
from __future__ import annotations

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






def    lowerCamelCase__				(				__lowerCAmelCase  :  list[list[int]]    ,							__lowerCAmelCase  :  list[int]    ,							__lowerCAmelCase  :  list[int]    ,							__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  list[list[int]]    ,							):




			"""simple docstring"""


			lowerCAmelCase_						=	[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(__lowerCAmelCase				)				)
			]  # the reference grid
			lowerCAmelCase_						=	1
			lowerCAmelCase_						=	[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(__lowerCAmelCase				)				)
			]  # the action grid

			lowerCAmelCase_						=	init[0]
			lowerCAmelCase_						=	init[1]
			lowerCAmelCase_						=	0
			lowerCAmelCase_						=	g + heuristic[x][y]  # cost from starting cell to destination cell
			lowerCAmelCase_						=	[[f, g, x, y]]

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

			while not found and not resign:
						if len(__lowerCAmelCase				) == 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()
									lowerCAmelCase_						=	cell.pop()
									lowerCAmelCase_						=	next_cell[2]
									lowerCAmelCase_						=	next_cell[3]
									lowerCAmelCase_						=	next_cell[1]

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

			lowerCAmelCase_						=	[]
			for i in range(len(__lowerCAmelCase				)				):
						path.append(invpath[len(__lowerCAmelCase				) - 1 - i]				)
			return path, action


if __name__ == "__main__":
			_A   =     [
			    [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],
			]

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

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

			_A,      _A   =     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])





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							lowerCAmelCase_						=	length
							lowerCAmelCase_						=	rng.normal(size=(length,)     ).astype(np.floataa     )
							lowerCAmelCase_						=	a * self.x + b + rng.normal(scale=0.1				,				size=(length,)     ).astype(np.floataa     )




				def __len__(					self     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      Any:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							if self.first_batch:
										print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
										lowerCAmelCase_						=	False
							return x * self.a + self.b






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 16				):




			"""simple docstring"""


			from datasets import load_dataset
			from transformers import AutoTokenizer

			lowerCAmelCase_						=	AutoTokenizer.from_pretrained("bert-base-cased"				)
			lowerCAmelCase_						=	{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
			lowerCAmelCase_						=	load_dataset("csv"    ,							data_files=__lowerCAmelCase				)
			lowerCAmelCase_						=	datasets["train"].unique("label"				)

			lowerCAmelCase_						=	{v: i for i, v in enumerate(__lowerCAmelCase				)}

			def tokenize_function(__lowerCAmelCase  :  List[Any]				):
						# max_length=None => use the model max length (it's actually the default)
						lowerCAmelCase_						=	tokenizer(
						    examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=__lowerCAmelCase    ,							max_length=__lowerCAmelCase    ,							padding="max_length"				)
						if "label" in examples:
									lowerCAmelCase_						=	[label_to_id[l] for l in examples["label"]]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			lowerCAmelCase_						=	datasets.map(
			    __lowerCAmelCase    ,							batched=__lowerCAmelCase    ,							remove_columns=["sentence1", "sentence2", "label"]    ,							)

			def collate_fn(__lowerCAmelCase  :  Optional[int]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(__lowerCAmelCase    ,							padding="max_length"    ,							max_length=128    ,							return_tensors="pt"				)
						return tokenizer.pad(__lowerCAmelCase    ,							padding="longest"    ,							return_tensors="pt"				)

			# Instantiate dataloaders.
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["train"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=2				)
			lowerCAmelCase_						=	DataLoader(tokenized_datasets["validation"]    ,							shuffle=__lowerCAmelCase    ,							collate_fn=__lowerCAmelCase    ,							batch_size=1				)

			return train_dataloader, eval_dataloader



