




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


lowerCamelCase							:						Tuple					       =  16
lowerCamelCase							:						Optional[int]					       =  32

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Accelerator      ,				lowercase      :			DatasetDict      ,				lowercase      :			List[int]      ,				lowercase      :			List[int]      ,				lowercase      :			int = 16	):


      '''simple docstring'''



      lowerCamelCase_							=       AutoTokenizer.from_pretrained('bert-base-cased'	)
      lowerCamelCase_							=       DatasetDict(
          {
              'train': dataset['train'].select(lowerCAmelCase__	),
              'validation': dataset['train'].select(lowerCAmelCase__	),
              'test': dataset['validation'],
          }	)

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

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

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

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

      lowerCamelCase_							=       DataLoader(
          tokenized_datasets['test']      ,				shuffle=lowerCAmelCase__      ,				collate_fn=lowerCAmelCase__      ,				batch_size=lowerCAmelCase__	)

      return train_dataloader, eval_dataloader, test_dataloader

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Dict      ,				lowercase      :			Optional[int]	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      # Download the dataset
      lowerCamelCase_							=       load_dataset('glue'      ,				'mrpc'	)
      # Create our splits
      lowerCamelCase_							=       StratifiedKFold(n_splits=int(args.num_folds	)	)
      # 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'	)

      # If the batch size is too big we use gradient accumulation
      lowerCamelCase_							=       1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCamelCase_							=       batch_size // MAX_GPU_BATCH_SIZE
            lowerCamelCase_							=       MAX_GPU_BATCH_SIZE

      set_seed(lowerCAmelCase__	)

      # New Code #
      # Create our folds:
      lowerCamelCase_							=       kfold.split(np.zeros(datasets['train'].num_rows	)      ,				datasets['train']['label']	)
      lowerCamelCase_							=       []
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase__	):
            lowerCamelCase_							=       get_fold_dataloaders(
                lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__      ,				)
            # 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=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).
            lowerCamelCase_							=       model.to(accelerator.device	)

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

            # Instantiate scheduler
            lowerCamelCase_							=       get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__      ,				num_warmup_steps=1_00      ,				num_training_steps=(len(lowerCAmelCase__	) * num_epochs) // gradient_accumulation_steps      ,				)

            # 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_							=       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	)
                        lowerCamelCase_							=       model(**lowerCAmelCase__	)
                        lowerCamelCase_							=       outputs.loss
                        lowerCamelCase_							=       loss / gradient_accumulation_steps
                        accelerator.backward(lowerCAmelCase__	)
                        if step % gradient_accumulation_steps == 0:
                              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():
                              lowerCamelCase_							=       model(**lowerCAmelCase__	)
                        lowerCamelCase_							=       outputs.logits.argmax(dim=-1	)
                        lowerCamelCase_							=       accelerator.gather_for_metrics((predictions, batch['labels'])	)
                        metric.add_batch(
                            predictions=lowerCAmelCase__      ,				references=lowerCAmelCase__      ,				)

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

            # New Code #
            # We also run predictions on the test set at the very end
            lowerCamelCase_							=       []
            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():
                        lowerCamelCase_							=       model(**lowerCAmelCase__	)
                  lowerCamelCase_							=       outputs.logits
                  lowerCamelCase_							=       accelerator.gather_for_metrics((predictions, batch['labels'])	)
                  fold_predictions.append(predictions.cpu()	)
                  if i == 0:
                        # We need all of the test predictions
                        test_references.append(references.cpu()	)
        # Use accelerator.print to print only on the main process.
            test_predictions.append(torch.cat(lowerCAmelCase__      ,				dim=0	)	)
            # We now need to release all our memory and get rid of the current model, optimizer, etc
            accelerator.free_memory()
      # New Code #
      # Finally we check the accuracy of our folded results:
      lowerCamelCase_							=       torch.cat(lowerCAmelCase__      ,				dim=0	)
      lowerCamelCase_							=       torch.stack(lowerCAmelCase__      ,				dim=0	).sum(dim=0	).div(int(args.num_folds	)	).argmax(dim=-1	)
      lowerCamelCase_							=       metric.compute(predictions=lowerCAmelCase__      ,				references=lowerCAmelCase__	)
      accelerator.print('Average test metrics from all folds:'      ,				lowerCAmelCase__	)

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       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.'	)
      # New Code #
      parser.add_argument('--num_folds'      ,				type=lowerCAmelCase__      ,				default=3      ,				help='The number of splits to perform across the dataset'	)
      lowerCamelCase_							=       parser.parse_args()
      lowerCamelCase_							=       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
      training_function(lowerCAmelCase__      ,				lowerCAmelCase__	)


if __name__ == "__main__":
 main()










"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )