





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_        =							1_6
lowerCAmelCase_        =							3_2






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase = 16					)						->		Dict:






				"""simple docstring"""


				snake_case_		:       Tuple           =    AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				snake_case_		:       Dict           =    DatasetDict(
				    {
				        '''train''': dataset['''train'''].select(snake_case__					),
				        '''validation''': dataset['''train'''].select(snake_case__					),
				        '''test''': dataset['''validation'''],
				    }					)

				def tokenize_function(_UpperCamelCase					):
								# max_length=None => use the model max length (it's actually the default)
								snake_case_		:       Optional[Any]           =    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():
								snake_case_		:       Tuple           =    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
				snake_case_		:       str           =    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.
								snake_case_		:       Optional[int]           =    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":
												snake_case_		:       Optional[Any]           =    16
								elif accelerator.mixed_precision != "no":
												snake_case_		:       Dict           =    8
								else:
												snake_case_		:       Tuple           =    None

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

				# Instantiate dataloaders.
				snake_case_		:       List[Any]           =    DataLoader(
				    tokenized_datasets['''train''']					,							shuffle=snake_case__					,							collate_fn=snake_case__					,							batch_size=snake_case__					)
				snake_case_		:       Dict           =    DataLoader(
				    tokenized_datasets['''validation''']					,							shuffle=snake_case__					,							collate_fn=snake_case__					,							batch_size=snake_case__					)

				snake_case_		:       Dict           =    DataLoader(
				    tokenized_datasets['''test''']					,							shuffle=snake_case__					,							collate_fn=snake_case__					,							batch_size=snake_case__					)

				return train_dataloader, eval_dataloader, test_dataloader






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


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

				snake_case_		:       List[Any]           =    evaluate.load('''glue'''					,							'''mrpc'''					)

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

				set_seed(snake_case__					)

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

								# Instantiate optimizer
								snake_case_		:       Optional[int]           =    AdamW(params=model.parameters()					,							lr=snake_case__					)

								# Instantiate scheduler
								snake_case_		:       int           =    get_linear_schedule_with_warmup(
								    optimizer=snake_case__					,							num_warmup_steps=100					,							num_training_steps=(len(snake_case__					) * 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.
								snake_case_		:       Dict           =    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__					):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device					)
																snake_case_		:       Union[str, Any]           =    model(**snake_case__					)
																snake_case_		:       List[Any]           =    outputs.loss
																snake_case_		:       List[str]           =    loss / gradient_accumulation_steps
																accelerator.backward(snake_case__					)
																if step % gradient_accumulation_steps == 0:
																				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():
																				snake_case_		:       Any           =    model(**snake_case__					)
																snake_case_		:       List[str]           =    outputs.logits.argmax(dim=-1					)
																snake_case_		:       Tuple           =    accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
																metric.add_batch(
																    predictions=snake_case__					,							references=snake_case__					,							)

												snake_case_		:       str           =    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:'''					,							snake_case__					)

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






def 	lowerCamelCase_		(							)						->		List[str]:






				"""simple docstring"""


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


if __name__ == "__main__":
	main()








def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


