




'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					UNetaDModel
       lowercase			:					PNDMScheduler






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              super().__init__()

              lowerCamelCase_							=PNDMScheduler.from_config(scheduler.config						)

              self.register_modules(unet=lowerCAmelCase,	scheduler=lowerCAmelCase						)






       @torch.no_grad()
       def __call__(							self,	lowerCAmelCase = 1,	lowerCAmelCase = 50,	lowerCAmelCase = None,	lowerCAmelCase = "pil",	lowerCAmelCase = True,	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=randn_tensor(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),	generator=lowerCAmelCase,	device=self.device,	)

              self.scheduler.set_timesteps(lowerCAmelCase						)
              for t in self.progress_bar(self.scheduler.timesteps						):
                     lowerCamelCase_							=self.unet(lowerCAmelCase,	lowerCAmelCase						).sample

                     lowerCamelCase_							=self.scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						).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(lowerCAmelCase						)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=lowerCAmelCase						)










'''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
#
########################################################################

a_   :	Any							   = 32





def   a_							(   __snake_case							:    Accelerator     , __snake_case							:    int = 16     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=AutoTokenizer.from_pretrained('''bert-base-cased'''     )
       lowerCamelCase_							=load_dataset('''glue'''     , '''mrpc'''     )

       def tokenize_function(__snake_case							:    str     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCamelCase_							=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():
              lowerCamelCase_							=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
       lowerCamelCase_							=tokenized_datasets.rename_column('''label'''     , '''labels'''     )

       def collate_fn(__snake_case							:    Union[str, Any]     ):
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase_							=16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase_							=8
              else:
                     lowerCamelCase_							=None

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

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

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





def   a_							(   __snake_case							:    Optional[int]     , __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     , __snake_case     ) == "1":
              lowerCamelCase_							=2
       # Initialize accelerator
       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_							=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(__snake_case     )     )
       def inner_training_loop(__snake_case							:    Tuple     ):
              # 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_							=observed_batch_size // batch_size

              # And then set it in the Accelerator directly:
              lowerCamelCase_							=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(__snake_case     )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowerCamelCase_							=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).
              lowerCamelCase_							=model.to(accelerator.device     )

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

              # Instantiate scheduler
              lowerCamelCase_							=get_linear_schedule_with_warmup(
                  optimizer=__snake_case     , num_warmup_steps=100     , 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.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=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()
                     for step, batch in enumerate(__snake_case     ):
                            # And perform gradient accumulation
                            with accelerator.accumulate(__snake_case     ):
                                   # We could avoid this line since we set the accelerator with `device_placement=True`.
                                   batch.to(accelerator.device     )
                                   lowerCamelCase_							=model(**__snake_case     )
                                   lowerCamelCase_							=outputs.loss
                                   accelerator.backward(__snake_case     )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                     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():
                                   lowerCamelCase_							=model(**__snake_case     )
                            lowerCamelCase_							=outputs.logits.argmax(dim=-1     )
                            lowerCamelCase_,   lowerCamelCase_							=accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
                            metric.add_batch(
                                predictions=__snake_case     , references=__snake_case     , )

                     lowerCamelCase_							=metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(F'''epoch {epoch}:'''     , __snake_case     )

    # 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   a_							(   )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=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.'''     , )
       parser.add_argument('''--cpu'''     , action='''store_true'''     , help='''If passed, will train on the CPU.'''     )
       lowerCamelCase_							=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_							={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
       training_function(__snake_case     , __snake_case     )


if __name__ == "__main__":
     main()




