
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
			import torch





class 			_lowerCAmelCase      (			__UpperCAmelCase ):
				_lowercase    	='''char'''
				_lowercase    	='''bpe'''
				_lowercase    	='''wp'''


_A     =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 			_lowerCAmelCase      (			__UpperCAmelCase ):
				_lowercase    	=['''image_processor''', '''char_tokenizer''']
				_lowercase    	='''ViTImageProcessor'''
				_lowercase    	='''MgpstrTokenizer'''




				def __init__(      self       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."       ,       __SCREAMING_SNAKE_CASE       ,       )
										lowerCAmelCase_      			=			kwargs.pop("feature_extractor"  )

							lowerCAmelCase_      			=			image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."  )
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."  )

							lowerCAmelCase_      			=			tokenizer
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("gpt2"  )
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("bert-base-uncased"  )

							super().__init__(__SCREAMING_SNAKE_CASE       ,       __SCREAMING_SNAKE_CASE  )




				def __call__(      self       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   Union[str, Any]:

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."  )

							if images is not None:
										lowerCAmelCase_      			=			self.image_processor(__SCREAMING_SNAKE_CASE       ,       return_tensors=__SCREAMING_SNAKE_CASE       ,       **__SCREAMING_SNAKE_CASE  )
							if text is not None:
										lowerCAmelCase_      			=			self.char_tokenizer(__SCREAMING_SNAKE_CASE       ,       return_tensors=__SCREAMING_SNAKE_CASE       ,       **__SCREAMING_SNAKE_CASE  )

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										lowerCAmelCase_      			=			encodings["input_ids"]
										return inputs




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			sequences
							lowerCAmelCase_      			=			char_preds.size(0  )

							lowerCAmelCase_			,						lowerCAmelCase_      			=			self._decode_helper(__SCREAMING_SNAKE_CASE       ,       "char"  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			self._decode_helper(__SCREAMING_SNAKE_CASE       ,       "bpe"  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			self._decode_helper(__SCREAMING_SNAKE_CASE       ,       "wp"  )

							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			[]
							for i in range(__SCREAMING_SNAKE_CASE  ):
										lowerCAmelCase_      			=			[char_scores[i], bpe_scores[i], wp_scores[i]]
										lowerCAmelCase_      			=			[char_strs[i], bpe_strs[i], wp_strs[i]]
										lowerCAmelCase_      			=			scores.index(max(__SCREAMING_SNAKE_CASE  )  )
										final_strs.append(strs[max_score_index]  )
										final_scores.append(scores[max_score_index]  )

							lowerCAmelCase_      			=			{}
							lowerCAmelCase_      			=			final_strs
							lowerCAmelCase_      			=			final_scores
							lowerCAmelCase_      			=			char_strs
							lowerCAmelCase_      			=			bpe_strs
							lowerCAmelCase_      			=			wp_strs
							return out




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[str]:

							if format == DecodeType.CHARACTER:
										lowerCAmelCase_      			=			self.char_decode
										lowerCAmelCase_      			=			1
										lowerCAmelCase_      			=			"[s]"
							elif format == DecodeType.BPE:
										lowerCAmelCase_      			=			self.bpe_decode
										lowerCAmelCase_      			=			2
										lowerCAmelCase_      			=			"#"
							elif format == DecodeType.WORDPIECE:
										lowerCAmelCase_      			=			self.wp_decode
										lowerCAmelCase_      			=			102
										lowerCAmelCase_      			=			"[SEP]"
							else:
										raise ValueError(f"""Format {format} is not supported."""  )

							lowerCAmelCase_			,						lowerCAmelCase_      			=			[], []
							lowerCAmelCase_      			=			pred_logits.size(0  )
							lowerCAmelCase_      			=			pred_logits.size(1  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			pred_logits.topk(1       ,       dim=-1       ,       largest=__SCREAMING_SNAKE_CASE       ,       sorted=__SCREAMING_SNAKE_CASE  )
							lowerCAmelCase_      			=			preds_index.view(-1       ,       __SCREAMING_SNAKE_CASE  )[:, 1:]
							lowerCAmelCase_      			=			decoder(__SCREAMING_SNAKE_CASE  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.nn.functional.softmax(__SCREAMING_SNAKE_CASE       ,       dim=2  ).max(dim=2  )
							lowerCAmelCase_      			=			preds_max_prob[:, 1:]

							for index in range(__SCREAMING_SNAKE_CASE  ):
										lowerCAmelCase_      			=			preds_str[index].find(__SCREAMING_SNAKE_CASE  )
										lowerCAmelCase_      			=			preds_str[index][:pred_eos]
										lowerCAmelCase_      			=			preds_index[index].cpu().tolist()
										lowerCAmelCase_      			=			pred_index.index(__SCREAMING_SNAKE_CASE  ) if eos_token in pred_index else -1
										lowerCAmelCase_      			=			preds_max_prob[index][: pred_eos_index + 1]
										lowerCAmelCase_      			=			pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
										dec_strs.append(__SCREAMING_SNAKE_CASE  )
										conf_scores.append(__SCREAMING_SNAKE_CASE  )

							return dec_strs, conf_scores




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[seq.replace(" "       ,       ""  ) for seq in self.char_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  )]
							return decode_strs




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							return self.bpe_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							lowerCAmelCase_      			=			[seq.replace(" "       ,       ""  ) for seq in self.wp_tokenizer.batch_decode(__SCREAMING_SNAKE_CASE  )]
							return decode_strs





from __future__ import annotations

_A     =  list[tuple[int, int]]

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





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   Tuple:
							lowerCAmelCase_      			=			pos_x
							lowerCAmelCase_      			=			pos_y
							lowerCAmelCase_      			=			(pos_y, pos_x)
							lowerCAmelCase_      			=			goal_x
							lowerCAmelCase_      			=			goal_y
							lowerCAmelCase_      			=			g_cost
							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			self.calculate_heuristic()




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




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





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			Node(start[1]       ,       start[0]       ,       goal[1]       ,       goal[0]       ,       0       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			Node(goal[1]       ,       goal[0]       ,       goal[1]       ,       goal[0]       ,       99_999       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			[self.start]
							lowerCAmelCase_      			=			[]

							lowerCAmelCase_      			=			False




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

										if current_node.pos == self.target.pos:
													lowerCAmelCase_      			=			True
													return self.retrace_path(_UpperCamelCase  )

										self.closed_nodes.append(_UpperCamelCase  )
										lowerCAmelCase_      			=			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
																lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase  )       ->   list[Node]:
							lowerCAmelCase_      			=			[]
							for action in delta:
										lowerCAmelCase_      			=			parent.pos_x + action[1]
										lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase  )       ->   Path:
							lowerCAmelCase_      			=			node
							lowerCAmelCase_      			=			[]
							while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)  )
										lowerCAmelCase_      			=			current_node.parent
							path.reverse()
							return path


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

			print("------")

			_A     =  GreedyBestFirst(init, goal)
			_A     =  greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									_A     =  2

						for elem in grid:
									print(elem)



