"""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
#
########################################################################


SCREAMING_SNAKE_CASE__:List[str]			=	16
SCREAMING_SNAKE_CASE__:str			=	32



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a = 1_6							):
       __a     				=  AutoTokenizer.from_pretrained("bert-base-cased"							)
       __a     				=  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)
              __a     				=  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():
              __a     				=  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
       __a     				=  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.
              __a     				=  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":
                     __a     				=  1_6
              elif accelerator.mixed_precision != "no":
                     __a     				=  8
              else:
                     __a     				=  None

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

       # Instantiate dataloaders.
       __a     				=  DataLoader(
           tokenized_datasets["train"] ,  shuffle=a ,  collate_fn=a ,  batch_size=a							)
       __a     				=  DataLoader(
           tokenized_datasets["validation"] ,  shuffle=a ,  collate_fn=a ,  batch_size=a							)

       __a     				=  DataLoader(
           tokenized_datasets["test"] ,  shuffle=a ,  collate_fn=a ,  batch_size=a							)

       return train_dataloader, eval_dataloader, test_dataloader



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

       __a     				=  evaluate.load("glue" ,  "mrpc"							)

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

       set_seed(a							)

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

              # Instantiate optimizer
              __a     				=  AdamW(params=model.parameters() ,  lr=a							)

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

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



def 					_lowerCamelCase(					):
       __a     				=  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"							)
       __a     				=  parser.parse_args()
       __a     				=  {"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 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  ):
       __a     				=  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 					_lowerCamelCase(					a							):
       __a     				=  []
       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)
