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


lowercase__					       =	16
lowercase__					       =	32







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = 16   )	->Union[str, Any]:
  a__:     int										=	AutoTokenizer.from_pretrained('bert-base-cased'   )
  a__:     List[str]										=	DatasetDict(
      {
          'train': dataset['train'].select(A__   ),
          'validation': dataset['train'].select(A__   ),
          'test': dataset['validation'],
      }   )

  def tokenize_function(_SCREAMING_SNAKE_CASE   ):
    # max_length=None => use the model max length (it's actually the default)
    a__:     Any										=	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__:     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
  a__:     Optional[Any]										=	tokenized_datasets.rename_column('label'		,     'labels'   )

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

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

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

  a__:     int										=	DataLoader(
      tokenized_datasets['test']		,     shuffle=A__		,     collate_fn=A__		,     batch_size=A__   )

  return train_dataloader, eval_dataloader, test_dataloader







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

  a__:     Tuple										=	evaluate.load('glue'		,     'mrpc'   )

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

  set_seed(A__   )

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

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

    # Instantiate scheduler
    a__:     Optional[Any]										=	get_linear_schedule_with_warmup(
        optimizer=A__		,     num_warmup_steps=100		,     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__:     List[Any]										=	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__:     List[str]										=	model(**A__   )
        a__:     Optional[Any]										=	outputs.loss
        a__:     Dict										=	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__:     Optional[int]										=	model(**A__   )
        a__:     Optional[Any]										=	outputs.logits.argmax(dim=-1   )
        a__:     Dict										=	accelerator.gather_for_metrics((predictions, batch['labels'])   )
        metric.add_batch(
            predictions=A__		,     references=A__		,     )

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







def 						__a   (	)	->str:
  a__:     int										=	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__:     List[str]										=	parser.parse_args()
  a__:     Dict										=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(A__		,     A__   )


if __name__ == "__main__":
 main()










"""simple docstring"""
from __future__ import annotations

import time

lowerCamelCase_					=      list[tuple[int, int]]

lowerCamelCase_					=      [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_					=      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class 			UpperCamelCase_       :





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Node | None						)						->			Dict:
				UpperCAmelCase_     : Any			     =					pos_x
				UpperCAmelCase_     : str			     =					pos_y
				UpperCAmelCase_     : int			     =					(pos_y, pos_x)
				UpperCAmelCase_     : int			     =					goal_x
				UpperCAmelCase_     : Tuple			     =					goal_y
				UpperCAmelCase_     : Union[str, Any]			     =					parent







class 			UpperCamelCase_       :





	def __init__(      self					: List[Any]	,	lowerCAmelCase_					: tuple[int, int]	,	lowerCAmelCase_					: tuple[int, int]						)						->			Tuple:
				UpperCAmelCase_     : List[str]			     =					Node(start[1]	,	start[0]	,	goal[1]	,	goal[0]	,	lowerCAmelCase_						)
				UpperCAmelCase_     : int			     =					Node(goal[1]	,	goal[0]	,	goal[1]	,	goal[0]	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Union[str, Any]			     =					[self.start]
				UpperCAmelCase_     : int			     =					False





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Path | None:
				while self.node_queue:
							UpperCAmelCase_     : str			     =					self.node_queue.pop(0						)

							if current_node.pos == self.target.pos:
										UpperCAmelCase_     : Optional[Any]			     =					True
										return self.retrace_path(lowerCAmelCase_						)

							UpperCAmelCase_     : Optional[Any]			     =					self.get_successors(lowerCAmelCase_						)

							for node in successors:
										self.node_queue.append(lowerCAmelCase_						)

				if not self.reached:
							return [self.start.pos]
				return None





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: Node						)						->			list[Node]:
				UpperCAmelCase_     : List[str]			     =					[]
				for action in delta:
							UpperCAmelCase_     : List[Any]			     =					parent.pos_x + action[1]
							UpperCAmelCase_     : List[str]			     =					parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(lowerCAmelCase_						) - 1):
										continue

							if grid[pos_y][pos_x] != 0:
										continue

							successors.append(
							    Node(lowerCAmelCase_	,	lowerCAmelCase_	,	self.target.pos_y	,	self.target.pos_x	,	lowerCAmelCase_						)						)
				return successors





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Node | None						)						->			Path:
				UpperCAmelCase_     : Union[str, Any]			     =					node
				UpperCAmelCase_     : Union[str, Any]			     =					[]
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)						)
							UpperCAmelCase_     : Tuple			     =					current_node.parent
				path.reverse()
				return path







class 			UpperCamelCase_       :





	def __init__(      self					: str	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Tuple						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					BreadthFirstSearch(lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					BreadthFirstSearch(lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					False





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Path | None:
				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase_     : int			     =					self.fwd_bfs.node_queue.pop(0						)
							UpperCAmelCase_     : Dict			     =					self.bwd_bfs.node_queue.pop(0						)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase_     : str			     =					True
										return self.retrace_bidirectional_path(
										    lowerCAmelCase_	,	lowerCAmelCase_						)

							UpperCAmelCase_     : str			     =					current_bwd_node
							UpperCAmelCase_     : List[str]			     =					current_fwd_node

							UpperCAmelCase_     : Tuple			     =					{
							    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCAmelCase_						),
							    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCAmelCase_						),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(lowerCAmelCase_						)

				if not self.reached:
							return [self.fwd_bfs.start.pos]
				return None





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: Node	,	lowerCAmelCase_					: Node						)						->			Path:
				UpperCAmelCase_     : Optional[Any]			     =					self.fwd_bfs.retrace_path(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.bwd_bfs.retrace_path(lowerCAmelCase_						)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase_     : str			     =					fwd_path + bwd_path
				return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			import doctest

			doctest.testmod()
			lowerCamelCase_					=      (0, 0)
			lowerCamelCase_					=      (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			lowerCamelCase_					=      time.time()
			lowerCamelCase_					=      BreadthFirstSearch(init, goal)
			lowerCamelCase_					=      bfs.search()
			lowerCamelCase_					=      time.time() - start_bfs_time

			print('''Unidirectional BFS computation time : ''', bfs_time)

			lowerCamelCase_					=      time.time()
			lowerCamelCase_					=      BidirectionalBreadthFirstSearch(init, goal)
			lowerCamelCase_					=      bd_bfs.search()
			lowerCamelCase_					=      time.time() - start_bd_bfs_time

			print('''Bidirectional BFS computation time : ''', bd_bfs_time)



