code: 
  
  
  
  
  
  '''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 (     UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      fname.split(os.path.sep    )[-1]
  	return re.search(R"""^(.*)_\d+\.jpg$"""       ,			UpperCAmelCase    ).groups()[0]
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      List[Any]=None      ,      _lowerCAmelCase						:      Optional[Any]=None      ):
  			A							  =      file_names
  			A							  =      image_transform
  			A							  =      label_to_id
  
  
  
  
  
  
  
  		def __len__(self						:      List[Any]      ):
  			return len(self.file_names      )
  
  
  
  
  
  
  
  		def __getitem__(self						:      List[str]      ,      _lowerCAmelCase						:      int      ):
  			A							  =      self.file_names[idx]
  			A							  =      PIL.Image.open(_lowerCAmelCase      )
  			A							  =      raw_image.convert("""RGB"""      )
  			if self.image_transform is not None:
  				A							  =      self.image_transform(_lowerCAmelCase      )
  			A							  =      extract_label(_lowerCAmelCase      )
  			if self.label_to_id is not None:
  				A							  =      self.label_to_id[label]
  			return {"image": image, "label": label}
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	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    __a (     )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	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()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DiffusionPipeline
  from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      bool = False      ):
  			A							  =      10
  
  			if only_cross_attention:
  				A							  =      12
  			else:
  				# when only cross attention is not set, the cross attention dim must be the same as the query dim
  				A							  =      query_dim
  
  			return {
  			    "query_dim": query_dim,
  			    "cross_attention_dim": cross_attention_dim,
  			    "heads": 2,
  			    "dim_head": 4,
  			    "added_kv_proj_dim": 6,
  			    "norm_num_groups": 1,
  			    "only_cross_attention": only_cross_attention,
  			    "processor": AttnAddedKVProcessor(),
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			A							  =      2
  
  			A							  =      torch.rand(_lowerCAmelCase      ,      _lowerCAmelCase      ,      3      ,      2      )
  			A							  =      torch.rand(_lowerCAmelCase      ,      4      ,      _lowerCAmelCase      )
  			A							  =      None
  
  			return {
  			    "hidden_states": hidden_states,
  			    "encoder_hidden_states": encoder_hidden_states,
  			    "attention_mask": attention_mask,
  			}
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			# self and cross attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is not None      )
  			self.assertTrue(attn.to_v is not None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			# only self attention
  
  			torch.manual_seed(0      )
  
  			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
  			A							  =      Attention(**_lowerCAmelCase      )
  
  			self.assertTrue(attn.to_k is None      )
  			self.assertTrue(attn.to_v is None      )
  
  			A							  =      self.get_forward_arguments(
  			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )
  
  			A							  =      attn(**_lowerCAmelCase      )
  
  			self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       unittest.TestCase						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			A							  =      DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			# the initial conversion succeeds
  			A							  =      DiffusionPipeline.from_pretrained(
  			    """hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			with tempfile.TemporaryDirectory() as tmpdir:
  				# save the converted model
  				pipe.save_pretrained(_lowerCAmelCase      )
  
  				# can also load the converted weights
  				A							  =      DiffusionPipeline.from_pretrained(_lowerCAmelCase      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )
  
  			A							  =      pipe(
  			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images
  
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
  
  