



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_			=     {
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

snake_case_			=     {"""mobilebert-uncased""": 512}


snake_case_			=     {}


class 	a__     (				_lowercase	):
	__magic_name__		:    List[str]	    =     VOCAB_FILES_NAMES
	__magic_name__		:    Optional[Any]	    =     PRETRAINED_VOCAB_FILES_MAP
	__magic_name__		:    Optional[int]	    =     PRETRAINED_INIT_CONFIGURATION
	__magic_name__		:    int	    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__		:    List[str]	    =     MobileBertTokenizer


	def __init__(self : Dict,					__UpperCAmelCase : Dict=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Any=True,					__UpperCAmelCase : str="[UNK]",					__UpperCAmelCase : int="[SEP]",					__UpperCAmelCase : Optional[Any]="[PAD]",					__UpperCAmelCase : List[Any]="[CLS]",					__UpperCAmelCase : Optional[Any]="[MASK]",					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : Tuple=None,					**__UpperCAmelCase : List[str],					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(
					    __UpperCAmelCase,					tokenizer_file=__UpperCAmelCase,					do_lower_case=__UpperCAmelCase,					unk_token=__UpperCAmelCase,					sep_token=__UpperCAmelCase,					pad_token=__UpperCAmelCase,					cls_token=__UpperCAmelCase,					mask_token=__UpperCAmelCase,					tokenize_chinese_chars=__UpperCAmelCase,					strip_accents=__UpperCAmelCase,					**__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
					if (
					    normalizer_state.get('''lowercase''',					__UpperCAmelCase      ) != do_lower_case
					    or normalizer_state.get('''strip_accents''',					__UpperCAmelCase      ) != strip_accents
					    or normalizer_state.get('''handle_chinese_chars''',					__UpperCAmelCase      ) != tokenize_chinese_chars
					):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= getattr(__UpperCAmelCase,					normalizer_state.pop('''type'''      )      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= do_lower_case
									SCREAMING_SNAKE_CASE  :						int      					= strip_accents
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tokenize_chinese_chars
									SCREAMING_SNAKE_CASE  :						List[Any]      					= normalizer_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= do_lower_case


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any=None      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

					return output


	def 				lowercase__  (self : int,					__UpperCAmelCase : List[int],					__UpperCAmelCase : Optional[List[int]] = None      )     ->			List[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [self.sep_token_id]
					SCREAMING_SNAKE_CASE  :						Any      					= [self.cls_token_id]
					if token_ids_a is None:
									return len(cls + token_ids_a + sep      ) * [0]
					return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[str] = None      )     ->			Tuple[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self._tokenizer.model.save(__UpperCAmelCase,					name=__UpperCAmelCase      )
					return tuple(__UpperCAmelCase      )









'''simple docstring'''

from __future__ import annotations

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

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

				SCREAMING_SNAKE_CASE  :						List[str]      					= False  # flag that is set when search is complete
				SCREAMING_SNAKE_CASE  :						Any      					= False  # flag set if we can't find expand

				while not found and not resign:
								if len(_SCREAMING_SNAKE_CASE       ) == 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()
												SCREAMING_SNAKE_CASE  :						List[str]      					= cell.pop()
												SCREAMING_SNAKE_CASE  :						List[str]      					= next_cell[2]
												SCREAMING_SNAKE_CASE  :						Dict      					= next_cell[3]
												SCREAMING_SNAKE_CASE  :						str      					= next_cell[1]

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

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								path.append(invpath[len(_SCREAMING_SNAKE_CASE       ) - 1 - i]       )
				return path, action


if __name__ == "__main__":
	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],
	]

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

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

	snake_case_			,				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])




