





'''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_bert import BertTokenizer


a__					:			List[Any]          =     logging.get_logger(__name__)

a__					:			List[str]          =     {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a__					:			Tuple          =     {
    '''vocab_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
        ),
        '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
        ),
        '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-multilingual-cased''': (
            '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-cased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
        ),
    },
}

a__					:			Optional[int]          =     {
    '''bert-base-uncased''': 512,
    '''bert-large-uncased''': 512,
    '''bert-base-cased''': 512,
    '''bert-large-cased''': 512,
    '''bert-base-multilingual-uncased''': 512,
    '''bert-base-multilingual-cased''': 512,
    '''bert-base-chinese''': 512,
    '''bert-base-german-cased''': 512,
    '''bert-large-uncased-whole-word-masking''': 512,
    '''bert-large-cased-whole-word-masking''': 512,
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': 512,
    '''bert-large-cased-whole-word-masking-finetuned-squad''': 512,
    '''bert-base-cased-finetuned-mrpc''': 512,
    '''bert-base-german-dbmdz-cased''': 512,
    '''bert-base-german-dbmdz-uncased''': 512,
    '''TurkuNLP/bert-base-finnish-cased-v1''': 512,
    '''TurkuNLP/bert-base-finnish-uncased-v1''': 512,
    '''wietsedv/bert-base-dutch-cased''': 512,
}

a__					:			Union[str, Any]          =     {
    '''bert-base-uncased''': {'''do_lower_case''': True},
    '''bert-large-uncased''': {'''do_lower_case''': True},
    '''bert-base-cased''': {'''do_lower_case''': False},
    '''bert-large-cased''': {'''do_lower_case''': False},
    '''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
    '''bert-base-multilingual-cased''': {'''do_lower_case''': False},
    '''bert-base-chinese''': {'''do_lower_case''': False},
    '''bert-base-german-cased''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
    '''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
    '''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
    '''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
    '''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  VOCAB_FILES_NAMES
	__lowerCAmelCase       						=  PRETRAINED_VOCAB_FILES_MAP
	__lowerCAmelCase       						=  PRETRAINED_INIT_CONFIGURATION
	__lowerCAmelCase       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__lowerCAmelCase       						=  BertTokenizer





	def __init__(		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_=True      ,       UpperCamelCase_="[UNK]"      ,       UpperCamelCase_="[SEP]"      ,       UpperCamelCase_="[PAD]"      ,       UpperCamelCase_="[CLS]"      ,       UpperCamelCase_="[MASK]"      ,       UpperCamelCase_=True      ,       UpperCamelCase_=None      ,       **UpperCamelCase_      ,       )      ->		List[Any]:
		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_      ,       )

		snake_case__	       =   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
		):
			snake_case__	       =   getattr(UpperCamelCase_      ,       normalizer_state.pop('type'		)		)
			snake_case__	       =   do_lower_case
			snake_case__	       =   strip_accents
			snake_case__	       =   tokenize_chinese_chars
			snake_case__	       =   normalizer_class(**UpperCamelCase_		)

		snake_case__	       =   do_lower_case





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Any:
		snake_case__	       =   [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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		List[int]:
		snake_case__	       =   [self.sep_token_id]
		snake_case__	       =   [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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = None		)      ->		Tuple[str]:
		snake_case__	       =   self._tokenizer.model.save(UpperCamelCase_      ,       name=UpperCamelCase_		)
		return tuple(UpperCamelCase_		)










'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

			if current_node.pos == self.target.pos:
				snake_case__	       =   True
				return self.retrace_path(UpperCamelCase_		)

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			for child_node in successors:
				if child_node in self.closed_nodes:
					continue

				if child_node not in self.open_nodes:
					self.open_nodes.append(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

					if child_node.g_cost < better_node.g_cost:
						self.open_nodes.append(UpperCamelCase_		)
					else:
						self.open_nodes.append(UpperCamelCase_		)

		if not self.reached:
			return [self.start.pos]
		return None





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

			if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCamelCase_		) - 1):
				continue

			if grid[pos_y][pos_x] != 0:
				continue

			successors.append(
			    Node(
			        UpperCamelCase_      ,       UpperCamelCase_      ,       self.target.pos_y      ,       self.target.pos_x      ,       parent.g_cost + 1      ,       UpperCamelCase_      ,       )		)
		return successors





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


if __name__ == "__main__":
			a__					:			List[str]          =     (0, 0)
			a__					:			Dict          =     (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



