



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_						:     Optional[Any]	     =		1_6
a_						:     Optional[Any]	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16 ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained("bert-base-cased" )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

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

      def collate_fn(UpperCAmelCase__ ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCamelCase								=		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":
                  lowerCamelCase								=		16
            elif accelerator.mixed_precision != "no":
                  lowerCamelCase								=		8
            else:
                  lowerCamelCase								=		None

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

      # Instantiate dataloaders.
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["train"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )
      lowerCamelCase								=		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

     a_						:     int	     =		mocked_dataloaders  # noqa: F811

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,   UpperCAmelCase__ ) == "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 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=UpperCAmelCase__ )
      def inner_training_loop(UpperCAmelCase__ ):
            # 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(UpperCAmelCase__ )

            # 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=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								=		model.to(accelerator.device )

            # Instantiate optimizer
            lowerCamelCase								=		AdamW(params=model.parameters()   ,   lr=UpperCAmelCase__ )
            lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__ )

            # Instantiate scheduler
            lowerCamelCase								=		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			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		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`.
                        batch.to(accelerator.device )
                        lowerCamelCase								=		model(**UpperCAmelCase__ )
                        lowerCamelCase								=		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								=		model(**UpperCAmelCase__ )
                        lowerCamelCase								=		outputs.logits.argmax(dim=-1 )
                        lowerCamelCase			,							lowerCamelCase								=		accelerator.gather_for_metrics((predictions, batch["labels"]) )
                        metric.add_batch(
                            predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

                  lowerCamelCase								=		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     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		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								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		UNetaDModel(
               block_out_channels=(32, 64)				,			layers_per_block=2				,			sample_size=32				,			in_channels=3				,			out_channels=3				,			down_block_types=("DownBlock2D", "AttnDownBlock2D")				,			up_block_types=("AttnUpBlock2D", "UpBlock2D")				,			)
           return model







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.dummy_uncond_unet
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=2				,			generator=__a				,			output_type="numpy"				,			return_dict=__a    )[0]

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           lowerCamelCase								=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           lowerCamelCase								=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2





@slow
@require_torch
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		"google/ncsnpp-celebahq-256"
           lowerCamelCase								=		UNetaDModel.from_pretrained(__a    )
           lowerCamelCase								=		KarrasVeScheduler()

           lowerCamelCase								=		KarrasVePipeline(unet=__a				,			scheduler=__a    )
           pipe.to(__a    )
           pipe.set_progress_bar_config(disable=__a    )

           lowerCamelCase								=		torch.manual_seed(0    )
           lowerCamelCase								=		pipe(num_inference_steps=20				,			generator=__a				,			output_type="numpy"    ).images

           lowerCamelCase								=		image[0, -3:, -3:, -1]
           assert image.shape == (1, 2_56, 2_56, 3)
           lowerCamelCase								=		np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2