
'''simple docstring'''







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
#
########################################################################


_lowercase :  Any    							=       1_6
_lowercase :  Optional[Any]    							=       3_2
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Accelerator      ,      __SCREAMING_SNAKE_CASE					: DatasetDict      ,      __SCREAMING_SNAKE_CASE					: List[int]      ,      __SCREAMING_SNAKE_CASE					: List[int]      ,      __SCREAMING_SNAKE_CASE					: int = 16							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       AutoTokenizer.from_pretrained('''bert-base-cased'''							)
						lowercase_	:     Optional[int]													=       DatasetDict(
						    {
						        '''train''': dataset['''train'''].select(UpperCAmelCase_							),
						        '''validation''': dataset['''train'''].select(UpperCAmelCase_							),
						        '''test''': dataset['''validation'''],
						    }							)

						def tokenize_function(__SCREAMING_SNAKE_CASE					: Optional[Any]							):
												# max_length=None => use the model max length (it's actually the default)
												lowercase_	:     int													=       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():
												lowercase_	:     Optional[int]													=       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
						lowercase_	:     Optional[int]													=       tokenized_datasets.rename_column('''label'''      ,      '''labels'''							)

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

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

						# Instantiate dataloaders.
						lowercase_	:     Optional[Any]													=       DataLoader(
						    tokenized_datasets['''train''']      ,      shuffle=UpperCAmelCase_      ,      collate_fn=UpperCAmelCase_      ,      batch_size=UpperCAmelCase_							)
						lowercase_	:     Any													=       DataLoader(
						    tokenized_datasets['''validation''']      ,      shuffle=UpperCAmelCase_      ,      collate_fn=UpperCAmelCase_      ,      batch_size=UpperCAmelCase_							)

						lowercase_	:     Optional[int]													=       DataLoader(
						    tokenized_datasets['''test''']      ,      shuffle=UpperCAmelCase_      ,      collate_fn=UpperCAmelCase_      ,      batch_size=UpperCAmelCase_							)

						return train_dataloader, eval_dataloader, test_dataloader
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       []
						# Download the dataset
						lowercase_	:     int													=       load_dataset('''glue'''      ,      '''mrpc'''							)
						# Create our splits
						lowercase_	:     Tuple													=       StratifiedKFold(n_splits=int(args.num_folds							)							)
						# Initialize accelerator
						lowercase_	:     Optional[Any]													=       Accelerator(cpu=args.cpu      ,      mixed_precision=args.mixed_precision							)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						lowercase_	:     List[Any]													=       config['lr']
						lowercase_	:     str													=       int(config['''num_epochs''']							)
						lowercase_	:     Optional[int]													=       int(config['''seed''']							)
						lowercase_	:     Union[str, Any]													=       int(config['''batch_size''']							)

						lowercase_	:     Tuple													=       evaluate.load('''glue'''      ,      '''mrpc'''							)

						# If the batch size is too big we use gradient accumulation
						lowercase_	:     Optional[Any]													=       1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase_	:     Optional[Any]													=       batch_size // MAX_GPU_BATCH_SIZE
												lowercase_	:     List[str]													=       MAX_GPU_BATCH_SIZE

						set_seed(UpperCAmelCase_							)

						# New Code #
						# Create our folds:
						lowercase_	:     str													=       kfold.split(np.zeros(datasets['''train'''].num_rows							)      ,      datasets['''train''']['''label''']							)
						lowercase_	:     Optional[int]													=       []
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase_							):
												lowercase_	:     int													=       get_fold_dataloaders(
												    UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      )
												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												lowercase_	:     Optional[Any]													=       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).
												lowercase_	:     Optional[Any]													=       model.to(accelerator.device							)

												# Instantiate optimizer
												lowercase_	:     Optional[Any]													=       AdamW(params=model.parameters()      ,      lr=UpperCAmelCase_							)

												# Instantiate scheduler
												lowercase_	:     List[str]													=       get_linear_schedule_with_warmup(
												    optimizer=UpperCAmelCase_      ,      num_warmup_steps=100      ,      num_training_steps=(len(UpperCAmelCase_							) * 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.
												lowercase_	:     int													=       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							)
																								lowercase_	:     Any													=       model(**UpperCAmelCase_							)
																								lowercase_	:     int													=       outputs.loss
																								lowercase_	:     List[str]													=       loss / gradient_accumulation_steps
																								accelerator.backward(UpperCAmelCase_							)
																								if step % gradient_accumulation_steps == 0:
																														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():
																														lowercase_	:     Any													=       model(**UpperCAmelCase_							)
																								lowercase_	:     List[Any]													=       outputs.logits.argmax(dim=-1							)
																								lowercase_	:     Dict													=       accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
																								metric.add_batch(
																								    predictions=UpperCAmelCase_      ,      references=UpperCAmelCase_      ,      )

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

												# New Code #
												# We also run predictions on the test set at the very end
												lowercase_	:     str													=       []
												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():
																								lowercase_	:     Tuple													=       model(**UpperCAmelCase_							)
																		lowercase_	:     int													=       outputs.logits
																		lowercase_	:     int													=       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(UpperCAmelCase_      ,      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:
						lowercase_	:     Tuple													=       torch.cat(UpperCAmelCase_      ,      dim=0							)
						lowercase_	:     Dict													=       torch.stack(UpperCAmelCase_      ,      dim=0							).sum(dim=0							).div(int(args.num_folds							)							).argmax(dim=-1							)
						lowercase_	:     Tuple													=       metric.compute(predictions=UpperCAmelCase_      ,      references=UpperCAmelCase_							)
						accelerator.print('''Average test metrics from all folds:'''      ,      UpperCAmelCase_							)
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_	:     Dict													=       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.'''							)
						# New Code #
						parser.add_argument('''--num_folds'''      ,      type=UpperCAmelCase_      ,      default=3      ,      help='''The number of splits to perform across the dataset'''							)
						lowercase_	:     Union[str, Any]													=       parser.parse_args()
						lowercase_	:     List[str]													=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(UpperCAmelCase_      ,      UpperCAmelCase_							)


if __name__ == "__main__":
							main()







'''simple docstring'''



import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





@require_torch
@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






