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


UpperCAmelCase    					=   16
UpperCAmelCase    					=   32

def      A (       A_							:					Accelerator    ,							A_							:					DatasetDict    ,							A_							:					List[int]    ,							A_							:					List[int]    ,							A_							:					int = 16       )     ->   Tuple:
						snake_case       :     int =    AutoTokenizer.from_pretrained('''bert-base-cased'''       )
						snake_case       :     Any =    DatasetDict(
						    {
						        '''train''': dataset['''train'''].select(A_       ),
						        '''validation''': dataset['''train'''].select(A_       ),
						        '''test''': dataset['''validation'''],
						    }       )

						def tokenize_function(A_							:					int       ):
												# max_length=None => use the model max length (it's actually the default)
												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():
												snake_case       :     Tuple =    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
						snake_case       :     Any =    tokenized_datasets.rename_column('''label'''    ,							'''labels'''       )

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

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

						# Instantiate dataloaders.
						snake_case       :     Optional[int] =    DataLoader(
						    tokenized_datasets['''train''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )
						snake_case       :     Any =    DataLoader(
						    tokenized_datasets['''validation''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )

						snake_case       :     Optional[int] =    DataLoader(
						    tokenized_datasets['''test''']    ,							shuffle=A_    ,							collate_fn=A_    ,							batch_size=A_       )

						return train_dataloader, eval_dataloader, test_dataloader

def      A (       A_							:					Optional[Any]    ,							A_							:					str       )     ->   int:
						# New Code #
						snake_case       :     int =    []
						# Download the dataset
						snake_case       :     List[str] =    load_dataset('''glue'''    ,							'''mrpc'''       )
						# Create our splits
						snake_case       :     Dict =    StratifiedKFold(n_splits=int(args.num_folds       )       )
						# Initialize accelerator
						snake_case       :     Optional[int] =    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       :     List[Any] =    config['''lr''']
						snake_case       :     str =    int(config['''num_epochs''']       )
						snake_case       :     List[Any] =    int(config['''seed''']       )
						snake_case       :     Dict =    int(config['''batch_size''']       )

						snake_case       :     List[str] =    evaluate.load('''glue'''    ,							'''mrpc'''       )

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

						set_seed(A_       )

						# New Code #
						# Create our folds:
						snake_case       :     Tuple =    kfold.split(np.zeros(datasets['''train'''].num_rows       )    ,							datasets['''train''']['''label''']       )
						snake_case       :     int =    []
						# Iterate over them
						for i, (train_idxs, valid_idxs) in enumerate(A_       ):
												snake_case       :     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)
												snake_case       :     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).
												snake_case       :     List[Any] =    model.to(accelerator.device       )

												# Instantiate optimizer
												snake_case       :     Tuple =    AdamW(params=model.parameters()    ,							lr=A_       )

												# Instantiate scheduler
												snake_case       :     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.
												snake_case       :     Union[str, 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       )
																								snake_case       :     Any =    model(**A_       )
																								snake_case       :     Optional[int] =    outputs.loss
																								snake_case       :     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():
																														snake_case       :     Dict =    model(**A_       )
																								snake_case       :     Optional[Any] =    outputs.logits.argmax(dim=-1       )
																								snake_case       :     List[Any] =    accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
																								metric.add_batch(
																								    predictions=A_    ,							references=A_    ,							)

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

def      A (       )     ->   Any:
						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'''       )
						snake_case       :     List[Any] =    parser.parse_args()
						snake_case       :     str =    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A_    ,							A_       )


if __name__ == "__main__":
						main()



'''simple docstring'''





import numpy as np

class 						a   :




				def __init__(							self	:				Dict      ):
										snake_case       :     List[str] =    (0, 0)
										snake_case       :     Tuple =    None
										snake_case       :     Union[str, Any] =    0
										snake_case       :     Dict =    0
										snake_case       :     List[str] =    0




				def __eq__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.position == cell.position




				def 	__snake_case							(							self	:				Optional[int]      ):
										print(self.position      )




class 						a   :




				def __init__(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=(5, 5)      ):
										snake_case       :     int =    np.zeros(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[int] =    world_size[0]
										snake_case       :     Optional[Any] =    world_size[1]




				def 	__snake_case							(							self	:				Any      ):
										print(self.w      )




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Union[str, Any] =    [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										snake_case       :     str =    cell.position[0]
										snake_case       :     Optional[Any] =    cell.position[1]
										snake_case       :     Any =    []
										for n in neughbour_cord:
																snake_case       :     List[str] =    current_x + n[0]
																snake_case       :     Tuple =    current_y + n[1]
																if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																						snake_case       :     int =    Cell()
																						snake_case       :     int =    (x, y)
																						snake_case       :     Any =    cell
																						neighbours.append(SCREAMING_SNAKE_CASE_      )
										return neighbours




def      A (       A_							:					Tuple    ,							A_							:					Tuple    ,							A_							:					Union[str, Any]       ):
						snake_case       :     Union[str, Any] =    []
						snake_case       :     Union[str, Any] =    []
						_open.append(A_       )

						while _open:
												snake_case       :     List[Any] =    np.argmin([n.f for n in _open]       )
												snake_case       :     List[Any] =    _open[min_f]
												_closed.append(_open.pop(A_       )       )
												if current == goal:
																		break
												for n in world.get_neigbours(A_       ):
																		for c in _closed:
																								if c == n:
																														continue
																		snake_case       :     Dict =    current.g + 1
																		snake_case,					snake_case       :     Optional[Any] =    n.position
																		snake_case,					snake_case       :     Union[str, Any] =    goal.position
																		snake_case       :     Tuple =    (ya - ya) ** 2 + (xa - xa) ** 2
																		snake_case       :     Union[str, Any] =    n.h + n.g

																		for c in _open:
																								if c == n and c.f < n.f:
																														continue
																		_open.append(A_       )
						snake_case       :     Dict =    []
						while current.parent is not None:
												path.append(current.position       )
												snake_case       :     Tuple =    current.parent
						path.append(current.position       )
						return path[::-1]


if __name__ == "__main__":
						UpperCAmelCase    					=   Gridworld()
						# Start position and goal
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (0, 0)
						UpperCAmelCase    					=   Cell()
						UpperCAmelCase    					=   (4, 4)
						print(f'''path from {start.position} to {goal.position}''')
						UpperCAmelCase    					=   astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												UpperCAmelCase    					=   1
						print(world.w)

