpperCAmelCase_      :	str						) ->							Dict:
							lowerCAmelCase__  =   Accelerator(cpu=args.cpu					, mixed_precision=args.mixed_precision						)

							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							lowerCAmelCase__  =   config["""lr"""]
							lowerCAmelCase__  =   int(config["""num_epochs"""]						)
							lowerCAmelCase__  =   int(config["""seed"""]						)
							lowerCAmelCase__  =   int(config["""batch_size"""]						)
							lowerCAmelCase__  =   config["""image_size"""]
							if not isinstance(__lowercase					, (list, tuple)						):
														lowerCAmelCase__  =   (image_size, image_size)

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

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

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

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

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

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

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

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

							# Instantiate learning rate scheduler
							lowerCAmelCase__  =   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.
							lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  =   accelerator.prepare(
							    __lowercase					, __lowercase					, __lowercase					, __lowercase					, __lowercase						)

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

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

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


if __name__ == "__main__":
		main()












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					F'''pretrained_models/{model_name}'''
  if not os.path.isfile(__lowercase):
    os.makedirs('''pretrained_models'''  ,				exist_ok=__lowercase)
    __lowerCAmelCase								=					F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
    download_url(__lowercase  ,				'''pretrained_models''')
  __lowerCAmelCase								=					torch.load(__lowercase  ,				map_location=lambda __lowercase  ,				__lowercase: storage)
  return model


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

  __lowerCAmelCase								=					state_dict['''x_embedder.proj.weight''']
  __lowerCAmelCase								=					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):
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

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

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					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''')

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					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
  __lowerCAmelCase								=					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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


if __name__ == "__main__":
      __lowercase				    =      argparse.ArgumentParser()

      parser.add_argument(
          """--image_size""",
          default=256,
          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."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
