






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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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
#
########################################################################


a						:			int     	=					16
a						:			Dict     	=					32






def 							lowerCamelCase__ (       __lowerCamelCase     :	Accelerator    ,						__lowerCamelCase     :	int = 16						):
  __UpperCAmelCase				:      List[str]	 =    AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  __UpperCAmelCase				:      str	 =    load_dataset("""glue"""    ,						"""mrpc"""						)

  def tokenize_function(__lowerCamelCase     :	List[str]						):
    # max_length=None => use the model max length (it's actually the default)
    __UpperCAmelCase				:      Tuple	 =    tokenizer(examples["""sentence1"""]    ,						examples["""sentence2"""]    ,						truncation=__lowerCamelCase    ,						max_length=__lowerCamelCase						)
    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():
    __UpperCAmelCase				:      str	 =    datasets.map(
        __lowerCamelCase    ,						batched=__lowerCamelCase    ,						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
  __UpperCAmelCase				:      Optional[Any]	 =    tokenized_datasets.rename_column("""label"""    ,						"""labels"""						)

  def collate_fn(__lowerCamelCase     :	str						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __UpperCAmelCase				:      Optional[Any]	 =    128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __UpperCAmelCase				:      Dict	 =    16
    elif accelerator.mixed_precision != "no":
      __UpperCAmelCase				:      str	 =    8
    else:
      __UpperCAmelCase				:      Optional[Any]	 =    None

    return tokenizer.pad(
        __lowerCamelCase    ,						padding="""longest"""    ,						max_length=__lowerCamelCase    ,						pad_to_multiple_of=__lowerCamelCase    ,						return_tensors="""pt"""    ,						)

  # Instantiate dataloaders.
  __UpperCAmelCase				:      List[Any]	 =    DataLoader(
      tokenized_datasets["""train"""]    ,						shuffle=__lowerCamelCase    ,						collate_fn=__lowerCamelCase    ,						batch_size=__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    DataLoader(
      tokenized_datasets["""validation"""]    ,						shuffle=__lowerCamelCase    ,						collate_fn=__lowerCamelCase    ,						batch_size=__lowerCamelCase						)

  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

       a						:			List[Any]     	=					mocked_dataloaders  # noqa: F811






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[str]    ,						__lowerCamelCase     :	List[Any]						):
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,						__lowerCamelCase						) == "1":
    __UpperCAmelCase				:      Union[str, Any]	 =    2
  # Initialize accelerator
  __UpperCAmelCase				:      List[Any]	 =    Accelerator(cpu=args.cpu    ,						mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __UpperCAmelCase				:      Union[str, Any]	 =    config["""lr"""]
  __UpperCAmelCase				:      Dict	 =    int(config["""num_epochs"""]						)
  __UpperCAmelCase				:      Tuple	 =    int(config["""seed"""]						)
  __UpperCAmelCase				:      Any	 =    int(config["""batch_size"""]						)

  __UpperCAmelCase				:      List[Any]	 =    evaluate.load("""glue"""    ,						"""mrpc"""						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=__lowerCamelCase						)
  def inner_training_loop(__lowerCamelCase     :	str						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(__lowerCamelCase						)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCAmelCase				:      Union[str, Any]	 =    AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,						return_dict=__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).
    __UpperCAmelCase				:      Optional[int]	 =    model.to(accelerator.device						)

    # Instantiate optimizer
    __UpperCAmelCase				:      Any	 =    AdamW(params=model.parameters()    ,						lr=__lowerCamelCase						)
    __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    get_dataloaders(__lowerCamelCase    ,						__lowerCamelCase						)

    # Instantiate scheduler
    __UpperCAmelCase				:      str	 =    get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase    ,						num_warmup_steps=100    ,						num_training_steps=(len(__lowerCamelCase						) * 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.
    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    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`.
        batch.to(accelerator.device						)
        __UpperCAmelCase				:      Dict	 =    model(**__lowerCamelCase						)
        __UpperCAmelCase				:      int	 =    outputs.loss
        accelerator.backward(__lowerCamelCase						)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(__lowerCamelCase						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        with torch.no_grad():
          __UpperCAmelCase				:      List[Any]	 =    model(**__lowerCamelCase						)
        __UpperCAmelCase				:      Union[str, Any]	 =    outputs.logits.argmax(dim=-1						)
        __UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
        metric.add_batch(
            predictions=__lowerCamelCase    ,						references=__lowerCamelCase    ,						)

      __UpperCAmelCase				:      Any	 =    metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""    ,						__lowerCamelCase						)

    # 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__ (       ):
  __UpperCAmelCase				:      List[str]	 =    argparse.ArgumentParser(description="""Simple example of training script."""						)
  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("""--cpu"""    ,						action="""store_true"""    ,						help="""If passed, will train on the CPU."""						)
  __UpperCAmelCase				:      List[str]	 =    parser.parse_args()
  __UpperCAmelCase				:      Tuple	 =    {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(__lowerCamelCase    ,						__lowerCamelCase						)


if __name__ == "__main__":
       main()






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

