


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_ :Union[str, Any]  							=			16
a_ :Any  							=			32




def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__ = 1_6							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__       :  Any						     =		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)
            SCREAMING_SNAKE_CASE__       :  Dict						     =		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():
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		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
      SCREAMING_SNAKE_CASE__       :  Optional[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.
            SCREAMING_SNAKE_CASE__       :  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":
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		8
            else:
                  SCREAMING_SNAKE_CASE__       :  int						     =		None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		DataLoader(
          tokenized_datasets['''train''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		DataLoader(
          tokenized_datasets['''validation''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		DataLoader(
          tokenized_datasets['''test''']      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__							)

      return train_dataloader, eval_dataloader, test_dataloader




def    a   (							A__      ,    A__							)	-> int:




      '''simple docstring'''

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

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		evaluate.load('''glue'''      ,    '''mrpc'''							)

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

      set_seed(A__							)

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

            # Instantiate optimizer
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		AdamW(params=model.parameters()      ,    lr=A__							)

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

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




def    a   (							)	-> Union[str, Any]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		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'''							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		parser.parse_args()
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(A__      ,    A__							)


if __name__ == "__main__":
    main()














'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






