code: 
  
  
  
  
  
  """simple docstring"""
  
  import argparse
  import os
  
  # New Code #
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator
  from accelerate.utils import find_executable_batch_size
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate,
  # specifically showcasing how to combine both the gradient accumulation
  # and automatic batch size finder utilities of Accelerate to perfrom
  # automatic gradient accumulation
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # New additions from the base script can be found quickly by
  # looking for the # New Code # tags
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  lowercase__      =				32
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase = 16 )		->					Optional[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							int             =    AutoTokenizer.from_pretrained("bert-base-cased" )
  		lowerCAmelCase_     :							Optional[Any]             =    load_dataset("glue"   , "mrpc" )
  
  		def tokenize_function(__UpperCamelCase ):
  				# max_length=None => use the model max length (it's actually the default)
  				lowerCAmelCase_     :							List[Any]             =    tokenizer(examples["sentence1"]   , examples["sentence2"]   , truncation=__UpperCamelCase   , max_length=__UpperCamelCase )
  				return outputs
  
  		# Apply the method we just defined to all the examples in all the splits of the dataset
  		# starting with the main process first:
  		with accelerator.main_process_first():
  				lowerCAmelCase_     :							Optional[int]             =    datasets.map(
  				    __UpperCamelCase   , batched=__UpperCamelCase   , remove_columns=["idx", "sentence1", "sentence2"]   , )
  
  		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  		# transformers library
  		lowerCAmelCase_     :							List[Any]             =    tokenized_datasets.rename_column("label"   , "labels" )
  
  		def collate_fn(__UpperCamelCase ):
  				# When using mixed precision we want round multiples of 8/16
  				if accelerator.mixed_precision == "fp8":
  						lowerCAmelCase_     :							Dict             =    16
  				elif accelerator.mixed_precision != "no":
  						lowerCAmelCase_     :							str             =    8
  				else:
  						lowerCAmelCase_     :							int             =    None
  
  				return tokenizer.pad(
  				    __UpperCamelCase   , padding="longest"   , pad_to_multiple_of=__UpperCamelCase   , return_tensors="pt"   , )
  
  		# Instantiate dataloaders.
  		lowerCAmelCase_     :							Any             =    DataLoader(
  		    tokenized_datasets["train"]   , shuffle=__UpperCamelCase   , collate_fn=__UpperCamelCase   , batch_size=__UpperCamelCase )
  		lowerCAmelCase_     :							Tuple             =    DataLoader(
  		    tokenized_datasets["validation"]   , shuffle=__UpperCamelCase   , collate_fn=__UpperCamelCase   , batch_size=__UpperCamelCase )
  
  		return train_dataloader, eval_dataloader
  
  
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  					from accelerate.test_utils.training import mocked_dataloaders
  
  					lowercase__      =				mocked_dataloaders  # noqa: F811
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					List[Any]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if os.environ.get("TESTING_MOCKED_DATALOADERS"   , __UpperCamelCase ) == "1":
  				lowerCAmelCase_     :							List[Any]             =    2
  		# Initialize accelerator
  		lowerCAmelCase_     :							Tuple             =    Accelerator(cpu=args.cpu   , mixed_precision=args.mixed_precision )
  		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  		lowerCAmelCase_     :							Dict             =    config["lr"]
  		lowerCAmelCase_     :							List[str]             =    int(config["num_epochs"] )
  		lowerCAmelCase_     :							str             =    int(config["seed"] )
  		lowerCAmelCase_     :							Dict             =    int(config["batch_size"] )
  
  		lowerCAmelCase_     :							Optional[int]             =    evaluate.load("glue"   , "mrpc" )
  
  		# New Code #
  		# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  		# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  		# half each time. From this, we can calculate the number of gradient accumulation steps needed
  		# and modify the Accelerator object as a result
  		@find_executable_batch_size(starting_batch_size=int(__UpperCamelCase ) )
  		def inner_training_loop(__UpperCamelCase ):
  				# Since we need to modify the outside accelerator object, we need to bring it
  				# to the local scope
  				nonlocal accelerator
  
  				# We can calculate the number of gradient accumulation steps based on the current
  				# batch size vs the starting batch size
  				lowerCAmelCase_     :							Optional[Any]             =    observed_batch_size // batch_size
  
  				# And then set it in the Accelerator directly:
  				lowerCAmelCase_     :							List[str]             =    num_gradient_accumulation_steps
  
  				# Next we need to free all of the stored model references in the Accelerator each time
  				accelerator.free_memory()
  
  				# And set the seed so our results are reproducable each reset
  				set_seed(__UpperCamelCase )
  
  				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  				lowerCAmelCase_     :							str             =    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"   , return_dict=__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).
  				lowerCAmelCase_     :							List[Any]             =    model.to(accelerator.device )
  
  				# Instantiate optimizer
  				lowerCAmelCase_     :							str             =    AdamW(params=model.parameters()   , lr=__UpperCamelCase )
  				lowerCAmelCase_     :							List[str]             =    get_dataloaders(__UpperCamelCase   , __UpperCamelCase )
  
  				# Instantiate scheduler
  				lowerCAmelCase_     :							List[Any]             =    get_linear_schedule_with_warmup(
  				    optimizer=__UpperCamelCase   , num_warmup_steps=100   , num_training_steps=(len(__UpperCamelCase ) * num_epochs)   , )
  
  				# 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_     :							Any             =    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 ):
  								# And perform gradient accumulation
  								with accelerator.accumulate(__UpperCamelCase ):
  										# We could avoid this line since we set the accelerator with `device_placement=True`.
  										batch.to(accelerator.device )
  										lowerCAmelCase_     :							Any             =    model(**__UpperCamelCase )
  										lowerCAmelCase_     :							List[Any]             =    outputs.loss
  										accelerator.backward(__UpperCamelCase )
  										optimizer.step()
  										lr_scheduler.step()
  										optimizer.zero_grad()
  
  						model.eval()
  						for step, batch in enumerate(__UpperCamelCase ):
  								# We could avoid this line since we set the accelerator with `device_placement=True`.
  								batch.to(accelerator.device )
  								with torch.no_grad():
  										lowerCAmelCase_     :							int             =    model(**__UpperCamelCase )
  								lowerCAmelCase_     :							Optional[int]             =    outputs.logits.argmax(dim=-1 )
  								lowerCAmelCase_     :							Optional[int]             =    accelerator.gather_for_metrics((predictions, batch["labels"]) )
  								metric.add_batch(
  								    predictions=__UpperCamelCase   , references=__UpperCamelCase   , )
  
  						lowerCAmelCase_     :							int             =    metric.compute()
  						# Use accelerator.print to print only on the main process.
  						accelerator.print(f'''epoch {epoch}:'''   , __UpperCamelCase )
  
      # New Code #
      # And call it at the end with no arguments
      # Note: You could also refactor this outside of your training loop function
  		inner_training_loop()
  
  def 	__lowerCamelCase			(						)		->					Optional[int]:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		lowerCAmelCase_     :							Tuple             =    argparse.ArgumentParser(description="Simple example of training script." )
  		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." )
  		lowerCAmelCase_     :							int             =    parser.parse_args()
  		# New Code #
  		# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  		lowerCAmelCase_     :							Dict             =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
  		training_function(__UpperCamelCase   , __UpperCamelCase )
  
  
  if __name__ == "__main__":
  					main()
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from .pipeline_kandinskya_a import KandinskyVaaPipeline
  from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
  from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
  from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
  from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
  from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
  from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
  