






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


__A    	=   1_6
__A    	=   3_2




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  ,		A  :       List[Any]  ,		A  :       str  ,		A  :       List[Any]  ,		A  :       Optional[Any] = 16  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= AutoTokenizer.from_pretrained('bert-base-cased'  )
   __snake_case				:      List[Any]     							= DatasetDict(
       {
           'train': dataset['train'].select(_lowercase  ),
           'validation': dataset['train'].select(_lowercase  ),
           'test': dataset['validation'],
       }  )

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

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

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

   # Instantiate dataloaders.
   __snake_case				:      List[str]     							= DataLoader(
       tokenized_datasets['train']  ,		shuffle=_lowercase  ,		collate_fn=_lowercase  ,		batch_size=_lowercase  )
   __snake_case				:      str     							= DataLoader(
       tokenized_datasets['validation']  ,		shuffle=_lowercase  ,		collate_fn=_lowercase  ,		batch_size=_lowercase  )

   __snake_case				:      Union[str, Any]     							= DataLoader(
       tokenized_datasets['test']  ,		shuffle=_lowercase  ,		collate_fn=_lowercase  ,		batch_size=_lowercase  )

   return train_dataloader, eval_dataloader, test_dataloader




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       Tuple  )    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= []
   # Download the dataset
   __snake_case				:      int     							= load_dataset('glue'  ,		'mrpc'  )
   # Create our splits
   __snake_case				:      Tuple     							= StratifiedKFold(n_splits=int(args.num_folds  )  )
   # Initialize accelerator
   __snake_case				:      str     							= 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				:      Dict     							= config['lr']
   __snake_case				:      int     							= int(config['num_epochs']  )
   __snake_case				:      Union[str, Any]     							= int(config['seed']  )
   __snake_case				:      str     							= int(config['batch_size']  )

   __snake_case				:      int     							= evaluate.load('glue'  ,		'mrpc'  )

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

   set_seed(_lowercase  )

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

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

      # Instantiate optimizer
      __snake_case				:      str     							= AdamW(params=model.parameters()  ,		lr=_lowercase  )

      # Instantiate scheduler
      __snake_case				:      Optional[Any]     							= get_linear_schedule_with_warmup(
          optimizer=_lowercase  ,		num_warmup_steps=1_00  ,		num_training_steps=(len(_lowercase  ) * 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				:      List[str]     							= accelerator.prepare(
          _lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase  ,		_lowercase  )

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

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

         __snake_case				:      int     							= metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""  ,		_lowercase  )

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




def        _SCREAMING_SNAKE_CASE  (				)    -> List[Any]:







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


if __name__ == "__main__":
      main()







'''simple docstring'''






from __future__ import annotations

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




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       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(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       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(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

   __snake_case				:      list[Sequence[Node | None]]     							= []

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= make_tree()
   print(F"""In-order Traversal: {inorder(A  )}"""  )
   print(F"""Pre-order Traversal: {preorder(A  )}"""  )
   print(F"""Post-order Traversal: {postorder(A  )}"""  ,		'\n'  )

   print(F"""Height of Tree: {height(A  )}"""  ,		'\n'  )

   print('Complete Level Order Traversal: '  )
   print(level_order(A  )  ,		'\n'  )

   print('Level-wise order Traversal: '  )

   for level in range(1  ,		height(A  ) + 1  ):
      print(F"""Level {level}:"""  ,		get_nodes_from_left_to_right(A  ,		level=A  )  )

   print('\nZigZag order Traversal: '  )
   print(zigzag(A  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()