"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						'true'


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=82     ,__UpperCAmelCase=16			):






	"""simple docstring"""





	set_seed(42			)
	_lowercase :    Optional[int]							=							RegressionModel()
	_lowercase :    List[str]							=							deepcopy(__SCREAMING_SNAKE_CASE			)
	_lowercase :    Optional[int]							=							RegressionDataset(length=__SCREAMING_SNAKE_CASE			)
	_lowercase :    str							=							DataLoader(__SCREAMING_SNAKE_CASE     ,batch_size=__SCREAMING_SNAKE_CASE			)
	model.to(accelerator.device			)
	_lowercase :    Dict							=							accelerator.prepare(__SCREAMING_SNAKE_CASE     ,__SCREAMING_SNAKE_CASE			)
	return model, ddp_model, dataloader


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=False			):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'			)
	_lowercase :    Tuple							=							load_dataset('glue'     ,'mrpc'     ,split='validation'			)

	def tokenize_function(__UpperCAmelCase			):
		_lowercase :    Optional[int]							=							tokenizer(examples['sentence1']     ,examples['sentence2']     ,truncation=__SCREAMING_SNAKE_CASE     ,max_length=__SCREAMING_SNAKE_CASE			)
		return outputs

	with accelerator.main_process_first():
		_lowercase :    Tuple							=							dataset.map(
		    __SCREAMING_SNAKE_CASE     ,batched=__SCREAMING_SNAKE_CASE     ,remove_columns=['idx', 'sentence1', 'sentence2']     ,)

	_lowercase :    Dict							=							tokenized_datasets.rename_column('label'     ,'labels'			)

	def collate_fn(__UpperCAmelCase			):
		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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							[]
	for batch in dataloader:
		_lowercase :    Optional[Any]							=							batch.values()
		with torch.no_grad():
			_lowercase :    Union[str, Any]							=							model(__SCREAMING_SNAKE_CASE			)
			_lowercase :    List[Any]							=							accelerator.gather_for_metrics((logit, target)			)
			logits_and_targets.append((logit, target)			)
	_lowercase :    List[Any]							=							[], []
	for logit, targ in logits_and_targets:
		logits.append(__SCREAMING_SNAKE_CASE			)
		targs.append(__SCREAMING_SNAKE_CASE			)
	_lowercase :    int							=							torch.cat(__SCREAMING_SNAKE_CASE			), torch.cat(__SCREAMING_SNAKE_CASE			)
	return logits, targs


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase=82     ,__UpperCAmelCase=False     ,__UpperCAmelCase=False     ,__UpperCAmelCase=16			):






	"""simple docstring"""





	_lowercase :    List[str]							=							get_basic_setup(__SCREAMING_SNAKE_CASE     ,__SCREAMING_SNAKE_CASE     ,__SCREAMING_SNAKE_CASE			)
	_lowercase :    Union[str, Any]							=							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      __lowerCAmelCase(		__UpperCAmelCase = False     ,__UpperCAmelCase = False			):






	"""simple docstring"""





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

	# Then do distributed
	_lowercase :    Dict							=							setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			_lowercase :    Any							=							model(**__SCREAMING_SNAKE_CASE			)
		_lowercase :    Optional[int]							=							outputs.logits.argmax(dim=-1			)
		_lowercase :    Union[str, Any]							=							batch["labels"]
		_lowercase :    Any							=							accelerator.gather_for_metrics((preds, references)			)
		metric.add_batch(predictions=__SCREAMING_SNAKE_CASE     ,references=__SCREAMING_SNAKE_CASE			)
	_lowercase :    List[Any]							=							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      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							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]:
			_lowercase :    int							=							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**'			)
	_lowercase :    Any							=							Accelerator()
	test_torch_metrics(__SCREAMING_SNAKE_CASE     ,512			)
	accelerator.state._reset_state()


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	main()


if __name__ == "__main__":
			main()




"""simple docstring"""







from __future__ import annotations

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





	_lowercase :    List[str]							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the reference grid
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Any							=							[
	    [0 for col in range(len(grid[0]			)			)] for row in range(len(__UpperCAmelCase			)			)
	]  # the action grid

	_lowercase :    List[Any]							=							init[0]
	_lowercase :    Optional[Any]							=							init[1]
	_lowercase :    Union[str, Any]							=							0
	_lowercase :    Union[str, Any]							=							g + heuristic[x][y]  # cost from starting cell to destination cell
	_lowercase :    Dict							=							[[f, g, x, y]]

	_lowercase :    Tuple							=							False  # flag that is set when search is complete
	_lowercase :    Tuple							=							False  # flag set if we can't find expand

	while not found and not resign:
		if len(__UpperCAmelCase			) == 0:
			raise ValueError('Algorithm is unable to find solution'			)
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			_lowercase :    List[Any]							=							cell.pop()
			_lowercase :    Optional[Any]							=							next_cell[2]
			_lowercase :    List[str]							=							next_cell[3]
			_lowercase :    Optional[int]							=							next_cell[1]

			if x == goal[0] and y == goal[1]:
				_lowercase :    Optional[Any]							=							True
			else:
				for i in range(len(__UpperCAmelCase			)			):  # to try out different valid actions
					_lowercase :    Any							=							x + DIRECTIONS[i][0]
					_lowercase :    Union[str, Any]							=							y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(__UpperCAmelCase			) and ya >= 0 and ya < len(grid[0]			):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							_lowercase :    int							=							g + cost
							_lowercase :    List[str]							=							ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]			)
							_lowercase :    List[Any]							=							1
							_lowercase :    Dict							=							i
	_lowercase :    Union[str, Any]							=							[]
	_lowercase :    Optional[int]							=							goal[0]
	_lowercase :    Any							=							goal[1]
	invpath.append([x, y]			)  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		_lowercase :    str							=							x - DIRECTIONS[action[x][y]][0]
		_lowercase :    Any							=							y - DIRECTIONS[action[x][y]][1]
		_lowercase :    Dict							=							xa
		_lowercase :    Tuple							=							ya
		invpath.append([x, y]			)

	_lowercase :    List[str]							=							[]
	for i in range(len(__UpperCAmelCase			)			):
		path.append(invpath[len(__UpperCAmelCase			) - 1 - i]			)
	return path, action


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						[
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
						    [0, 1, 0, 0, 0, 0],
						    [0, 1, 0, 0, 1, 0],
						    [0, 0, 0, 0, 1, 0],
						]

						SCREAMING_SNAKE_CASE									=						[0, 0]
						# all coordinates are given in format [y,x]
						SCREAMING_SNAKE_CASE									=						[len(grid) - 1, len(grid[0]) - 1]
						SCREAMING_SNAKE_CASE									=						1

						# the cost map which pushes the path closer to the goal
						SCREAMING_SNAKE_CASE									=						[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
						for i in range(len(grid)):
												for j in range(len(grid[0])):
																		SCREAMING_SNAKE_CASE									=						abs(i - goal[0]) + abs(j - goal[1])
																		if grid[i][j] == 1:
																								# added extra penalty in the heuristic map
																								SCREAMING_SNAKE_CASE									=						99

						SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE									=						search(grid, init, goal, cost, heuristic)

						print('ACTION MAP')
						for i in range(len(action)):
												print(action[i])

						for i in range(len(path)):
												print(path[i])



