





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_ 							=       16
UpperCAmelCase_ 							=       32





def   lowerCamelCase__       (       UpperCamelCase__     :    Accelerator    ,      UpperCamelCase__     :    DatasetDict    ,      UpperCamelCase__     :    List[int]    ,      UpperCamelCase__     :    List[int]    ,      UpperCamelCase__     :    int = 16  )     ->	int:



					'''simple docstring'''




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

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

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

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

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

					_snake_case													=			DataLoader(
					    tokenized_datasets['test']    ,      shuffle=UpperCamelCase__    ,      collate_fn=UpperCamelCase__    ,      batch_size=UpperCamelCase__  )

					return train_dataloader, eval_dataloader, test_dataloader





def   lowerCamelCase__       (       UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int  )     ->	Optional[int]:



					'''simple docstring'''




					_snake_case													=			[]
					# Download the dataset
					_snake_case													=			load_dataset('glue'    ,      'mrpc'  )
					# Create our splits
					_snake_case													=			StratifiedKFold(n_splits=int(args.num_folds  )  )
					# Initialize accelerator
					_snake_case													=			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													=			config['lr']
					_snake_case													=			int(config['num_epochs']  )
					_snake_case													=			int(config['seed']  )
					_snake_case													=			int(config['batch_size']  )

					_snake_case													=			evaluate.load('glue'    ,      'mrpc'  )

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

					set_seed(UpperCamelCase__  )

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

										# Instantiate optimizer
										_snake_case													=			AdamW(params=model.parameters()    ,      lr=UpperCamelCase__  )

										# Instantiate scheduler
										_snake_case													=			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.
										_snake_case							,       _snake_case							,       _snake_case							,       _snake_case							,       _snake_case													=			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  )
																				_snake_case													=			model(**UpperCamelCase__  )
																				_snake_case													=			outputs.loss
																				_snake_case													=			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():
																									_snake_case													=			model(**UpperCamelCase__  )
																				_snake_case													=			outputs.logits.argmax(dim=-1  )
																				_snake_case							,       _snake_case													=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=UpperCamelCase__    ,      references=UpperCamelCase__    ,      )

															_snake_case													=			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
										_snake_case													=			[]
										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():
																				_snake_case													=			model(**UpperCamelCase__  )
															_snake_case													=			outputs.logits
															_snake_case							,       _snake_case													=			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:
					_snake_case													=			torch.cat(UpperCamelCase__    ,      dim=0  )
					_snake_case													=			torch.stack(UpperCamelCase__    ,      dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
					_snake_case													=			metric.compute(predictions=UpperCamelCase__    ,      references=UpperCamelCase__  )
					accelerator.print('Average test metrics from all folds:'    ,      UpperCamelCase__  )





def   lowerCamelCase__       (       )     ->	Any:



					'''simple docstring'''




					_snake_case													=			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'  )
					_snake_case													=			parser.parse_args()
					_snake_case													=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(UpperCamelCase__    ,      UpperCamelCase__  )


if __name__ == "__main__":
					main()










from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def   lowerCamelCase__       (       )     ->	List[str]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			9, 14  # noqa: F841
					_snake_case													=			[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_snake_case													=			defaultdict(UpperCamelCase__  )
					for nodea, nodea, cost in edges:
										adjancency[nodea].append([nodea, cost]  )
										adjancency[nodea].append([nodea, cost]  )

					_snake_case													=			mst(UpperCamelCase__  )

					_snake_case													=			[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					for answer in expected:
										_snake_case													=			tuple(answer[:2]  )
										_snake_case													=			tuple(edge[::-1]  )
										assert edge in result or reverse in result



