



from __future__ import annotations







def    _snake_case (       __snake_case    )     ->     None:






      '''simple docstring'''







      create_state_space_tree(__snake_case					,  []					,  0					,  [0 for i in range(len(__snake_case    )    )]    )







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )     ->     None:






      '''simple docstring'''







      if index == len(__snake_case    ):
            print(__snake_case    )
            return

      for i in range(len(__snake_case    )    ):
            if not index_used[i]:
                  current_sequence.append(sequence[i]    )
                  UpperCAmelCase_  :	List[str]				      =		True
                  create_state_space_tree(__snake_case					,  __snake_case					,  index + 1					,  __snake_case    )
                  current_sequence.pop()
                  UpperCAmelCase_  :	Optional[int]				      =		False
__lowerCamelCase         =			[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCamelCase         =			["A", "B", "C"]





generate_all_permutations(sequence_a)





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         =			16
__lowerCamelCase         =			32







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = 1_6    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	int				      =		AutoTokenizer.from_pretrained("bert-base-cased"    )
      UpperCAmelCase_  :	List[Any]				      =		DatasetDict(
          {
              "train": dataset["train"].select(__snake_case    ),
              "validation": dataset["train"].select(__snake_case    ),
              "test": dataset["validation"],
          }    )

      def tokenize_function(__snake_case    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_  :	List[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():
            UpperCAmelCase_  :	int				      =		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
      UpperCAmelCase_  :	Tuple				      =		tokenized_datasets.rename_column("label"					,  "labels"    )

      def collate_fn(__snake_case    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase_  :	Dict				      =		1_2_8 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_  :	Optional[Any]				      =		1_6
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_  :	str				      =		8
            else:
                  UpperCAmelCase_  :	Dict				      =		None

            return tokenizer.pad(
                __snake_case					,  padding="longest"					,  max_length=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_tensors="pt"					,  )

      # Instantiate dataloaders.
      UpperCAmelCase_  :	List[str]				      =		DataLoader(
          tokenized_datasets["train"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )
      UpperCAmelCase_  :	Union[str, Any]				      =		DataLoader(
          tokenized_datasets["validation"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      UpperCAmelCase_  :	str				      =		DataLoader(
          tokenized_datasets["test"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=__snake_case    )

      return train_dataloader, eval_dataloader, test_dataloader







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







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

      UpperCAmelCase_  :	Optional[Any]				      =		evaluate.load("glue"					,  "mrpc"    )

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

      set_seed(__snake_case    )

      # New Code #
      # Create our folds:
      UpperCAmelCase_  :	int				      =		kfold.split(np.zeros(datasets["train"].num_rows    )					,  datasets["train"]["label"]    )
      UpperCAmelCase_  :	Dict				      =		[]
      # Iterate over them
      for i, (train_idxs, valid_idxs) in enumerate(__snake_case    ):
            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	str				      =		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)
            UpperCAmelCase_  :	Optional[int]				      =		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).
            UpperCAmelCase_  :	Union[str, Any]				      =		model.to(accelerator.device    )

            # Instantiate optimizer
            UpperCAmelCase_  :	Tuple				      =		AdamW(params=model.parameters()					,  lr=__snake_case    )

            # Instantiate scheduler
            UpperCAmelCase_  :	str				      =		get_linear_schedule_with_warmup(
                optimizer=__snake_case					,  num_warmup_steps=1_0_0					,  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.
            UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		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    )
                        UpperCAmelCase_  :	Optional[Any]				      =		model(**__snake_case    )
                        UpperCAmelCase_  :	Dict				      =		outputs.loss
                        UpperCAmelCase_  :	List[Any]				      =		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():
                              UpperCAmelCase_  :	str				      =		model(**__snake_case    )
                        UpperCAmelCase_  :	Optional[int]				      =		outputs.logits.argmax(dim=-1    )
                        UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		accelerator.gather_for_metrics((predictions, batch["labels"])    )
                        metric.add_batch(
                            predictions=__snake_case					,  references=__snake_case					,  )

                  UpperCAmelCase_  :	Optional[Any]				      =		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
            UpperCAmelCase_  :	Union[str, Any]				      =		[]
            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():
                        UpperCAmelCase_  :	Tuple				      =		model(**__snake_case    )
                  UpperCAmelCase_  :	Optional[int]				      =		outputs.logits
                  UpperCAmelCase_      ,      UpperCAmelCase_  :	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(__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:
      UpperCAmelCase_  :	Union[str, Any]				      =		torch.cat(__snake_case					,  dim=0    )
      UpperCAmelCase_  :	str				      =		torch.stack(__snake_case					,  dim=0    ).sum(dim=0    ).div(int(args.num_folds    )    ).argmax(dim=-1    )
      UpperCAmelCase_  :	List[str]				      =		metric.compute(predictions=__snake_case					,  references=__snake_case    )
      accelerator.print("Average test metrics from all folds:"					,  __snake_case    )







def    _snake_case (       )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		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"    )
      UpperCAmelCase_  :	Any				      =		parser.parse_args()
      UpperCAmelCase_  :	List[Any]				      =		{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__snake_case					,  __snake_case    )


if __name__ == "__main__":
   main()
