from __future__ import annotations

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Any		, ):
						UpperCamelCase_		:							Any  =		[
						    [0 for col in range(len(grid[0]			)			)] for row in range(len(a__			)			)
						]  # the reference grid
						UpperCamelCase_		:							Optional[Any]  =		1
						UpperCamelCase_		:							Any  =		[
						    [0 for col in range(len(grid[0]			)			)] for row in range(len(a__			)			)
						]  # the action grid

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

						UpperCamelCase_		:							Optional[int]  =		False  # flag that is set when search is complete
						UpperCamelCase_		:							Dict  =		False  # flag set if we can't find expand

						while not found and not resign:
												if len(a__			) == 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()
																		UpperCamelCase_		:							List[str]  =		cell.pop()
																		UpperCamelCase_		:							Dict  =		next_cell[2]
																		UpperCamelCase_		:							Tuple  =		next_cell[3]
																		UpperCamelCase_		:							int  =		next_cell[1]

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

						UpperCamelCase_		:							Optional[Any]  =		[]
						for i in range(len(a__			)			):
												path.append(invpath[len(a__			) - 1 - i]			)
						return path, action


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	[
	    [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		:     int         =	[0, 0]
	# all coordinates are given in format [y,x]
	SCREAMING_SNAKE_CASE		:     List[str]         =	[len(grid) - 1, len(grid[0]) - 1]
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	1

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

	SCREAMING_SNAKE_CASE		:     int         =	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])









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


UpperCAmelCase					:			Tuple       =	HfArgumentParser(PretokenizationArguments)
UpperCAmelCase					:			Optional[Any]       =	parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase					:			List[Any]       =	multiprocessing.cpu_count()
UpperCAmelCase					:			str       =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase					:			Optional[int]       =	time.time()
UpperCAmelCase					:			Any       =	load_dataset(args.dataset_name, split='train')
print(f"""Dataset loaded in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(f"""Dataset tokenized in {time.time()-t_start:.2f}s""")

UpperCAmelCase					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")






