






"""simple docstring"""



import argparse
import os

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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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)
#
# 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
#
########################################################################


_UpperCamelCase		:  List[str]						=		1_6
_UpperCamelCase		:  Optional[int]						=		3_2
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Accelerator		,					__snake_case			:  int = 16    ):





  '''simple docstring'''





  lowercase							    =       AutoTokenizer.from_pretrained('bert-base-cased'    )
  lowercase							    =       load_dataset('glue'		,					'mrpc'    )

  def tokenize_function(__snake_case			:  Dict    ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase							    =       tokenizer(examples['sentence1']		,					examples['sentence2']		,					truncation=__snake_case		,					max_length=__snake_case    )
    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():
    lowercase							    =       datasets.map(
        __snake_case		,					batched=__snake_case		,					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
  lowercase							    =       tokenized_datasets.rename_column('label'		,					'labels'    )

  def collate_fn(__snake_case			:  List[Any]    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowercase							    =       1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowercase							    =       16
    elif accelerator.mixed_precision != "no":
      lowercase							    =       8
    else:
      lowercase							    =       None

    return tokenizer.pad(
        __snake_case		,					padding='longest'		,					max_length=__snake_case		,					pad_to_multiple_of=__snake_case		,					return_tensors='pt'		,					)

  # Instantiate dataloaders.
  lowercase							    =       DataLoader(
      tokenized_datasets['train']		,					shuffle=__snake_case		,					collate_fn=__snake_case		,					batch_size=__snake_case    )
  lowercase							    =       DataLoader(
      tokenized_datasets['validation']		,					shuffle=__snake_case		,					collate_fn=__snake_case		,					batch_size=__snake_case    )

  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

    _UpperCamelCase		:  Union[str, Any]						=		mocked_dataloaders  # noqa: F811
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,					__snake_case    ) == "1":
    lowercase							    =       2
  # New Code #
  lowercase							    =       int(args.gradient_accumulation_steps    )
  lowercase							    =       int(args.local_sgd_steps    )
  # Initialize accelerator
  lowercase							    =       Accelerator(
      cpu=args.cpu		,					mixed_precision=args.mixed_precision		,					gradient_accumulation_steps=__snake_case    )
  if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
    raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase							    =       config['lr']
  lowercase							    =       int(config['num_epochs']    )
  lowercase							    =       int(config['seed']    )
  lowercase							    =       int(config['batch_size']    )

  lowercase							    =       evaluate.load('glue'		,					'mrpc'    )

  set_seed(__snake_case    )
  lowercase    ,     lowercase							    =       get_dataloaders(__snake_case		,					__snake_case    )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase							    =       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,					return_dict=__snake_case    )

  # 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).
  lowercase							    =       model.to(accelerator.device    )

  # Instantiate optimizer
  lowercase							    =       AdamW(params=model.parameters()		,					lr=__snake_case    )

  # Instantiate scheduler
  lowercase							    =       get_linear_schedule_with_warmup(
      optimizer=__snake_case		,					num_warmup_steps=1_00		,					num_training_steps=(len(__snake_case    ) * 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.
  lowercase    ,     lowercase    ,     lowercase    ,     lowercase    ,     lowercase							    =       accelerator.prepare(
      __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )

  # Now we train the model
  for epoch in range(__snake_case    ):
    model.train()
    with LocalSGD(
        accelerator=__snake_case		,					model=__snake_case		,					local_sgd_steps=__snake_case		,					enabled=local_sgd_steps is not None    ) as local_sgd:
      for step, batch in enumerate(__snake_case    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        # New code #
        # We use the new `accumulate` context manager to perform gradient accumulation
        # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
        with accelerator.accumulate(__snake_case    ):
          lowercase							    =       model(**__snake_case    )
          lowercase							    =       output.loss
          accelerator.backward(__snake_case    )
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          # LocalSGD-specific line
          local_sgd.step()

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

    lowercase							    =       metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'		,					__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,					type=__snake_case		,					default=__snake_case		,					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.'		,					)
  # New Code #
  parser.add_argument(
      '--gradient_accumulation_steps'		,					type=__snake_case		,					default=1		,					help='The number of minibatches to be ran before gradients are accumulated.'		,					)
  parser.add_argument(
      '--local_sgd_steps'		,					type=__snake_case		,					default=8		,					help='Number of local SGD steps or None to disable local SGD'    )
  parser.add_argument('--cpu'		,					action='store_true'		,					help='If passed, will train on the CPU.'    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__snake_case		,					__snake_case    )


if __name__ == "__main__":
    main()










"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      super().__init__()
      self.register_modules(unet=_lowerCamelCase							,		scheduler=_lowerCamelCase       )







    @torch.no_grad()
    def __call__(			self							,		_lowerCamelCase = 1							,		_lowerCamelCase = None							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		**_lowerCamelCase							,		):
      lowercase							    =       torch.randn(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,		generator=_lowerCamelCase							,		)
      lowercase							    =       image.to(self.device       )

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

      for t in self.progress_bar(self.scheduler.timesteps       ):
        # 1. predict noise model_output
        lowercase							    =       self.unet(_lowerCamelCase							,		_lowerCamelCase       ).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
        lowercase							    =       self.scheduler.step(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ).prev_sample

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

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

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


