





"""simple docstring"""




import numpy as np


class        __UpperCamelCase				:






							def __init__(   self      )	-> int:
								a      :		Tuple	=				(0, 0)
								a      :		Any	=				None
								a      :		int	=				0
								a      :		Optional[int]	=				0
								a      :		List[str]	=				0






							def __eq__(   self      ,					lowerCAmelCase__      )	-> Optional[Any]:
								return self.position == cell.position






							def     __a   (   self      )	-> List[Any]:
								print(self.position      )


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__=(5, 5)      )	-> Any:
								a      :		Union[str, Any]	=				np.zeros(lowerCAmelCase__      )
								a      :		List[str]	=				world_size[0]
								a      :		Union[str, Any]	=				world_size[1]






							def     __a   (   self      )	-> Optional[Any]:
								print(self.w      )






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	a      :		Optional[Any]	=				[]
	a      :		Union[str, Any]	=				[]
	_open.append(_lowercase					)

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

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


if __name__ == "__main__":
		a						: int     	=			Gridworld()
		# Start position and goal
		a						: List[str]     	=			Cell()
		a						: str     	=			(0, 0)
		a						: str     	=			Cell()
		a						: Optional[int]     	=			(4, 4)
		print(F'''path from {start.position} to {goal.position}''')
		a						: int     	=			astar(world, start, goal)
		#   Just for visual reasons.
		for i in s:
				a						: Optional[Any]     	=			1
		print(world.w)





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase_       		=  "true"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






    A__          =   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
    A__          =   load_dataset('glue'    ,			'mrpc'    ,			split='validation'      )

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   tokenized_datasets.rename_column('label'    ,			'labels'      )

    def collate_fn(SCREAMING_SNAKE_CASE__			:	Dict      ):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='longest'    ,			return_tensors='pt'      )
        return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='max_length'    ,			max_length=128    ,			return_tensors='pt'      )

    return DataLoader(SCREAMING_SNAKE_CASE__    ,			shuffle=SCREAMING_SNAKE_CASE__    ,			collate_fn=SCREAMING_SNAKE_CASE__    ,			batch_size=16      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






    A__          =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__    ,			split_batches=SCREAMING_SNAKE_CASE__      )
    A__          =   get_dataloader(SCREAMING_SNAKE_CASE__    ,			not dispatch_batches      )
    A__          =   AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,			return_dict=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      List[str]:





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   generate_predictions(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    assert (
        len(SCREAMING_SNAKE_CASE__      ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__      )}'
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






    A__          =   evaluate.load('glue'    ,			'mrpc'      )
    A__      ,						A__          =   get_mrpc_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    # First do baseline
    A__      ,						A__      ,						A__          =   setup['no']
    model.to(SCREAMING_SNAKE_CASE__      )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__      )
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=batch['labels']      )
    A__          =   metric.compute()

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]    ,			distributed[key]      ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('**Testing gather_for_metrics**'      )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'      )
                test_mrpc(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test torch metrics**'      )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'      )
            test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			99      )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()

