'''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
#
########################################################################


_UpperCamelCase					:		Optional[Any]							      =		16
_UpperCamelCase					:		Optional[Any]							      =		32



def     __snake_case			(							lowerCAmelCase							:  Accelerator   ,	lowerCAmelCase							:  DatasetDict   ,	lowerCAmelCase							:  List[int]   ,	lowerCAmelCase							:  List[int]   ,	lowerCAmelCase							:  int = 16      ):
 __UpperCAmelCase								=       AutoTokenizer.from_pretrained('bert-base-cased'      )
 __UpperCAmelCase								=       DatasetDict(
     {
         'train': dataset['train'].select(lowerCAmelCase      ),
         'validation': dataset['train'].select(lowerCAmelCase      ),
         'test': dataset['validation'],
     }      )

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

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

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

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

 __UpperCAmelCase								=       DataLoader(
     tokenized_datasets['test']   ,	shuffle=lowerCAmelCase   ,	collate_fn=lowerCAmelCase   ,	batch_size=lowerCAmelCase      )

 return train_dataloader, eval_dataloader, test_dataloader



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

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

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

 set_seed(lowerCAmelCase      )

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

  # Instantiate optimizer
  __UpperCAmelCase								=       AdamW(params=model.parameters()   ,	lr=lowerCAmelCase      )

  # Instantiate scheduler
  __UpperCAmelCase								=       get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase   ,	num_warmup_steps=100   ,	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.
  __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase	,      __UpperCAmelCase								=       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      )
    __UpperCAmelCase								=       model(**lowerCAmelCase      )
    __UpperCAmelCase								=       outputs.loss
    __UpperCAmelCase								=       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():
     __UpperCAmelCase								=       model(**lowerCAmelCase      )
    __UpperCAmelCase								=       outputs.logits.argmax(dim=-1      )
    __UpperCAmelCase	,      __UpperCAmelCase								=       accelerator.gather_for_metrics((predictions, batch['labels'])      )
    metric.add_batch(
        predictions=lowerCAmelCase   ,	references=lowerCAmelCase   ,	)

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



def     __snake_case			(							):
 __UpperCAmelCase								=       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'      )
 __UpperCAmelCase								=       parser.parse_args()
 __UpperCAmelCase								=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 training_function(lowerCAmelCase   ,	lowerCAmelCase      )


if __name__ == "__main__":
     main()







from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any



@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					42
					A__     =					None
					A__     =					None






def 					UpperCamelCase   (      )  ->    Node | None:
		"""simple docstring"""




		lowercase__	     =						Node(1	)
		lowercase__	     =						Node(2	)
		lowercase__	     =						Node(3	)
		lowercase__	     =						Node(4	)
		lowercase__	     =						Node(5	)
		return tree






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [root.data, *preorder(root.left	), *preorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return postorder(root.left	) + postorder(root.right	) + [root.data] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    list[int]:
		"""simple docstring"""




		return [*inorder(root.left	), root.data, *inorder(root.right	)] if root else []






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    int:
		"""simple docstring"""




		return (max(height(root.left	)       ,       height(root.right	)	) + 1) if root else 0






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		if root is None:
				return output

		lowercase__	     =						deque([root]	)

		while process_queue:
				lowercase__	     =						process_queue.popleft()
				output.append(node.data	)

				if node.left:
						process_queue.append(node.left	)
				if node.right:
						process_queue.append(node.right	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if not root:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.left       ,       level - 1	)
						populate_output(root.right       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None       ,       __magic_name__	:				int	)  ->    Sequence[Node | None]:
		"""simple docstring"""




		lowercase__	     =						[]

		def populate_output(__magic_name__	:				Node | None       ,       __magic_name__	:				int	) -> None:
				if root is None:
						return
				if level == 1:
						output.append(root.data	)
				elif level > 1:
						populate_output(root.right       ,       level - 1	)
						populate_output(root.left       ,       level - 1	)

		populate_output(__magic_name__       ,       __magic_name__	)
		return output






def 					UpperCamelCase   (      __magic_name__	:				Node | None	)  ->    Sequence[Node | None] | list[Any]:
		"""simple docstring"""




		if root is None:
				return []

		lowercase__	     =						[]

		lowercase__	     =						0
		lowercase__	     =						height(__magic_name__	)

		for h in range(1       ,       height_tree + 1	):
				if not flag:
						output.append(get_nodes_from_left_to_right(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						1
				else:
						output.append(get_nodes_from_right_to_left(__magic_name__       ,       __magic_name__	)	)
						lowercase__	     =						0

		return output






def 					UpperCamelCase   (      )  ->    None:  # Main function for testing.
		"""simple docstring"""




		lowercase__	     =						make_tree()
		print(f'''In-order Traversal: {inorder(__magic_name__	)}'''	)
		print(f'''Pre-order Traversal: {preorder(__magic_name__	)}'''	)
		print(f'''Post-order Traversal: {postorder(__magic_name__	)}'''       ,       """\n"""	)

		print(f'''Height of Tree: {height(__magic_name__	)}'''       ,       """\n"""	)

		print("""Complete Level Order Traversal: """	)
		print(level_order(__magic_name__	)       ,       """\n"""	)

		print("""Level-wise order Traversal: """	)

		for level in range(1       ,       height(__magic_name__	) + 1	):
				print(f'''Level {level}:'''       ,       get_nodes_from_left_to_right(__magic_name__       ,       level=__magic_name__	)	)

		print("""\nZigZag order Traversal: """	)
		print(zigzag(__magic_name__	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

