





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


UpperCAmelCase						:   Any			  =     16
UpperCAmelCase						:   Any			  =     32







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						a = 16  )      ->      Any:
					__A	:						List[Any]       =				AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A	:						int       =				DatasetDict(
					    {
					        'train': dataset['train'].select(a  ),
					        'validation': dataset['train'].select(a  ),
					        'test': dataset['validation'],
					    }  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A	:						Optional[Any]       =				tokenizer(examples['sentence1']					,						examples['sentence2']					,						truncation=a					,						max_length=a  )
										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():
										__A	:						int       =				datasets.map(
										    a					,						batched=a					,						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
					__A	:						int       =				tokenized_datasets.rename_column('label'					,						'labels'  )

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

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

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

					__A	:						Optional[Any]       =				DataLoader(
					    tokenized_datasets['test']					,						shuffle=a					,						collate_fn=a					,						batch_size=a  )

					return train_dataloader, eval_dataloader, test_dataloader







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

					__A	:						str       =				evaluate.load('glue'					,						'mrpc'  )

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

					set_seed(a  )

					# New Code #
					# Create our folds:
					__A	:						Optional[Any]       =				kfold.split(np.zeros(datasets['train'].num_rows  )					,						datasets['train']['label']  )
					__A	:						int       =				[]
					# Iterate over them
					for i, (train_idxs, valid_idxs) in enumerate(a  ):
										__A    ,				__A    ,				__A	:						Optional[Any]       =				get_fold_dataloaders(
										    a					,						a					,						a					,						a					,						)
										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A	:						Any       =				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,						return_dict=a  )

										# 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).
										__A	:						str       =				model.to(accelerator.device  )

										# Instantiate optimizer
										__A	:						Tuple       =				AdamW(params=model.parameters()					,						lr=a  )

										# Instantiate scheduler
										__A	:						Optional[int]       =				get_linear_schedule_with_warmup(
										    optimizer=a					,						num_warmup_steps=1_00					,						num_training_steps=(len(a  ) * 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.
										__A    ,				__A    ,				__A    ,				__A    ,				__A	:						int       =				accelerator.prepare(
										    a					,						a					,						a					,						a					,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				__A	:						Optional[Any]       =				model(**a  )
																				__A	:						Optional[int]       =				outputs.loss
																				__A	:						Optional[int]       =				loss / gradient_accumulation_steps
																				accelerator.backward(a  )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															for step, batch in enumerate(a  ):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device  )
																				with torch.no_grad():
																									__A	:						List[str]       =				model(**a  )
																				__A	:						Any       =				outputs.logits.argmax(dim=-1  )
																				__A    ,				__A	:						Dict       =				accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a					,						references=a					,						)

															__A	:						Optional[int]       =				metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:"""					,						a  )

										# New Code #
										# We also run predictions on the test set at the very end
										__A	:						Any       =				[]
										for step, batch in enumerate(a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															with torch.no_grad():
																				__A	:						Dict       =				model(**a  )
															__A	:						str       =				outputs.logits
															__A    ,				__A	:						List[str]       =				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(a					,						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:
					__A	:						List[Any]       =				torch.cat(a					,						dim=0  )
					__A	:						int       =				torch.stack(a					,						dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
					__A	:						Any       =				metric.compute(predictions=a					,						references=a  )
					accelerator.print('Average test metrics from all folds:'					,						a  )







def  _SCREAMING_SNAKE_CASE				(   )      ->      Union[str, Any]:
					__A	:						str       =				argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision'					,						type=a					,						default=a					,						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=a					,						default=3					,						help='The number of splits to perform across the dataset'  )
					__A	:						List[Any]       =				parser.parse_args()
					__A	:						List[str]       =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(a					,						a  )


if __name__ == "__main__":
				main()











from heapq import heappop, heappush

import numpy as np







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      tuple[float | int, list[tuple[int, int]]]:
					__A    ,				__A	:						int       =				grid.shape
					__A	:						Any       =				[-1, 1, 0, 0]
					__A	:						Optional[Any]       =				[0, 0, -1, 1]
					if allow_diagonal:
										dx += [-1, -1, 1, 1]
										dy += [-1, 1, -1, 1]

					__A    ,				__A	:						Optional[int]       =				[(0, source)], set()
					__A	:						Any       =				np.full((rows, cols)					,						np.inf  )
					__A	:						Any       =				0
					__A	:						Any       =				np.empty((rows, cols)					,						dtype=a  )
					__A	:						Optional[Any]       =				None

					while queue:
										((__A)    ,				(__A))	:						List[str]       =				heappop(a  )
										if (x, y) in visited:
															continue
										visited.add((x, y)  )

										if (x, y) == destination:
															__A	:						int       =				[]
															while (x, y) != source:
																				path.append((x, y)  )
																				__A    ,				__A	:						Optional[int]       =				predecessors[x, y]
															path.append(a  )  # add the source manually
															path.reverse()
															return matrix[destination], path

										for i in range(len(a  )  ):
															__A    ,				__A	:						Union[str, Any]       =				x + dx[i], y + dy[i]
															if 0 <= nx < rows and 0 <= ny < cols:
																				__A	:						Optional[int]       =				grid[nx][ny]
																				if next_node == 1 and matrix[nx, ny] > dist + 1:
																									heappush(a					,						(dist + 1, (nx, ny))  )
																									__A	:						List[Any]       =				dist + 1
																									__A	:						Union[str, Any]       =				(x, y)

					return np.inf, []


if __name__ == "__main__":
				import doctest

				doctest.testmod()




